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This work is motivated by real-world particle shapes, observed using a scanning electron microscopy. The focus of the presented studies was to understand the influence of the particle shapes on the effective elastic properties of the two-phase composites. For this, particles with polyhedral, undulated and other shapes were numerically modeled using analytical functions. Creation of some shapes, like polyhedral, are known from the literature but Laplace’s spherical harmonics, as well as the Goursat’s surface and some others, were used for the first time to create novel particle shapes. Elastic properties of the composites with different particle shapes were calculated using the finite element analysis. The obtained results show good agreement with mean-field homogenization methods such like Mori-Tanaka and Lielens as well as other numerical results available in the literature. Further, the dependence of the effective Young’s moduli of the composite on the shape and the corresponding surface-to-volume ratio of the particles was studied. It was observed that the effective Young’s moduli increase with the surface-to-volume ratio of the particles in the case where particles are stiffer in comparison to the matrix. It was also remarked that, in the case of particles of similar shapes, the particle surface-to-volume ratio and the effective Young’s moduli differ significantly with the surface curvature and the edge sharpness of the particles.
Understanding N2O formation and consumption in ammonia combustion is crucial in realizing the impact of ammonia as an alternative fuel to mitigate the impact of climate change. This study demonstrates the feasibility of using Raman spectroscopy for in-situ N2O measurements in ammonia flames. Raman spectra were acquired along a NH3/H2/N2-air flame in a laminar opposed jet burner, using a pulsed laser combined with a three-disk rotating shutter system to suppress the luminous flame background. This setup enabled the clear detection of the N2O Raman spectrum. Raman libraries of N2, O2, H2, NO, and N2O were fitted to the spectra using a newly developed fitting routine. This yielded qualitative N2O mole fractions along the flame that align closely with numerical simulations based on recently published chemical reaction models for ammonia oxidation, paving the way for future quantitative N2O measurements in ammonia flames. Since no prior libraries for temperature-dependent N2O Raman spectra were available, a methodology for its simulation is introduced. High-resolution N2O spectra were acquired between 295 and 1091 K as validation data for the simulation. Despite minor deviations, the simulation effectively captures the spectral shape and temperature dependence of the Raman cross sections, enabling its use in the spectral fitting routine towards quantitative in-situ concentration measurements.
Polyhedral Bunsen flames, induced by hydrodynamic and thermo-diffusive instabilities, are characterized by periodic trough and cusp cellular structures along the conical flame front. In this study, the effects of flow velocity, hydrogen content, and equivalence ratio on the internal cellular structure of premixed fuel-lean hydrogen/methane/air polyhedral flames are experimentally investigated. A high-spatial-resolution one-dimensional Raman/Rayleigh scattering system is employed to measure the internal scalar structures of polyhedral flames in troughs and cusps. Planar laser-induced fluorescence of hydroxyl radicals and chemiluminescence imaging measurements are used to quantify the flame front morphology. In the experiments, stationary polyhedral flames with varying flow velocities from 1.65 to 2.50 m/s, hydrogen contents from 50 to 83%, and equivalence ratios from 0.53 to 0.64 are selected and measured. The results indicate that the positively curved troughs exhibit significantly higher hydrogen mole fractions and local equivalence ratios compared to the negatively curved cusps, due to the respective focusing/defocusing effect of trough/cusp structure on highly diffusive hydrogen. The hydrogen mole fraction and local equivalence ratio differences between troughs and cusps are first increased and then decreased with increasing measurement height from 5 to 13 mm, due to the three-dimensional effect of the flame front. With increasing flow velocity from 1.65 to 2.50 m/s, the hydrogen mole fraction and local equivalence ratio differences between troughs and cusps decrease, which is attributed to the overall decreasing curvatures in troughs and cusps due to the decreased residence time and increased velocity-induced strain. With increasing hydrogen content from 50 to 83%, the hydrogen mole fraction and local equivalence ratio differences between troughs and cusps are amplified, due to the enhanced effects of the flame front curvature and the differential diffusion of hydrogen. With increasing equivalence ratio from 0.53 to 0.64, a clear increasing trend in hydrogen mole fraction and equivalence ratio differences between troughs and cusps is observed at constant flow velocity condition, which is a trade-off result between increasing effective Lewis number and increasing curvatures in troughs and cusps.
Mobile Contactless Fingerprint Presentation Attack Detection: Generalizability and Explainability
(2024)
Contactless fingerprint recognition is an emerging biometric technology that has several advantages over contact-based schemes, such as improved user acceptance and fewer hygienic concerns. Like for most other biometrics, Presentation Attack Detection (PAD) is crucial to preserving the trustworthiness of contactless fingerprint recognition methods. For many contactless biometric characteristics, Convolutional Neural Networks (CNNs) represent the state-of-the-art of PAD algorithms. For CNNs, the ability to accurately classify samples that are not included in the training is of particular interest, since these generalization capabilities indicate robustness in real-world scenarios. In this work, we focus on the generalizability and explainability aspects of CNN-based contactless fingerprint PAD methods. Based on previously obtained findings, we selected four CNN-based methods for contactless fingerprint PAD: two PAD methods designed for other biometric characteristics, an algorithm for contact-based fingerprint PAD and a general-purpose ResNet18. For our evaluation, we use four databases and partition them using Leave-One-Out (LOO) protocols. Furthermore, the generalization capability to a newly captured database is tested. Moreover, we explore t-SNE plots as a means of explainability to interpret our results in more detail. The low D-EERs obtained from the LOO experiments (below 0.1% D-EER for every LOO group) indicate that the selected algorithms are well-suited for the particular application. However, with an D-EER of 4.14%, the generalization experiment still has room for improvement.
Zeit für Veränderung
(2024)
Der Beitrag nimmt unter Bezugnahme auf die Beiträge des Schwerpunkts „Staatliche Anerkennung“ einen Ausblick zur Perspektive der staatlichen Anerkennung vor und formuliert drei Impulse, die zu einer Neujustierung rund um Fragen der Qualifizierung von Sozialarbeiter_innen und Sozialpädagog_innen beitragen könnten.
Die staatliche Anerkennung von Sozialarbeiter_innen und Sozialpädagog_innen blickt auf eine lange Tradition zurück und ist seit vielen Jahren Gegenstand zahlreicher und kontroverser Auseinandersetzungen. Der Beitrag widmet sich neben einer grundlegenden Einführung zur Thematik, der gegenwärtigen Relevanz sowie der aktuellen Vergabepraxis der staatlichen Anerkennung, identifiziert zentrale
Diskussionsbedarfe und führt in den Schwerpunkt ein.
Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible.
We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification.
Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.
Face Morphing Attacks pose a threat to the security of identity documents, especially with respect to a subsequent access control process, because they allow both involved individuals to use the same document. Several algorithms are currently being developed to detect Morphing Attacks, often requiring large data sets of morphed face images for training. In the present study, face embeddings are used for two different purposes: first, to pre-select images for the subsequent large-scale generation of Morphing Attacks, and second, to detect potential Morphing Attacks. Previous studies have demonstrated the power of embeddings in both use cases. However, we aim to build on these studies by adding the more powerful MagFace model to both use cases, and by performing comprehensive analyses of the role of embeddings in pre-selection and attack detection in terms of the vulnerability of face recognition systems and attack detection algorithms. In particular, we use recent developments to assess the attack potential, but also investigate the influence of morphing algorithms. For the first objective, an algorithm is developed that pairs individuals based on the similarity of their face embeddings. Different state-of-the-art face recognition systems are used to extract embeddings in order to pre-select the face images and different morphing algorithms are used to fuse the face images. The attack potential of the differently generated morphed face images will be quantified to compare the usability of the embeddings for automatically generating a large number of successful Morphing Attacks. For the second objective, we compare the performance of the embeddings of two state-of-the-art face recognition systems with respect to their ability to detect morphed face images. Our results demonstrate that ArcFace and MagFace provide valuable face embeddings for image pre-selection. Various open-source and commercial-off-the-shelf face recognition systems are vulnerable to the generated Morphing Attacks, and their vulnerability increases when image pre-selection is based on embeddings compared to random pairing. In particular, landmark-based closed-source morphing algorithms generate attacks that pose a high risk to any tested face recognition system. Remarkably, more accurate face recognition systems show a higher vulnerability to Morphing Attacks. Among the systems tested, commercial-off-the-shelf systems were the most vulnerable to Morphing Attacks. In addition, MagFace embeddings stand out as a robust alternative for detecting morphed face images compared to the previously used ArcFace embeddings. The results endorse the benefits of face embeddings for more effective image pre-selection for face morphing and for more accurate detection of morphed face images, as demonstrated by extensive analysis of various designed attacks. The MagFace model is a powerful alternative to the often-used ArcFace model in detecting attacks and can increase performance depending on the use case. It also highlights the usability of embeddings to generate large-scale morphed face databases for various purposes, such as training Morphing Attack Detection algorithms as a countermeasure against attacks.
This study investigates the diffusion of AI-based service applications within the business models of German manufacturing industries, surveying 162 decision-makers. The integration of AI into business model is assessed through the Business Model Canvas (BMC) framework, evaluating its value in terms of effectiveness as well as efficiency. Rather than focusing on specific use cases, the study delves into the intended usage of value-driven AI services references to enhance effectiveness and efficiency across various elements of the business models. Through this research, eleven service values have been identified. Each service vale corresponds to a distinct element of the BMC. Decision-makers were surveyed using a Confirmation/Disconfirmation (C/D) paradigm to measure the disparities between their current and target performance levels. Consequently, this study provides valuable insights from the perspective of decision makers regarding the current and desired state of AI integration in the German manufacturing industry, taking into account AI usage or no AI usage at the time of data collection.
In many forensic scenarios, criminals often attempt to conceal their identity by covering their face and other distinctive body parts. In such situations, physical evidence may, however, reveal other unique characteristics, e.g. hands, which can be used to identify offenders. In this context, several state-of-the-art biometric recognition systems have been proposed recently. These recognition systems offer high identification performance in restricted environments. However, in forensic scenarios, the environment is often unconstrained, making biometric identification considerably more difficult, with a consequent decrease in accuracy. In this article, we explore methods (e.g. hand alignment and information fusion) to improve the identification of subjects within forensic investigations. Experimental results show that explored techniques play an important role in the improvement of the identification performance of existing schemes: the combination of hand alignment and information fusion results in the highest Rank-1 identification performance improvement of up to 13.10% (i.e., 26.30% vs. 13.20%) and 16.30% (i.e., 77.00% vs. 60.70%) with respect to the baseline for the unconstrained databases NTU-PI_v1 and HaGRID, respectively ( https://github.com/ljsoler/IF-HA-HandRecognition ).
The development of large-scale identification systems that ensure the privacy protection of enrolled subjects represents a major challenge. Biometric deployments that provide interoperability and usability by including efficient multi-biometric solutions are a recent requirement. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes seem inadequate for indexing (workload reduction) in biometric identification systems. More specifically, they have been used in identification systems that perform exhaustive searches, leading to a degradation of computational efficiency. To overcome these limitations, we present an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates and is agnostic with respect to biometric characteristics and biometric template protection schemes. To this end, a multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics. Experimental results reported on publicly available databases using state-of-the-art Deep Neural Network (DNN)-based embedding extractors show that the protected multi-biometric identification system can reduce the computational workload to approximately 57% (indexing up to three types of biometric characteristics) and 53% (indexing up to two types of biometric characteristics), while simultaneously improving the biometric performance of the baseline biometric system at the high-security thresholds. Code is available at https://github.com/dosorior/FBP-Multi-biometric-Indexing.
Millistructured Coiled Flow Inverter for Biphasic Continuous Flow 5‐Chloromethylfurfural Synthesis
(2024)
Syntheses of alternative platform chemicals, such as 5‐chloromethylfurfural (CMF), from bio‐based starting materials are often associated with complicated kinetic schemes and mass transfer processes. Millistructured flow reactor concepts can help to elucidate kinetic schemes and determine rate constants which are of crucial importance for the design of respective technical processes. For the first time, the influence of proton concentration on the rate constants involved in the biphasic synthesis of CMF is systematically investigated. Results are discussed in terms of green chemistry metrics.
