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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.