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Entwicklung der Versorgungssituation und des Energiebedarfes mineralischer Rohstoffe im Kontext der globalen Energiewende
Get PDFDie Bekämpfung des Klimawandel erfordert eine grundlegende Veränderung des weltweiten Energiesystems, um den Ausstoß von klimaschädlichen Treibhausgasen zu reduzieren. Die Nutzung erneuerbarer Energien ermöglicht es, auf fossile Brennstoffe zu verzichten. Allerdings ist dafür im Vergleich zu fossilen Technologien ein erhöhter Einsatz mineralischer Rohstoffe erforderlich. Die globale Energiewende ist damit auch als ein Wandel des Energiesystems hin zu einem materialintensiveren System zu begreifen. Zunehmende Bemühungen zum Ausbau von erneuerbaren Energien und von Technologien zu deren Nutzung können daher zukünftig die Nachfrage nach mineralischen Rohstoffen stark ansteigen lassen. Vor diesem Hintergrund beschäftigt sich diese Arbeit mit der Einschätzung der potenziellen Bedarfssteigerungen und untersucht, welche möglichen zukünftigen Auswirkungen mit Fokus auf den Aspekten der Versorgungssituation und des Energiebedarfes sich dadurch ergeben können. Zusätzlich wird untersucht, ob die Energiewende von ihren eigenen Auswirkungen rückwirkend beeinflusst oder behindert wird und ob sich limitierende Faktoren identifizieren lassen, die die Transformation und insbesondere ihre Geschwindigkeit beeinträchtigen. In dieser Arbeit wird die Thematik beispielhaft anhand der mineralischen Rohstoffe Kupfer und Lithium zunächst separat untersucht, bevor die Ergebnisse anschließend in einen gemeinsamen Kontext gebracht werden. Die Arbeit bildet insgesamt eine breite und aktuelle Wissenssammlung über die Rolle von Rohstoffen in der Energiewende und verwendet insbesondere das Mittel der Metastudie, um fundierte Prognosen über zukünftige Entwicklungen bis zum Jahre 2050 abzuleiten. Die wichtigsten Erkenntnisse dieser Masterarbeit lassen sich in Form der folgenden Kernaussagen zusammenfassen:
• Die Geschwindigkeit der globalen Energiewende kann durch Knappheit und durch hohe Preise von Kupfer und Lithium negativ beeinflusst werden. Dies kann als Rückwirkung des globalen Marktes angesehen werden, der durch eine schnelle Transformation angespannt wird.
• Die Bedarfe nach Kupfer und Lithium steigen im Zuge der Energiewende voraussichtlich stark an, weswegen eine Unterdeckung der Nachfrage eintreten kann, da die Angebotskapazitäten gegebenenfalls an die Grenzen des realisierbaren Wachstums gelangen. Für beide betrachtete Rohstoffe stellt die Elektromobilität einen der größten Bedarfstreiber dar.
• Der entscheidendste limitierende Faktor für das Angebotswachstum beider betrachteter Rohstoffe ist die Geschwindigkeit des Ausbaus der primären Extraktionskapazitäten, da die Primärproduktion auch zukünftig weiterhin die wichtigste Versorgungsroute darstellen wird.
• Die Verfügbarkeit von Lithium stellt aufgrund fehlender absehbarer Substitutionsmöglichkeiten einen limitierenden Faktor für den Ausbau der Elektromobilität dar und könnte damit auch dämpfend auf die Energietransformation einwirken. Als Ergänzung sollten daher lithiumfreie Batterie- und Speichertechnologien verstärkt in Betracht gezogen werden.
• Erschöpfungserscheinungen der Erzvorkommen ergaben sich für Kupfer als die relevanteste Rückwirkung einer intensiven Förderung. Die schon lange Zeit praktizierte industrielle Gewinnung von Kupfer führt durch eine sinkende Erzqualität zu einem überproportional ansteigenden Aufwand bei der primären Förderung aus Minen. Dieser steigende Aufwand wirkt dämpfend auf das Angebotswachstum und erhöht den Energiebedarf der Kupferbereitstellung. Dies wiederum verschlechtert die Energiebilanz kupferhaltiger Technologien zunehmend.