In order to generate a machine learning algorithm (MLA) that can support ophthalmologists with the diagnosis of glaucoma, a carefully selected dataset that is based on clinically confirmed glaucoma patients as well as borderline cases (e.g., patients with suspected glaucoma) is required. The clinical annotation of datasets is usually performed at the expense of the data volume, which results in poorer algorithm performance. This study aimed to evaluate the application of an MLA for the automated classification of physiological optic discs (PODs), glaucomatous optic discs (GODs), and glaucoma-suspected optic discs (GSODs). Annotation of the data to the three groups was based on the diagnosis made in clinical practice by a glaucoma specialist. Color fundus photographs and 14 types of metadata (including visual field testing, retinal nerve fiber layer thickness, and cup–disc ratio) of 1168 eyes from 584 patients (POD = 321, GOD = 336, GSOD = 310) were used for the study. Machine learning (ML) was performed in the first step with the color fundus photographs only and in the second step with the images and metadata. Sensitivity, specificity, and accuracy of the classification of GSOD vs. GOD and POD vs. GOD were evaluated. Classification of GOD vs. GSOD and GOD vs. POD performed in the first step had AUCs of 0.84 and 0.88, respectively. By combining the images and metadata, the AUCs increased to 0.92 and 0.99, respectively. By combining images and metadata, excellent performance of the MLA can be achieved despite having only a small amount of data, thus supporting ophthalmologists with glaucoma diagnosis.
The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas.
The kinetics and mechanism of drug binding to its target are critical to pharmacological efficacy. A high throughput (HTS) screen often results in hundreds of hits, of which usually only simple IC50 values are determined during reconfirmation. However, kinetic parameters such as residence time for reversible inhibitors and the kinact/KI ratio, which is the critical measure for evaluating covalent inactivators, are early predictive measures to assess the chances of success of the hits in the clinic. Using the promising cancer target human histone deacetylase 8 as an example, we present a robust method that calculates concentration-dependent apparent rate constants for the inhibition or inactivation of HDAC8 from dose–response curves recorded after different pre-incubation times. With these data, hit compounds can be classified according to their mechanism of action, and the relevant kinetic parameters can be calculated in a highly parallel fashion. HDAC8 inhibitors with known modes of action were correctly assigned to their mechanism, and the binding mechanisms of some hits from an internal HDAC8 screening campaign were newly determined. The oxonitriles SVE04 and SVE27 were classified as fast reversible HDAC8 inhibitors with moderate time-constant IC50 values of 4.2 and 2.6 µM, respectively. The hit compound TJ-19-24 and SAH03 behave like slow two-step inactivators or reversible inhibitors, with a very low reverse isomerization rate.
Für das Erreichen der Klimaneutralität 2045 in Deutschland hat die Bundesregierung 2022 ein Gesetz vorgelegt, welches Kommunen ab 20.000 Einwohner verpflichtet einen kommunalen Wärmebedarfsplan aufzustellen. Eine Wärmebedarfsplan beinhaltet den aktuellen, sowie den zukünftigen Wärmebedarf der Kommune. Außerdem werden Potenziale für die Erzeugung erneuerbarer Energien mit Wärmepumpen ermittelt (Landes Energie Agentur Hessen, 2024). Aus den Potenzialen lassen sich innerhalb der Kommune stadtteil-/ oder gebäudespezifisch Teilmaßnahmen für eine mögliche Umsetzung ableiten. Diese Maßnahmen bestehen aus Sanierungen, dem Aufbau und der Erweiterung von Wärmeverbundlösungen und spezifische Einzellösungen. Zudem dient die Potenzialermittlung einer zukünftigen effizienten Koordination von Planung, Umsetzung und Förderung. Über die Stadtwerke können die Kommunen zielgerichtet Wärmenetze neu bauen oder ausbauen (Landes Energie Agentur Hessen, 2020).
Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms’ fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.
We address the need for a large-scale database of children’s faces by using generative adversarial networks (GANs) and face-age progression (FAP) models to synthesize a realistic dataset referred to as “HDA-SynChildFaces”. Hence, we proposed a processing pipeline that initially utilizes StyleGAN3 to sample adult subjects, which is subsequently progressed to children of varying ages using InterFaceGAN. Intra-subject variations, such as facial expression and pose, are created by further manipulating the subjects in their latent space. Additionally, this pipeline allows the even distribution of the races of subjects, allowing the generation of a balanced and fair dataset with respect to race distribution. The resulting HDA-SynChildFaces consists of 1,652 subjects and 188,328 images, each subject being present at various ages and with many different intra-subject variations. We then evaluated the performance of various facial recognition systems on the generated database and compared the results of adults and children at different ages. The study reveals that children consistently perform worse than adults on all tested systems and that the degradation in performance is proportional to age. Additionally, our study uncovers some biases in the recognition systems, with Asian and black subjects and females performing worse than white and Latino-Hispanic subjects and males.
District heating plays a key role in the German heat transition (“Wärmewende”) to achieve climate protection targets. In order to realise the heating transition, the legislator has established cost efficiency as a central criterion in the relevant legislation. Ecology, as the third pillar of sustainability, is thus taking a back seat, despite the transformation’s influence on other sustainability dimensions beyond climate protection.
The article takes an ecological perspective on the district heating
transformation and shows that, from this perspective, greater emphasis
should be placed on local environmental heat and large heat pumps.
In the second step, the decentralised information available on the
actual transformation plans of district heating suppliers is aggregated and evaluated at a national level for the first time. The evaluation indicates a possible gap between the developed sustainable target state and the plans of district heating suppliers, which are primarily focussed on the cost efficiency criterion. This comparison identifies a potential conflict of objectives between the legislative cost efficiency criterion and the ecological sustainability perspective.
In this paper, we present a new processing method, called MOSES—Impacts, for the detection of micrometer-sized damage on glass plate surfaces. It extends existing methods by a separation of damaged areas, called impacts, to support state-of-the-art recycling systems in optimizing their parameters. These recycling systems are used to repair process-related damages on glass plate surfaces, caused by accelerated material fragments, which arise during a laser–matter interaction in a vacuum. Due to a high number of impacts, the presented MOSES—Impacts algorithm focuses on the separation of connected impacts in two-dimensional images. This separation is crucial for the extraction of relevant features such as centers of gravity and radii of impacts, which are used as recycling parameters. The results show that the MOSES—Impacts algorithm effectively separates impacts, achieves a mean agreement with human users of (82.0 ± 2.0)%, and improves the recycling of glass plate surfaces by identifying around 7% of glass plate surface area as being not in need of repair compared to existing methods.
