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Three-dimensional Distributed Acoustic Sensing to monitor geothermal fields in Munich, Germany
Get PDFInterphase-engineering by atomic layer deposition of nacre-inspired alumina composites
Get PDFNacre, also known as mother of pearl, is a layered brick-and-mortar structure composed of hard mineral platelets and soft organic protein. Found in the inner shells of certain mollusks, this natural architecture has evolved to optimize mechanical properties for the protection of the organism. The unique structured design of nacre and its exceptional mechanical performance have inspired the development of synthetic nacre-like materials. In this work, we introduce a novel approach for fabricating nacre-inspired ceramic composites using atomic layer deposition (ALD) to engineer the interphase, or “mortar”, between aligned alumina platelets. ALD, known for its sub-nanometer thickness control and conformal coating capabilities, enables uniform tuning of the interphase thickness from 30 nm up to 120 nm, offering a significant advantage in tailoring the mechanical properties of ceramic-ceramic composites. Our results reveal a direct correlation between ALD-deposited aluminum oxide thickness and enhanced mechanical performance, with increased modulus and hardness observed as the mortar thickens. Three-point bending tests further show that flexural strength is maximized with thicker ALD coatings. In situ micromechanical testing reveals crack initiation at surface defects in micro-cantilevers, with cracks effectively deflected by the nacre-inspired architecture. This resultsin an average flexural strength of approximately 400 MPa, achieved without additional heat treatment or sintering. The precision of ALD in creating conformal interphases is critical to the improved structural integrity and performance of the composite, demonstrating its potential asa powerful technique for fabricating high-performance, bio-inspired ceramic composites
Civil society-led shared mobility for transport equity? An empirical analysis from Berlin
Get PDFThis study explores the potential of community-led shared mobility to achieve equitable transport, using a socially innovative cargo bike-sharing system in Berlin as a case study. The empirical approach combines booking records, survey responses, and spatial data. This approach enables an analysis of transport equity in terms of spatial distribution, as well as user structure and behavior. The results suggest that community-led initiatives may complement commercial shared mobility operators and be associated with more equitable transport system outcomes. In particular, the results show no differences in cargo bike host locations based on social status index groups. Additionally, the gender distribution aligns with that of the general population. Cargo bikes were found to be used for diverse purposes which differed by social status area and gender. The findings suggest that promoting socially innovative, community-led projects may represent a promising governance approach associated with enhanced transport equity
DIME: Diffusion-Based Maximum Entropy Reinforcement Learning
Get PDFMaximum entropy reinforcement learning (MaxEnt-RL) has become the standard approach to RL due to its beneficial exploration properties. Traditionally, policies are parameterized using Gaussian distributions, which significantly limits their representational capacity. Diffusion-based policies offer a more expressive alternative, yet integrating them into MaxEnt-RL poses challenges—primarily due to the intractability of computing their marginal entropy. To overcome this, we propose Diffusion-Based Maximum Entropy RL (DIME). DIME leverages recent advances in approximate inference with diffusion models to derive a lower bound on the maximum entropy objective. Additionally, we propose a policy iteration scheme that provably converges to the optimal diffusion policy. Our method enables the use of expressive diffusion-based policies while retaining the principled exploration benefits of MaxEnt-RL, significantly outperforming other diffusion-based methods on challenging high-dimensional control benchmarks. It is also competitive with state-of-the-art non-diffusion based RL methods while requiring fewer algorithmic design choices and smaller update-to-data ratios, reducing computational complexity
Numerical analysis of uncertainty propagation in short fiber-reinforced composites: From injection molding to material testing
Get PDFThe present study investigates a novel methodology for the numerical assessment of uncertainties and their propagation in injection-molded fiber-reinforced polymers (FRPs). Focusing on two primary sources of uncertainty, which are the microstructural variability due to injection molding process parameters and inherent material scatter, the research examines their individual contributions to the scattering of effective properties, specifically the Young’s modulus of the composite. A random vector model was used to describe the orientation states across the structure, derived from multiple injection molding simulations with varying input parameter distributions. The scatter of structural material parameters is further built upon a joint distribution between orientation concentration and constitutive parameters. The results reveal that while the overall orientation states exhibited less scatter than expected, a clear relationship emerged between the concentration of injection molding parameters and the cumulative distribution functions (CDFs) of effective modulus, indicating non-linear interactions between orientation and material scatter. Additionally, the analysis highlighted the increased sensitivity of scattering behavior based on sample orientation, emphasizing the effect of geometry on flow properties. This research underscores the complex interplay of uncertainties in determining effective material behavior, suggesting that future studies should explore a broader range of input parameters and refine distribution assumptions. The findings provide valuable insights for advancing the design and manufacturing processes of polymer composites, establishing a foundation for more comprehensive analyses of uncertainty in material properties