• Das Innovationspotenzial zur Angebotssteigerung und Senkung des Energiebedarfes für Kupfer ist weitestgehend ausgeschöpft. Für den erst seit vergleichsweise kurzer Zeit in größerem Ausmaß genutzten Rohstoff Lithium besteht hingegen noch viel Innovationspotenzial in allen Bereichen
How speaking versus writing to conversational agents shapes consumers’ choice and choice satisfaction
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The use of conversational agents (e.g., chatbots) to simplify or aid consumers’ purchase decisions is on the rise. In designing those conversational agents, a key question for companies is whether and when it is advisable to enable voice-based rather than text-based interactions. Addressing this question, this study finds that matching consumers’ communication modality with product type (speaking about hedonic products; writing about utilitarian products) shapes consumers’ choice and increases choice satisfaction. Specifically, speaking fosters a feeling-based verbalizing focus, while writing triggers a reason-based focus. When this focus matches consumers’ mindset in evaluating the product type, preference fluency increases, thereby enhancing choice satisfaction. Accordingly, the authors provide insights into managing interactions with conversational agents more effectively to aid decision-making processes and increase choice satisfaction. Finally, they show that communication modality can serve as a strategic tool for low-equity brands to better compete with high-equity brands
Parametric Optimization on HPC Clusters with Geneva
Get PDFMany challenges of today’s science are parametric optimization problems that are extremely complex and computationally intensive to calculate. At the same time, the hardware for high-performance computing is becoming increasingly powerful. Geneva is a framework for parallel optimization of large-scale problems with highly nonlinear quality surfaces in grid and cloud environments. To harness the immense computing power of high-performance computing clusters, we have developed a new networking component for Geneva—the so-called MPI Consumer—which makes Geneva suitable for HPC. Geneva is most prominent for its evolutionary algorithm, which requires repeatedly evaluating a user-defined cost function. The MPI Consumer parallelizes the computation of the candidate solutions’ cost functions by sending them to remote cluster nodes. By using an advanced multithreading mechanism on the master node and by using asynchronous requests on the worker nodes, the MPI Consumer is highly scalable. Additionally, it provides fault tolerance, which is usually not the case for MPI programs but becomes increasingly important for HPC. Moreover, the MPI Consumer provides a framework for the intuitive implementation of fine-grained parallelization of the cost function. Since the MPI Consumer conforms to the standard paradigm of HPC programs, it vastly improves Geneva’s user-friendliness on HPC clusters. This article gives insight into Geneva’s general system architecture and the system design of the MPI Consumer as well as the underlying concepts. Geneva—including the novel MPI Consumer—is publicly available as an open source project on GitHub ( https://github.com/gemfony/geneva ) and is currently used for fundamental physics research at GSI in Darmstadt, Germany
Generalisable Presentation Attack Detection For Multiple Types Of Biometric Characteristics
Get PDFBiometric systems have experienced a large development in recent
years since they are accurate, secure, and in many cases, more user
convenient than traditional credential-based access control systems. Inspite of their benefits, biometric systems are still vulnerable to attack presentations (APs), which can be easily launched by a fraudulentsubject without having a wide expert knowledge. This way, he/she can gain access to several applications, such as bank accounts and smartphone unlocking, where biometric systems are frequently deployed. In order to mitigate such threats and increase the security of biometric systems, the development of reliable Presentation Attack
Detection (PAD) algorithms is of utmost importance to the research
community.In the context of PAD, we explore in this Thesis different strategies and methods in order to improve the generalisation capability of PAD schemes. To that end, we propose the definition of a semantic common feature space which successfully discriminates bona fide presentations (BPs)1 from APs. In essence, this process is seeking for those significant features extracted from known PAI species samples that are observed in unknown PAI species. In addition, we explore several handcrafted techniques in order to build a reliable description of features per biometric characteristic studied. The experimental evaluation shows that a common feature space can be computed through the fusion between generative models and discriminative approaches. Remarkable detection performances for high-security thresholds lead to the construction of a convenient (i.e., low BP rejection rates or Bona fide Presentation Classification Error Rate (BPCER)) and secure (i.e., low AP acceptance rates or Attack Presentation Classification Error Rate (APCER)) PAD subsystem
Anxiety in response to the climate and environmental crises: validation of the Hogg Eco-Anxiety Scale in Germany
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As the climate and environmental crises unfold, eco-anxiety, defined as anxiety about the crises’ devastating consequences for life on earth, affects mental health worldwide. Despite its importance, research on eco-anxiety is currently limited by a lack of validated assessment instruments available in different languages. Recently, Hogg and colleagues proposed a multidimensional approach to assess eco-anxiety. Here, we aim to translate the original English Hogg Eco-Anxiety Scale (HEAS) into German and to assess its reliability and validity in a German sample.
Methods
Following the TRAPD (translation, review, adjudication, pre-test, documentation) approach, we translated the original English scale into German. In total, 486 participants completed the German HEAS. We used Bayesian confirmatory factor analysis (CFA) to assess whether the four-factorial model of the original English version could be replicated in the German sample. Furthermore, associations with a variety of emotional reactions towards the climate crisis, general depression, anxiety, and stress were investigated.
