RWTH Aachen University

Approximation of high dimensional functions is in the focus of machine learning and data-based scientific computing. In many applications, empirical risk minimisation techniques over nonlinear model classes are employed. Neural networks, kernel methods and tensor decomposition techniques are among the most popular model classes. This work is targeted on providing a numerical study comparing the performance of these methods on various high-dimensional functions with focus on optimal control problems, where the collection of the dataset is based on the application of the State-Dependent Riccati Equation, an extension of the LQR technique to nonlinear systems and nonquadratic cost functionals.

Some introductory remarks about the philosophy of technology are required to permit a philosophical analysis of the phenomenon of chatbots.

Large Language Models have been shown to carry the potential to change many of our communication habits, our workflows and employment, and our understanding of some dimensions of human interactions and their norms (like customer service). This makes them uniquely positioned in the garden of technological varieties to become the most relationally relevant technology, with considerable consequences for our way of relating to each other, and to technology.

After all conceptual tools are assembled, we can assess how chatbots can be understood as a candidate for social relationships. We reject the idea of anthropomorphism, as the negatives outweigh the positives. We also reject the idea that we relate to machines via mental states or gendered appearance. We discuss the idea of understanding chatbots as instances of the objective spirit but reject this on conceptual grounds. Instead, we can understand chatbots as opening up a similar social category like animals, through a “second domestication.” This allows for a plethora of non-anthropomorphic realizations.

LLMs, general-purpose chatbots, and any more specific interactive conversational technology will be more socially relatable than any other technology before. This has thus been the premise under which philosophers have speculated about questions of moral standing, robot rights, and other terms to reckon with their different social and moral status. This chapter explores the relationality of these machines via the concept of positionality, investigates the consequences for our relationships with technology, and explores other values relevant in this context.

This chapter discusses the constitution of the social as a relational network. We propose to understand social relationships as embedded and consequential relationships. Through a pragmatic approach to assigning descriptors and thereby constituting them, we reject the idea of essentialist interpretations of social categories. The introduction of the concepts of domestication and digitization proves the points by showing that social categories have been expanded to include animals, and that physical presence is not required anymore for social relationships.

This chapter turns toward the consequences of human–human relationships when a relational approach to human–machine relationships is adopted. This happens in four ways. First, we enter the debate on robot rights and test the pragmacentrist approach. We find that a relational approach of this kind, similar to Gunkel’s and Coeckelbergh’s positions, defuses many of the issues motivating the robot rights debate. Second, we assess whether human-centered design requires some limitations to this relational approach, which it does. Third, we evaluate the relational real estate and propose to understand conversational AI as “digital persons” to explain issues of responsibility and blame. And fourth, we introduce new terminology to denote the fault lines creating social tension—robophobia and robophilia.

Large‐format tabless cylindrical cells have been a top research subject within recent years. However, research so far has exclusively focused on isolated understanding of individual aspects such as the performance, safety, or cost. This study introduces a global optimization framework for battery systems with tabless cylindrical cells based on the groundwork laid within recent years. The framework is applied to gain comprehensive understanding of cross interactions between different design variables and the key performance indicators of the battery system. It was found that a well‐defined diameter exists which optimizes the battery energy for given boundary conditions. The multiobjective trade‐off between energy, performance, weight, and cost however might lead to different solutions with respect to the desired properties of the system. Small cylindrical cells with diameter less than 25 mm provide enhanced performance but lower energy and higher cost. Very large cylindrical cells with diameter more than 50 mm have less options for interconnection but provide the best cost‐saving potential. With realistic constraints, only diameters larger than 40 mm achieve Pareto‐optimal solutions. Aluminum housings are found to outmatch steel housings in nearly all properties, especially for larger diameters. Considering the widespread introduction of aluminum housings is recommended for automotive manufacturers.

One of the most mature technologies for green hydrogen production is alkaline water electrolysis. However, this process is kinetically limited by the sluggish oxygen evolution reaction (OER). Improving the OER kinetics requires electrocatalysts, which can offer superior catalytic activity and stability in alkaline environments. Stainless steel (SS) has been reported as a cost‐effective and promising OER electrode due to its ability to form active Ni‐Fe oxyhydroxides during OER. However, it is limited by a high Fe‐to‐Ni ratio, leading to severe Fe‐leaching in alkaline environments. This affects not only the electrode activity and stability but can also be detrimental to the electrolyzer system. Therefore, we investigate the effect of different Ni‐coatings on both pure Ni‐ and SS‐supports on the OER activity, while monitoring the extent of Fe‐leaching during continuous operation. We show that thin layers of Ni enable enhanced OER activities compared to thicker ones. Especially, a less than 1 µm thick Ni layer on an SS‐support shows superior OER activity and stability with respect to the bare supports. X‐ray photoelectron spectroscopy reveals traces of oxidized Fe species on the catalyst surface after OER, suggesting that Fe from the SS may be incorporated into the layer during operation, forming active Ni‐Fe oxyhydroxides with a very low Fe leaching rate. Utilizing inductively coupled plasma‐optical emission spectroscopy, we prove that thin Ni layers on SS decrease Fe leaching whereas the Fe from the uncoated SS‐support dissolves into the electrolyte during operation. Thus, OER active and stable electrodes can be obtained while maintaining a low Fe concentration in the electrolyte. This is particularly relevant for application in high‐performance electrolyzer systems.