Human histone deacetylase 4 (HDAC4) is a key epigenetic regulator involved in a number of important cellular processes. This makes HDAC4 a promising target for the treatment of several cancers and neurodegenerative diseases, in particular Huntington's disease. HDAC4 is highly regulated by phosphorylation and oxidation, which determine its nuclear or cytosolic localization, and exerts its function through multiple interactions with other proteins, forming multiprotein complexes of varying composition. The catalytic domain of HDAC4 is known to interact with the SMRT/NCOR corepressor complex when the structural zinc-binding domain (sZBD) is intact and forms a closed conformation. Crystal structures of the HDAC4 catalytic domain have been reported showing an open conformation of HDAC4 when bound to certain ligands. Here, we investigated the relevance of this HDAC4 conformation under physiological conditions in solution. We show that proper zinc chelation in the sZBD is essential for enzyme function. Loss of the structural zinc ion not only leads to a massive decrease in enzyme activity, but it also has serious consequences for the overall structural integrity and stability of the protein. However, the Zn2+ free HDAC4 structure in solution is incompatible with the open conformation. In solution, the open conformation of HDAC4 was also not observed in the presence of a variety of structurally divergent ligands. This suggests that the open conformation of HDAC4 cannot be induced in solution, and therefore cannot be exploited for the development of HDAC4-specific inhibitors.
The development of compact neutron sources for applications is extensive and features many approaches. For ion-based approaches, several projects with different parameters exist. This article focuses on ion-based neutron production below the spallation barrier for proton and deuteron beams with arbitrary energy distributions with kinetic energies from 3 MeV to 97 MeV. This model makes it possible to compare different ion-based neutron source concepts against each other quickly. This contribution derives a predictive model using Monte Carlo simulations (an order of 50,000 simulations) and deep neural networks. It is the first time a model of this kind has been developed. With this model, lengthy Monte Carlo simulations, which individually take a long time to complete, can be circumvented. A prediction of neutron spectra then takes some milliseconds, which enables fast optimization and comparison. The models’ shortcomings for low-energy neutrons (<0.1 MeV
) and the cut-off prediction uncertainty (±3 MeV
) are addressed, and mitigation strategies are proposed.
A growing body of literature mainly in the context of consumer research indicates that the formal-aesthetic and conceptual design of objects can influence users' thoughts, emotions and even behavioural patterns. While there is strong evidence regarding these effects on actual purchasing decisions, evidence on the effect of aesthetic design features (e.g., haptics, colour) on health-related mental concepts and intentions for health behaviour change is scarce. Based on insights from material and conceptual priming, this article illustrates the research-driven and evidence-based design process of two design primes and comprises pre-tests and an experiment in two settings on the effect of design on health behaviour focusing i.a. on intention for health behaviour change. In an evidence-based and research-driven process, two lecterns were designed to work as primes, i.e., to have a positive vs. negative influence on several mental constructs (sense of control, sense of coherence, resiliency, self-efficacy) and health-related intention. The lecterns differed mainly in terms of aesthetic appearance (e.g., material, colour, proportion, steadiness). They were tested in (a.) a university setting with students (n = 83) and (b.) a clinical setting with orthopaedic rehabilitation patients (n = 38). Participants were asked to perform an unrelated task (evaluation of an unrelated product) while standing at and using the lecterns. Overall, t-tests and Mann-Whitney-U tests show no significant differences but differing tendencies in a mentioning task. When asked to name health-promoting activities, in the clinical setting, participants using the "positive" prime (i.e., the steady lectern, n = 13) mentioned more sport-related aspects on average and a higher portion of sport-related aspects of their answers than participants using the "negative" prime (n = 11). In the university setting (positive: n = 36; negative n = 38), no such differences emerged. This finding gives reason to believe that the prime might be specifically effective in the clinical setting as it relates to physical activity being the most relevant topic of the patients' pathology.
The leather industry is a complex system with multiple actors that faces a fundamental transition toward more sustainable chemistry. To support this process, this article analyzes challenges of the industry and consumers’ roles as a nexus of transition-relevant developments. We present findings of an empirical study (N = 439) among consumers on their perception of leather, related knowledge, and purchasing behavior. We found that participants perceived leather as natural, robust, and of high quality. Knowledge about the manufacturing of leather products was overall limited but varied. Applying a psychological behavior theory, we found that being aware of environmental and health consequences from conventional manufacturing of leather products was positively associated with a personal norm to purchase leather products that are less harmful to environment and health. The perceived ease of buying such products was positively associated with their purchase. Our findings shed light on consumers’ roles in the current leather system and their support of niche innovations toward more sustainable chemistry. Against this backdrop, we discuss implications for product design, consumer information, and needs for traceability along supply chains.
Technostress – d. h. Stress, der aus dem Umgang mit digitalen Technologien resultiert – ist eine gravierende Schattenseite der voranschreitenden Digitalisierung der Arbeitswelt. Die negativen Auswirkungen dieses Phänomens sind bereits heute erkennbar. Sie beinhalten sowohl negative gesundheitliche Folgen für die betroffenen Mitarbeiter_innen als auch gravierende Folgekosten für Unternehmen durch gesteigerte Fehlzeiten sowie negative Auswirkungen auf Mitarbeiterproduktivität und -zufriedenheit. Die vorliegende Studie untersucht, ob das Führungsverhalten einer Führungskraft die Entstehung von Technostress bei den ihr direkt unterstellten Mitarbeiter_innen beeinflusst. Darüber hinaus werden Einflüsse weiterer individueller und organisationaler Faktoren überprüft. Mittels validierter Erhebungsinstrumente werden selbstberichtete Daten von N=849 Mitarbeiter_innen deutscher Unternehmen erhoben. Die Einschätzung des Führungsverhaltens der direkten Führungskraft erfolgt auf Grundlage der Führungsstile des „Full Range of Leadership Modells“ nach Avolio und Bass (1991) unter Zuhilfenahme des MLQ 5x short. Die Ergebnisse der Datenauswertung mittels Strukturgleichungsmodellierung weisen darauf hin, dass das Führungsverhalten der bzw. des direkten Vorgesetzten Einfluss auf das Technostress-Empfinden der Mitarbeiter_innen hat.
Solar phase scintillation and solar amplitude scintillation are fundamentally important in deep space mission operations for designing a communication system capable of transmitting signals when the signal path is close to the Sun. The ESA’s BepiColombo measurement data were analyzed in a previous paper in terms of the power spectral density of the solar phase scintillation, also with a comparison with Woo’s solar phase scintillation theory, when X-band and Ka-band signals propagate close to the Sun with a small Sun-Earth-Probe (SEP) angle during the superior solar conjunction campaign in March 2021 in its cruise phase to Mercury. In this paper the solar amplitude scintillation is analyzed both by calculating the power spectral density and the scintillation index. The results of scintillation index, derived from these measurement data, fit the NASA JPL’s scintillation index model.