Results
The German HEAS was internally consistent (Cronbach’s alphas 0.71–0.86) and the Bayesian CFA showed that model fit was best for the four-factorial model, comparable to the factorial structure of the original English scale (affective symptoms, rumination, behavioral symptoms, anxiety about personal impact). Weak to moderate associations were found with negative emotional reactions towards the climate crisis and with general depression, anxiety, and stress.
Discussion
Our results support the original four-factorial model of the scale and indicate that the German HEAS is a reliable and valid scale to assess eco-anxiety in German speaking populations
Rezension zu: Hoffjann, Olaf: Die Flucht in die Ambiguität. Strategische Kommunikation zwischen Ein- und Mehrdeutigkeiten.
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Online multiple testing with super-uniformity reward
Get PDFValid online inference is an important problem in contemporary multiple testing research,to which various solutions have been proposed recently. It is well-known that these existing methods can suffer from a significant loss of power if the null p-values are conservative. In this work, we extend the previously introduced methodology to obtain more powerful procedures for the case of super-uniformly distributed p-values. These types of p-values arise in important settings, e.g. when discrete hypothesis tests are performed or when the p-values are weighted. To this end, we introduce the method of super-uniformity reward (SUR) that incorporates information about the individual null cumulative distribution functions. Our approach yields several new 'rewarded' procedures that offer uniform power improvements over known procedures and come with mathematical guarantees for controlling online error criteria based either on the family-wise error rate (FWER) or the marginal false discovery rate (mFDR). We illustrate the benefit of super-uniform rewarding in real-data analyses and simulation studies. While discrete tests serve as our leading example, we also show how our method can be applied to weighted p-values
Let’s face it: When and how facial emojis increase the persuasiveness of electronic word of mouth
Get PDFFacial emojis have increasingly permeated electronic word of mouth (eWOM), but the persuasive consequences of this phenomenon remain unclear. Drawing on emotions as social information (EASI) theory, this research reveals that facial emojis influence persuasion (e.g., product choice) by affecting emotional arousal and perceived ambiguity. While the effect through emotional arousal is generally positive, the effect through ambiguity depends on the emojis’ function in eWOM: facial emojis that replace a verbal expression increase ambiguity and therefore reduce persuasion, whereas those that reiterate a verbal expression decrease ambiguity and therefore enhance persuasion. Both the emotional-arousal and ambiguity pathways determine the net persuasive effect. This research also explores two situations (high verbal context richness and eWOM from strong ties) where replacing facial emojis can increase persuasion. Finally, the authors show that facial emojis’ persuasive power is generalizable to online brand communications, influencing key management outcomes such as click-through rates for digital ads
Introduction of a cascaded segmentation pipeline for parametric T1 mapping in cardiovascular magnetic resonance to improve segmentation performance
Get PDFThe manual and often time-consuming segmentation of the myocardium in cardiovascular magnetic resonance is increasingly automated using convolutional neural networks (CNNs). This study proposes a cascaded segmentation (CASEG) approach to improve automatic image segmentation quality. First, an object detection algorithm predicts a bounding box (BB) for the left ventricular myocardium whose 1.5 times enlargement defines the region of interest (ROI). Then, the ROI image section is fed into a U-Net based segmentation. Two CASEG variants were evaluated: one using the ROI cropped image solely (cropU) and the other using a 2-channel-image additionally containing the original BB image section (crinU). Both were compared to a classical U-Net segmentation (refU). All networks share the same hyperparameters and were tested on basal and midventricular slices of native and contrast enhanced (CE) MOLLI T1 maps. Dice Similarity Coefficient improved significantly (p < 0.05) in cropU and crinU compared to refU (81.06%, 81.22%, 72.79% for native and 80.70%, 79.18%, 71.41% for CE data), while no significant improvement (p < 0.05) was achieved in the mean absolute error of the T1 time (11.94 ms, 12.45 ms, 14.22 ms for native and 5.32 ms, 6.07 ms, 5.89 ms for CE data). In conclusion, CASEG provides an improved geometric concordance but needs further improvement in the quantitative outcome
Brand placements in video games: How local in‐game experiences influence brand attitudes
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Brand placements are omnipresent in video games, but their overall effect on brand attitudes is small and varies substantially between studies. The present research takes an evaluative conditioning perspective to explain when and how brand placements in video games influence brand attitudes. In two experiments with a 3D first‐person video game, we show that only brands encountered during positive in‐game experiences benefit from the placement, but not those encountered during negative in‐game experiences. Building on the cognitive processes underlying evaluative conditioning, we also show that brand attitudes largely depend on the memory for the pairing of a brand with positive/negative in‐game experiences. Pairing memory and thus also evaluative conditioning effects increase when players attend to the pairing of brands and positive/negative experiences, for example, when such pairings are a central part of the game's storyline. Overall, our findings show that evaluative conditioning and its cognitive mechanisms can be utilized to explain and predict advertising effects in applied settings, such as brand placements in video games