This article outlines a method for utilizing machine learning, particularly artificial neural networks, to estimate the fatigue strength of structural steel details. Data have been taken out of a structured database of fatigue tests, depicting the background of EN 1993-1-9. The artificial neural network has been trained and verified on the basis of experimental fatigue test results on the example of the transverse stiffener. Results show that the neural network is capable of predicting the fatigue strength of random transverse stiffener details. Comparisons have been made to a numerical approach applying the effective notch stress approach, showing also limits. This study helps paving the way for a thorough investigation into the complex relationship between different influencing factors and fatigue strength, highlighting the benefits and limitations of using machine learning tools.

Mg3(Sb,Bi)2 alloys offer exceptional near‐room temperature thermoelectric (TE) performance comparable to Bi2Te3. However, the low carrier mobility due to Mg vacancies and relatively high lattice thermal conductivity adversely affect overall TE performance. Herein, carrier mobility and thermal conductivity of Mg3(Sb,Bi)2 are decoupled by modulating both the phase interface and grain boundaries through incorporating different sizes of iron (Fe). Ferromagnetic micrometer‐sized Fe particles enhance the figure of merit (ZT) more than superparamagnetic nano‐sized counterparts. Magnetic moments of Fe induce the charge density overlap near phase boundaries and ≈1 nm Fe interfacial layer lowers grain‐boundary barriers, leading to sharply increased carrier mobility. Moreover, the additional magnon‐phonon scattering reduces lattice thermal conductivity by over 40%. Consequently, Fe/Mg3(Sb,Bi)2 composite achieves a high average ZT of 1.4 over room temperature to 573 K. The fabricated Mg3(Sb,Bi)2‐CdSb module demonstrates a high conversion efficiency of 8.4% under a 275 K temperature gradient, among the best for Mg3(Sb,Bi)2‐based modules. This work uncovers the role of thermo‐electro‐magnetic interactions in bolstering TE performance and inspires the development of low‐cost, high‐efficiency TE modules for low‐grade heat recovery.

The term “caring community” typically refers to regional concepts of a culture of care that integrates professional and volunteer services, focusing on issues of dying, death, and grief. Both the caring community and the broader compassionate city movements are gaining momentum nationally and internationally, with the two concepts existing alongside each other. The concept of caring communities/compassionate cities originally emerged within the context of public health, inspired by the Healthy Cities Network. Allan Kellehear was the first to connect the public health approach to the palliative care movement, highlighting the societal approach of local care networks aimed at fostering civic responsibility for fellow citizens. Professional structures collaborate with civil society as well as with political and private sector actors to advocate for a dignified life and death for all citizens and promote mutual relational responsibility. Since the adoption of guidelines in the context of the Charter for the Care of the Critically Ill and Dying in Germany in 2016, a federal coordination office has been established to sustainably support the development and implementation of caring communities.

Cold acclimation increases insulin sensitivity, and some level of muscle contraction appears to be needed for provoking this effect. Here 15 men and (postmenopausal) women with overweight or obesity, the majority of whom had impaired glucose tolerance, were intermittently exposed to cold to induce 1 h of shivering per day over 10 days. We determined the effect of cold acclimation with shivering on overnight fasted oral glucose tolerance (primary outcome) and on skeletal muscle glucose transporter 4 translocation (secondary outcome). We find that cold acclimation with shivering improves oral glucose tolerance, fasting glucose, triglycerides, non-esterified fatty acid concentrations and blood pressure. Cold acclimation with shivering may thus represent an alternative lifestyle approach for the prevention and treatment of obesity-related metabolic disorders. ClinicalTrials.gov registration: NCT04516018.

Predicting the failure of a noncrystalline solid such as silica glass is challenging due to the complex fracture phenomenon. Failure originates from particular local zones where the atomic structure is highly susceptible to local rearrangement. The local yield stress (LYS) method predicts such locations of plastic events during external deformation. Despite the precise prediction accuracy of the location of the failure initiation, the LYS method requires substantial computational resources as it evaluates each local zone separately for rearrangement and is time‐consuming. Owing to new age development in artificial neural networks, a relation can be established between the plastic events and the atomic structure. In this study, a neural network is trained to identify this relation and, hence, predict the locations of atomic rearrangements. This approach results in a heatmap indicating soft spots that highly correlate with elementary events of fracture and allows for reliable incorporation of structural identification to coarser mechanical frameworks that need local information for a physically meaningful description. The advantage of this method is that it can be trained on a specific material and can predict the local soft spots for unseen structural information without the need for multiple time‐consuming molecular simulations.