With regard to AI as a key technology, this scientific paper deals with the identification of user drivers on the purchase decision of a cooperative AI (as explainable AI—XAI), as well as the analysis of the willingness to pay in the context of value-based pricing. Besides the economic dimension with regard to usefulness and usability of the system, the focus is mainly on the (innovative) explainable character. The analysis is carried out by a choice-based conjoint analysis (CBC) using the example of an intelligent assistance system for employees that supports internal business processes and workflows in business organizations. For this purpose, fictitious purchase offers were created under which decision-makers in manufacturing business organizations in Germany made simulated purchase decisions. The analysis shows that the target group attach great utility value to transparency in the sense of explanatory content, in addition to a high degree of interactivity and a high level of reliability.
Random Forests are a powerful and frequently applied Machine Learning tool. The permutation variable importance (VIMP) has been proposed to improve the explainability of such a pure prediction model. It describes the expected increase in prediction error after randomly permuting a variable and disturbing its association with the outcome. However, VIMPs measure a variable’s marginal influence only, that can make its interpretation difficult or even misleading. In the present work we address the general need for improving the explainability of prediction models by exploring VIMPs in the presence of correlated variables. In particular, we propose to use a variable’s residual information for investigating if its permutation importance partially or totally originates from correlated predictors. Hypotheses tests are derived by a resampling algorithm that can further support results by providing test decisions and p-values. In simulation studies we show that the proposed test controls type I error rates. When applying the methods to a Random Forest analysis of post-transplant survival after kidney transplantation, the importance of kidney donor quality for predicting post-transplant survival is shown to be high. However, the transplant allocation policy introduces correlations with other well-known predictors, which raises the concern that the importance of kidney donor quality may simply originate from these predictors. By using the proposed method, this concern is addressed and it is demonstrated that kidney donor quality plays an important role in post-transplant survival, regardless of correlations with other predictors.
Evaluation of the Explanatory Power Of Layer-wise Relevance Propagation using Adversarial Examples
(2023)
Approaches for visualizing and explaining the decision process of convolutional neural networks (CNNs) have recently received increasing attention. Particularly popular approaches are so-called saliency methods, which aim to assign a valence to each input pixel based on its importance and influence on the classification via saliency maps. In our paper, we contribute by a novel analyzing approach build on adversarial examples to investigate the explanatory power of saliency methods exemplified by layer-wise relevance propagation (LRP). Based on the hypothesis that distinct decisions, such as an image’s classification and the classification of its corresponding adversarial examples, should yield to dissimilar saliency maps to provide transparent rationales, we break down relevance scores of images and corresponding adversarial examples and analyze them using a comprehensive statistical evaluation. It turns out that different relevance decomposition rules of LRP do not lead to clearly distinguishable saliency maps for images and corresponding adversarial examples, neither in terms of their contour lines, nor in terms of the statistical analysis.
Der Begriff und das Thema Nachhaltigkeit haben sich in der Sozialen Arbeit etabliert. Offen bleibt bisher, was genau mit Nachhaltigkeit verbunden wird und in welchen Kontexten Sozialer Arbeit welche Bezüge zur Nachhaltigkeitsdebatte aufgerufen werden. Der vorliegende Einführungsbeitrag in den Schwerpunkt Nachhaltigkeit – ein Thema (in) der Sozialen Arbeit will aufzeigen und systematisieren, was Nachhaltigkeit für die Soziale Arbeit derzeit bedeutet und zukünftig bedeuten kann oder soll. Neben einer theoretischen Auseinandersetzung gewährt er exemplarische Einblicke in die Nachhaltigkeitsdebatten in Praxisfeldern Sozialen Arbeit, identifiziert Impulse für Theorieentwicklung und Handlungskonzepte der Sozialen Arbeit und führt in die Beiträge des Schwerpunktes ein.
Artificial Intelligence in studies—use of ChatGPT and AI-based tools among students in Germany
(2023)
AI-based tools such as ChatGPT and GPT-4 are currently changing the university landscape and in many places, the consequences for future forms of teaching and examination are already being discussed. In order to create an empirical basis for this, a nationwide survey of students was carried out in order to analyse the use and possible characteristics of AI-based tools that are important to students. The aim of the quantitative study is to be able to draw conclusions about how students use such AI tools. A total of more than 6300 students across Germany took part in the anonymous survey. The results of this quantitative analysis make it clear that almost two-thirds of the students surveyed use or have used AI-based tools as part of their studies. In this context, almost half of the students explicitly mention ChatGPT or GPT-4 as a tool they use. Students of engineering sciences, mathematics and natural sciences use AI-based tools most frequently. A differentiated examination of the usage behaviour makes it clear that students use AI-based tools in a variety of ways. Clarifying questions of understanding and explaining subject-specific concepts are the most relevant reasons for use in this context.
A novel material testing concept is developed in order to provide tensile and compressive properties within a single mechanical test. A new specimen geometry is designed for testing in a universal testing machine. Under tensile load, both a homogeneous tensile stress condition as well as a homogeneous compressive stress condition occur in the specimen. Measurements accompanying the experimental test with digital image correlation provide tensile and compressive Poisson’s ratio as well as tensile modulus. These properties are input parameters for subsequent finite element simulations. The compressive modulus is determined by iteratively adjusting finite element simulations in order to couple experimental and simulated results. For validating the concept, experimental tests are carried out on polyoxymethylene. While the tensile Poisson’s ratio of the new concept shows the best agreement with the reference value, the compressive modulus is approximately 15% higher. Further work should focus on an appropriate material model in order to reduce the deviation.
A growing number of economic geography scholars have discussed the
spatial dimensions of sustainability transitions (STs), which entail radical changes in socio-technical systems to overcome societal, economic, and ecological problems. This involves innovation processes with a broad range of distinctive actors. Innovation intermediaries, such as universities and research institutes, are needed to support and accelerate the transfer of knowledge. Nevertheless, little is known about the influence of such actors on the configuration of the knowledge bases required for STs. This article presents insights from 14 semi-structured interviews with experts conducted in a regional innovation system (RIS) in East Germany. In cooperation with the Eberswalde University for Sustainable Development, we investigate four innovation intermediaries in the region of Eberswalde. The analytical framework links the concept of differentiated knowledge bases to small wins. Our results show that, first, in the Eberswalde region, the relevant actors involved in regional knowledge transfer focus predominantly on synthetic knowledge bases, such as experiencebased knowledge of local area settings. Second, symbolic knowledge bases are crucial and often prerequisites for intermediary organizations to recombine knowledge bases and support the capability to innovate in regional knowledge transfer. Symbolic knowledge entails the ability to translate scientific findings to a language that can be understood by the various actors in knowledge transfer. Third, changes in organizational structures complement changes in cultural–cognitive and normative institutions to support innovation on a systemic level and foster change processes.