Diffusive synchronization of phase waves in the FitzHugh–Nagumo system
Get PDFWe analyze synchronization of relaxation oscillations in multiple-timescale reaction-diffusion systems. Interpreting synchronization as convergence to frequency-synchronized wave-train solutions, we resolve for the first time the case of phase waves. These waves are nearly phase-synchronized relaxation oscillations, featuring quasistationary plateaus of length separated by fast transition layers, where . is the timescale separation parameter. Tracking the decay of modulations via a Bloch-wave eigenfunction analysis, we find a remarkably weak interaction strength of order . This weak layer interaction and many of the technical difficulties arise from repeated scattering of eigenfunctions through fold points at the ends of the quasistationary plateaus. We capture this by combining a novel geometric desingularization approach with Lin’s method, exponential trichotomies, and the Riccati transform. While our spectral stability analysis yields diffusive synchronization of all phase waves in the FitzHugh–Nagumo system, it also identifies potential finite-wavelength instabilities, which we realize in a system variant
Accessible Augmented Reality in Sheltered Workshops: A Mixed-Methods Evaluation for Users with Mental Disabilities
Get PDFA prominent application of Augmented Reality (AR) is to provide step-by-step guidance for procedural tasks as it allows information to be displayed in situ by overlaying it directly onto the user’s physical environment. While the potential of AR is well known, the perspectives and requirements of individuals with mental disabilities, who face both cognitive and psychological barriers at work, have yet to be addressed, particularly on Head-Mounted Displays (HMDs). To understand practical limitations of such a system, we conducted a mixed-methods user study with 29 participants, including individuals with mental disabilities, their colleagues, and support professionals. Participants used a commercially available system on an AR HMD to perform a machine setup task. Quantitative results revealed that participants with mental disabilities perceived the system as less usable than those without. Qualitative findings point towards actionable leverage points of improvement such as privacy-aware human support, motivating but lightweight gamification, user-controlled pacing with clear feedback, confidence-building interaction patterns, and clearer task intent of multimodal instructions
Key Elements of Trust in Building Renovation Data Management: A Usability Study of a Centralized Platform
Get PDFThe urgent need for building renovation is growing, yet the availability of machine-readable data remains limited, hindering the decision-making process for building owners. This paper addresses this gap by identifying key elements in document digitization, data enrichment, and data analysis that are essential to fostering trust in data processing. To address this, we developed a mock-up based on design thinking principles that aims to consolidate existing building information into a central, accessible location. This platform provides users with a comprehensive overview of building data. We conducted a user study with 44 participants to evaluate the usability of the platform using the System Usability Scale (SUS). The results showed a high SUS score, reflecting strong usability and positive feedback. Participants highlighted the value of centralizing building data, which significantly supports renovation decision-making. The results underscore the platform’s potential to drive digital transformation in the building sectors, marking a critical step forward in renovation planning
Gaussian Process Regression for System Identification of Autonomous Surface Vessels
Get PDFAutonomous surface vehicles (ASVs) have gained significant attention across a range of applications, yet a primary challenge lies in developing accurate mathematical models to describe their complex dynamical behavior. Given the partial submersion of surface vessels in water, deriving a first-principles description proves difficult. In general, data-driven approaches, particularly black-box and gray-box models, are increasingly employed to avoid the need of structural first-principle models. Among the range of supervised learning approaches, Gaussian Process Regression (GPR) models stand out due to their simplistic and nonparametric nature. This paper presents an approach to modeling the dynamics of surface vessels using GPRs. The work outlines the process of generating synthetic data, training the GPR model, and applying it to the vessel maneuvers of path-following and dynamic positioning