The brain integrates activity across networks of interconnected neurons to generate behavioral outputs. Several physiological and imaging-based approaches have been previously used to monitor responses of individual neurons. While these techniques can identify cellular responses greater than the neuron’s action potential threshold, less is known about the events that are smaller than this threshold or are localized to subcellular compartments. Here we use NEAs to obtain temporary intracellular access to neurons allowing us to record information-rich data that indicates action potentials, and sub-threshold electrical activity. We demonstrate these recordings from primary hippocampal neurons, induced pluripotent stem cell-derived (iPSC) neurons, and iPSC-derived brain organoids. Moreover, our results show that our arrays can record activity from subcellular compartments of the neuron. We suggest that these data might enable us to correlate activity changes in individual neurons with network behavior, a key goal of systems neuroscience.

Zusammenfassung Hintergrund Für die Qualitätssicherung in der Pathologie ist die strukturierte Erfassung von Daten aus histopathologischen Befunden sowie die Interoperabilität dieser Daten von zentraler Bedeutung. Methodik Zur Harmonisierung des Inhalts der Befunde hat die International Collaboration on Cancer Reporting (ICCR) standardisierte Datensätze erstellt. Diese liegen noch nicht flächendeckend auf Deutsch vor. Hier wird diese Lücke am Beispiel des Usecase der transurethralen Blasenresektion (TUR-B) adressiert. Ergebnisse Wir beschreiben den Prozess der Etablierung der Datensätze durch Übersetzung, Einbindung von SNOMED-CT-Codes und Nutzung der Hierarchie in SNOMED zum Füllen der Felder des Datensatzes. Zusätzlich definieren wir Regeln zur Datenqualitätsprüfung anhand der TUR‑B. Diskussion Mit diesem Beitrag haben wir beispielhaft eine deutsche Version der ICCR-Datensatz TUR‑B inklusive Mapping auf die SNOMED-CT-Terminologie geschaffen. Weitere Schritte sollten nun in der abstrakten Modellierung von Erkrankungen liegen, um das in SNOMED CT liegende Potenzial weiter auszuschöpfen.

Understanding sustainability behavior is essential in tackling the global challenge of climate change. The importance of studying sustainability practices and their dynamics grows in light of recent global crises such as the COVID-19 pandemic and the energy crisis following the Ukraine war. These events both challenge and shape individual sustainable practices, offering opportunities for fostering individual sustainable practices and enhancing societal resilience. An online survey was conducted in Germany (n = 571, May 2023) to investigate sustainable behavior dynamics (mobility, energy-saving, and shopping habits) and to identify segments reflecting behavioral shifts. We found relative stability in sustainable mobility choices compared to pre-crisis times, with a tendency towards reduction, as well as an overall increase in energy-saving and sustainable shopping habits. Factor analyses revealed that sustainable mobility behavior (SMB) and sustainable consumer practices (SCP) formed two separate domains. Cluster analyses further identified four segments within each domain, each exhibiting unique behavioral patterns compared to pre-crisis practices. Examining individual variables, adopting more sustainable mobility practices was associated with sociodemographic factors (income, education, and area of living), higher levels of environmental awareness, institutional trust, and increased risk perceptions. Sociodemographic variables had less influence on sustainable consumer practices. Here, higher levels of knowledge, climate change awareness, trust, and risk perceptions played a significant role. Our findings highlight the importance of separately considering behavioral domains in understanding crises-induced changes in sustainability practices. Moreover, it is important to consider specific individual factors and to develop tailored interventions and policies to promote sustainable practices during volatile times.

Single cell RNA sequencing has provided unprecedented insights into the molecular cues and cellular heterogeneity underlying human disease. However, the high costs and complexity of single cell methods remain a major obstacle for generating large-scale human cohorts. Here, we compare current state-of-the-art single cell multiplexing technologies, and provide a widely applicable demultiplexing method, SoupLadle, that enables simple, yet robust high-throughput multiplexing leveraging genetic variability of patients.

This study aims to investigate the fatigue resistance of additively manufactured steel plates with galvanized surfaces using a Zn–5Al alloy. In addition to fatigue tests, this study examined the effect of hot-dip galvanization (HDG) on fatigue resistance by investigating the surface roughness, zinc layer condition, and quasi-static tension on both galvanized and ungalvanized specimens. The filling effect of the zinc coating using a Zn–5Al alloy on the surface was also examined, as it influences the surface shape and smooths local areas, leading to an improvement in fatigue resistance compared with conventional galvanized surfaces. Overall, this study provides insight into the effects of HDG on the fatigue resistance of additively manufactured steel plates with galvanized surfaces.