Rezension „Cyber-Sicherheit“
(2023)
The manufacturing industry is undergoing a transformation marked by the emergence of Industry 4.0 and Industry 5.0 paradigms, which are characterized by the integration and automation of machinery. Thereby, the machinery evolves into Cyber-Physical Systems (CPSs). These CPSs consist of software and hardware modules implementing complex manufacturing processes. The ongoing integration of machinery, and external technologies, e.g., the Industrial Internet of Things (IIoT), led to an evolving Smart Manufacturing (SM) environment. At the same time, legacy machinery, the brownfield machinery, exists side-by-side with modern CPSs. The brownfield machinery might be integrated by retrofitting in the modern manufacturing process. Therefore, the evolution of the SM domain thriven by the Industry 4.0 and Industry 5.0 paradigms leads to a more complex SM environment. Moreover, the integration and ongoing adaption of technologies and processes introduce novel relationships and dependencies between employed machinery and systems. Fault Diagnosis (FD) in such a complex SM environment becomes more time-consuming and laborious. A side effect of the ongoing evolution is the advancing capabilities of the machinery and the ability to produce data. Therewith, not only complex data has to be analyzed during any FD but also vast quantities. The search for the origin of the fault is challenging. Additionally, technical challenges in the SM environment hinder a thorough FD. For instance, the available bandwidth for data transmission is unequal to the capabilities of the machinery to produce vast data quantities. Therefore, the application challenge exists to focus on specific areas of the SM environment while choosing a reasonable granularity in data surveillance to cover the fault traces without losing too much information. Thereby, any FD depends heavily on the domain knowledge of the professionals entrusted with the FD task. On top, there is also economic pressure, which raises the tension on the employed professionals as an unexpected downtime, and the loss in production quantity equals the economic loss.
The thesis introduces context-aware FD to mitigate the risen complexity of the SM environment and support the professionals in their work. By supporting the professionals, the time for FD can be reduced, which results in faster fault amendment and reduced cost-intensive production downtimes. The Context-Aware Diagnosis in Smart Manufacturing (TAOISM) Visual Analytics (VA) model backs the context-aware FD. The TAOISM VA model is the theoretical foundation for the context-aware FD and defines the data layer, the models layer, the visualization layer, and the knowledge layer for SM. Hereby, the VA model enables the definition of context, context models, and context hierarchies for their integration in the respective layers. The main idea behind the context-aware FD is to use the narrowing character of the context definition to slice vast amounts of data into manageable context-separated data groups. Thereby, the context model works as a virtual boundary across machinery and systems, which encloses the physical domain (hardware) and the immaterial domain (software) equally. Further, the thesis focuses on contextual faults, which arise from context model violations, and proposes approaches for collecting contextual data. Also, the automated building of context models and the extraction and transformation of contextual data is part of the thesis. Employing the context models impacts each layer of the proposed TAOISM VA model. For each layer, various approaches show the impact of the context models and their employment in three different application scenarios for FD in SM. The performed research is tested and verified in the scenarios of Robotics Application Development (RAD), Maintenance of Industrial Inspection Machines (MIIM) and Abnormal Event Management in Production Lines (AEMPL). Along with employing context models, data augmentation with context models is proposed. Along with other benefits, the presented data augmentation technique has the ability to balance undersampled datasets, which would enable a reduction of data recordings for any context-aware FD in the future. Thereby, the data augmentation technique is to answer the existing inaccuracies in an SM environment, which also impacts the quality of any employed Artificial Intelligence (AI). Another approach targets the unsupervised selection of production-relevant variables to focus FD-related data recordings and surveillance on areas of the SM environment active during production automatically without any domain knowledge involved. The hypothesis, which was proven right, was that faults, especially contextual faults, occur more often on active software and hardware modules. Another challenge from the vast amount of data is that labeling data for AI becomes uneconomical, even for small fault cases in AI. As a result, evaluating any AI model in SM becomes challenging, as standard measures, e.g., accuracy, precision, recall, and F1-score, cannot be applied. In this case, the thesis proposes novel AI performance metrics that decouple comparability and correctness to enable the evaluation of AI models in an SM environment. All the contributions have led to the development of two distinct Proof of Concepts (PoCs). The PoCs are the reference implementation of the context-aware FD and reflect a knowledge-based FD Expert System (ES) and an unsupervised data-driven FD system. The latter was part of the thorough evaluation of the context-aware FD by two groups of domain experts and junior professionals. The successful qualitative evaluation not only hints towards a working context-aware FD but also unveils future research directions and a future vision for SM. Additional domain expert interviews expose the views on the relevancy of a context-aware FD in SM for the future. In general, the evaluation hints towards a context-aware FD, which has versatile applicability, usability, and suitability in SM-related FD.
The overall objective of this dissertation is to enable a more efficient and effective point cloud and mesh partition for artists and 3D application developers. In this dissertation, 3D scans are assumed as the source data material of the 3D application development, reducing the manual and time-consuming modelling of virtual objects. Furthermore, the scanned data is assumed to be processed to a point cloud and reconstructed to a polygon mesh. The mesh has to be partitioned into the objects of interest to design specific interactions with a game engine. Interviews revealed that the partition is manually conducted on a mesh with a 3D manipulation software, which is time-consuming. The partition creation should be automated to increase efficiency and effectiveness. Freely available point cloud and mesh partition algorithms require an expert with appropriate programming skills and field knowledge, which makes them difficult to use. More precisely, the algorithms cannot be used in existing workflows as they are not implemented in a common graphical 3D manipulation software. Beneath these problems, the partition automation should work on real-world data and have a low runtime to raise efficiency. Different sub-research objectives were formulated from these problems and requirements, leading to novel approaches in the domains of: (a) sequential partition creation with deep reinforcement and imitation learning, (b) episodic partition creation with graph neural networks, (c) match-based reward calculation and (d) synthetic scene generation. One sub-research objective is the replacement of a human expert with an agent. In this context, a novel deep reinforcement learning (DRL) partition framework is presented. Experiments were conducted using this framework combined with the region growing algorithm and synthetic scenes created by a self-developed scene generator. The maximum reward could almost be achieved with a fine-tuned PointNet and by evaluating the wall and non-wall objects separately. This approach is not applicable to real-world scenes, which is necessary to achieve the efficiency and effectiveness objective. Therefore, another DRL partition approach is introduced, where an agent unifies superpoints in the so-called superpoint growing environment. The point cloud is divided into superpoints, which will be unified into the objects of interest by an agent. The experimental results show that this approach can be applied to real-world scenes. Beneath the application of DRL, an imitation learning approach was developed, increasing the agent’s performance in the superpoint growing environment. The runtime in the sequential superpoint growing environment is poor, as each union decision requires a neural network call. Hence, a further sub-research objective is to improve the runtime. An episodic environment was developed as a solution, only requiring one graph neural network call. Similarities between superpoints are estimated in this environment and passed to a union algorithm. The differences between two graph neural network architectures and two union algorithms were experimentally investigated. According to the results, calculating the superpoint similarities with a correlation of the embedded node features is more robust than the similarity estimation with a sigmoid activation function. The reward function, used in the DRL partition approaches, was realised by a matching procedure. As this function influences the partition quality, another sub-research objective is to investigate the differences between various match types. Matching functions from the literature were compared, and another match type was introduced. The usage of different match types in the learning process was experimentally evaluated. Although an agent gets more feedback with all match types, the best results (visual and in terms of the partition size) were achieved by only using first-order matches in the reward function. The synthetic scenes of the region growing approach lack realism as the lighting information is ignored, which can be important to train networks for the partition task. Therefore, a further sub-research objective is to develop a scene generator where the lighting is taken into account. After its development, the generated scenes were experimentally evaluated in a pre-training task. It turned out that the lighting information is important for a pre-training as larger accuracies were achieved. Furthermore, a faster convergence can be achieved with the pre-trained network instead of training a network on a target data set from scratch.
Another sub-research objective targets the development of a usable partition interface. In this context, the Blender add-on OpenXtract was developed, containing five open-source point cloud partition algorithms. The partition algorithms were extended by approximating geodesic distances so that the edges of meshes are used. An experiment has shown that the extended algorithms produce larger accuracies, which is considered an increase in effectiveness. Moreover, unstructured interviews revealed that OpenXtract can improve the effectiveness and efficiency of the partition creation.
"Jede Stadt hat ihre Mollerstadt" war ein geflügelter Satz im Forschungsprojekt "s:ne", dem Transferprojekt, aus dem diese Publikation hervorgegangen ist.
Tatsächlich haben viele deutsche Städte ähnliche Quartiere. Im Zweiten Weltkrieg zerbombt, wieder aufgebaut im Stil und mit den niedrigen Standards der Nachkriegszeit. Mit einer kleinteiligen Parzellenstruktur und heterogener Eigentümerschaft in einer innerstädtischen Eins-B-Lage - wo Wohnen anders als in der benachbarten "City" noch eine zentrale Rolle spielt.
Diese Quartiere sind in die Jahre gekommen. Sie haben aber eine besondere Bedeutung auch und gerade im Hinblick auf eine nachhaltige Stadtentwicklung im Sinne der Stadt der kurzen Wege.
Diese Arbeit verbindet durch die Einordnung in den Nachhaltigkeitswissenschaften als multidisziplinäres Wissenschaftsgebiet einen ingenieurstechnischen- und einen sozialwissenschaftlichen Teil. Im ingenieurstechnischen Teil konnten Indizien dafür gefunden werden, dass Rezyklatkunststoffe hinsichtlich deren mechanischen Eigenschaften in hochbelasteten Strukturbauteilen eingesetzt werden können und somit Neuwarekunststoffe substituieren. Dazu werden Neuwaren- und Rezyklatkunststoffe aus Polyprophylen mit 30 Gewichtsprozent Talkumfüllung systematisch untersucht. Dabei wird der Einfluss von Kerben, Bindenähten, Mittelspannung, Temperatur und Alterung auf die mechanischen Eigenschaften unter statischer und zyklischer Belastung untersucht. Begleitende analytische Untersuchungen beschreiben die molekularen und kristallinen Unterschiede von Neuwaren- und Rezyklatkunststoffen. Damit können Rückschlüsse auf die mechanischen Eigenschaften gezogen werden und lassen sich dadurch wissenschaftlich begründen.
Die ermittelten mechanischen Kennwerte unter statischer und zyklischer Belastung fließen in ein Kerbspannungskonzept nach dem höchst beanspruchten Werkstoffvolumen V80 und nach dem Spannungsgradienten χ* ein. Lokale Beanspruchungskennwerte werden nach dem in dieser Arbeit entwickelten Konzept der relativen inelastischen Dehnungen ermittelt.
Damit wird an einem Geräteträger einer Geschirrspülmaschine ein zyklischer Festigkeitsnachweis erbracht, dass der Geräteträger aus dem untersuchten Rezyklatmaterial die geforderte Lebensdauer ertragen kann. Begleitende numerische Berechnungen und Bauteilversuche unter Einsatzbedingungen validieren die Lebensdauerabschätzung.
Im sozialwissenschaftlichen Teil dieser Arbeit wird untersucht, wie Rezyklate aus ihrer technologischen Nische in eine breite Anwendung gelangen können. Dazu wird das Modell der Multi-Level Perspective nach Geels [Gee02] verwendet. Um Rezyklate aus der technologischen Nische zu heben, bedarf es einer Strategie von verschiedenen Akteuren aus unterschiedlichen Ebenen. Dabei soll die Strategie Faktoren ermitteln, die es ermöglichen, das Rezyklate ein eigenes Regime bilden können. Diese Strategie wird in leitfadengestützten Experteninterviews mit Akteurgruppen aus dem sozioökonomischen, -technischen und -politischen Bereich, erfragt. Dabei wird mit Hilfe einer inhaltlich strukturierenden qualitativen Inhaltsanalyse die Strategie abgeleitet, die Rezyklate verstärkter in technischen Anwendungen einsetzt und wie sich der Markt hierfür zukünftig weiterentwickeln muss.
Background
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.
Valid 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.
Discrete uniform and homogeneous p-values often arise in applications with multiple testing. For example, this occurs in genome wide association studies whenever
a non-parametric one-sample (or two-sample) test is
applied throughout the gene loci. In this paper, we considermultiple comparison procedures for such scenarios
based on several existing estimators for the proportion
of true null hypotheses, 𝜋0, which take the discreteness
of the p-values into account. The theoretical guarantees
of the several approaches with respect to the estimation of 𝜋0 and the false discovery rate control are reviewed. The performance of the discrete procedures is investigated through intensive Monte Carlo simulations considering both independent and dependent p-values. The methods are applied to three real data sets for illustration
purposes too. Since the particular estimator of
𝜋0 used to compute the q-values may influence its performance, relative advantages and disadvantages of the reviewed procedures are discussed. Practical recommendations are given.
Several classical methods exist for controlling the false discovery exceedance (FDX) for large-scale multiple testing problems, among them the Lehmann-Romano procedure (Lehmann and Romano 2005) ([LR] below) and the Guo-Romano procedure (Guo and Romano 2007) ([GR] below). While these two procedures are the most prominent, they were
originally designed for homogeneous test statistics, that is, when the null distribution functions of the p-values Fi, 1 ≤ i ≤ m, are all equal. In many applications, however, the data are heterogeneous which leads to heterogeneous null distribution functions. Ignoring this heterogeneity induces a lack of power. In this paper, we develop three new procedures that incorporate the Fi’s, while maintaining rigorous FDX control. The heterogeneous version of [LR], denoted [HLR], is based on the arithmetic average of the Fi’s, while the heterogeneous version of [GR], denoted [HGR], is based on the geometric average of the Fi’s. We also introduce a procedure [PB], that is based on the Poisson-binomial distribution and that uniformly improves [HLR] and [HGR], at the price of a higher computational complexity. Perhaps surprisingly, this shows that, contrary to the known theory of false discovery rate (FDR) control under heterogeneity, the way to incorporate the Fi’s can be particularly simple in the case of FDX control, and does not require any further correction term. The performances of the new proposed procedures are illustrated by real and simulated data in two important heterogeneous settings: first, when the test statistics are continuous but
the p-values are weighted by some known independent weight vector, e.g., coming from co-data sets; second, when the test statistics are discretely distributed, as is the case for data representing frequencies or counts. Our new procedures are implemented in the R package FDX, see Junge and Döhler (2020).
This experimental study investigates readers’ perceived text quality and trust towards journalistic opinion pieces written by the language model GPT-3. GPT-3 is capable of automatically writing texts in human language and is often referred to as an artificial intelligence (AI). In a 2x2x2 within- subjects experimental design, 192 participants were presented with two randomly selected articles each for evaluation. The articles were varied with regard to the variables actual source, declared source (in each case human-written or AI-written) and the topic (1 & 2). Prior to the experimental design, participants indicated the extent to which they agreed with various statements about the trustworthiness of AI in order to capture their personal attitudes towards the topic.
The study found for one, that readers considered articles written by GPT-3 to be just as good as those written by human journalists. The AI-generated versions were rated slightly better in terms of text quality as well as the trust placed in the content. However, the effect was not statistically significant. For another, no negative effect on article perception was found for texts disclosed as AI-written. Articles declared as written by an AI were mostly rated equally well or again minimally better than texts declared as human, especially regarding trust. Only the readability was rated slightly worse for the case of declaring the AI as a source. Furthermore, a correlation was found between the participants’ personal attitudes towards the topic of AI and their perception of allegedly AI-written articles. For articles declared as AI-written, there are slight to moderate positive correlations of the personal attitudes towards AI with each quality rating criterion. Personal preconception thus plays a role in the perception of AI-written articles.
KI-basierte Tools wie ChatGPT bzw. GPT-4 verändern derzeit die Hochschullandschaft und vielerorts wird bereits über die Konsequenzen für die zukünftigen Lehr- und Prüfungsformen diskutiert. Um hier eine empirische Grundlage zu schaffen, ist eine deutschlandweite Befragung von Studierenden durchgeführt worden, in welcher das Nutzungsverhalten im Umgang mit KI-basierten Tools im Rahmen des Studiums und Alltags erfasst wurde. Hierbei wurden unter anderem diverse Funktionen der KIbasierten Tools identifiziert, die für die Studierenden als besonders wichtig eingeschätzt wurden. Das Ziel der quantitativen Befragung lag somit in der Erfassung davon, wie KI-Tools genutzt werden und welche Faktoren für die Nutzung maßgeblich sind.
Insgesamt haben sich deutschlandweit über 6300 Studierende an der anonymen Befragung beteiligt. Die Ergebnisse dieser quantitativen Analyse verdeutlichen, dass fast zwei Drittel der befragten Studierenden KI-basierte Tools im Rahmen des Studiums nutzen bzw. genutzt haben. Explizit nennen in diesem Kontext fast die Hälfte der befragten Studierenden ChatGPT bzw. GPT-4 als genutztes Tool. Am häufigsten nutzen Studierende der Ingenieurwissenschaften sowie Mathematik und Naturwissenschaften KI-basierte Tools.
Eine differenzierte Betrachtung des Nutzungsverhaltens verdeutlicht, dass die Studierenden KI-basierte Tools vielfältig einsetzen. Die Klärung von Verständnisfragen und Erläuterung fachspezifischer Konzepte zählen in diesem Kontext zu den relevantesten Nutzungsgründen.
Die 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.
Die hessische Landesregierung hat sich dazu verpflichtet, zukünftig den Endenergieverbrauch für Wärme vollständig aus erneuerbaren Energienquellen zu beziehen. Dies erfordert eine Umstellung von fossilen Energieträgern hin zu Heizkonzepten, welche erneuerbare Energiequellen nutzen. Durch den Einsatz einer Wärmepumpe besteht die Möglichkeit Wärmequellen zu erschließen, wordurch fossile Energieträger in jedem Fall keine Verwendung mehr finden.
In dieser Thesis wird die Machbarkeit des Einsatzes von Wärmepumpen am Campus Schöfferstraße der Hochschule Darmstadt geprüft.
Des Weiteren soll diese Arbeit aufzeigen, welche Maßnahmen für den Einsatz von Wärmepumpen erforderlich sind und welche Risiken bzw. Schwierigkeiten sich daraus ergeben. Auf dieser Grundlage ließen sich Investitionskosten abschätzen. Außerdem soll vermittelt werden, welche Faktoren einen Einfluss auf den Einsatz von Wärmepumpen haben.
Im Hinblick auf die Frage, ob der Campus Schöfferstraße an ein Fernwärmenetz der Stadt Darmstadt angeschlossen werden soll oder sich autark mit Wärmepumpen versorgt ist eine Machbarkeitsstudie für den Einsatz von Wärmepumpen essentiell.