Constructing reduced-order models (ROMs) capable of efficiently predicting the evolution of parameter-dependent high-dimensional dynamical systems is crucial in many applications in engineering and applied sciences. A popular class of projection-based ROMs projects the high-dimensional full-order model (FOM) dynamics onto a low-dimensional manifold. These projection-based ROMs approaches often rely on classical model reduction techniques such as proper orthogonal decomposition (POD) or, more recently, on neural network architectures such as autoencoders (AEs). In the case that the ROM is constructed by the POD, one has approximation guaranteed based on the singular values of the problem at hand. However, POD-based techniques can suffer from slow decay of the singular values in transport- and advection-dominated problems. In contrast to that, AEs allow for better reduction capabilities than the POD, often with the first few modes, but at the price of theoretical considerations. In addition, it is often observed, that AEs exhibits a plateau of the projection error with the increment of the dimension of the trial manifold. In this work, we propose a deep invertible AE architecture, named inv-AE, that computationally improves upon the stagnation of the reconstruction error typical of traditional AE architectures, e.g., convolutional, and the reconstructions quality. Inv-AE is composed of several invertible neural network layers that allows for gradually recovering more information about the FOM solutions the more we increase the dimension of the reduced manifold. Through the application of inv-AE to a parametric 1-dimensional Burgers’ equation, a parametric 2-dimensional fluid flow around an obstacle with variable geometry, and a parametric 3-dimensional Korteweg–de Vries, we show that (i) inv-AE mitigates the issue of the characteristic plateau of (convolutional and fully connected) AEs and (ii) inv-AE can be combined with popular autoencoder-based ROM approaches, e.g., DL-ROM and POD-DL-ROM, to improve their accuracy.
Hydrogel-based plant bioelectronics are emerging as promising platforms for real-time monitoring and modulation of plant physiology, stress responses, environmental interactions, and growth. Compared with rigid electrodes and conventional polymer films, hydrogels provide a soft, hydrated, conductive, and tunable interface that reduces mechanical mismatch with growing plant tissues while enabling electrochemical, electrophysiological, optical, and multimodal sensing. This review examines recent advances in hydrogel materials for plant bioelectronics, focusing on how network structure, design requirements, materials strategies including crosslinking chemistry, porosity, swelling, adhesion, conductivity, transparency, gas permeability, and biocompatibility affect plant-device performance. Applications in monitoring plant physiology, hormones, pH, moisture, glucose, and overall plant health are highlighted. Reported hydrogel systems exhibit Young’s moduli from ∼ 1 kPa to several MPa and ionic conductivities of 10−3-10−1 S cm−1. Several plant-interfacing devices sustain strains above 300 %, maintain stable electrical performance over 10,000 loading cycles, and support continuous growth monitoring for up to 14 days. Despite these advances, standardised evaluation under realistic agricultural conditions remains limited. Future research should prioritise standardised testing, biodegradable biomass-derived materials, multimodal sensing integration, and closed-loop bioelectronic systems to advance precision agriculture and bio-regenerative life-support applications.
Digital planning for urban systems can benefit from integrated frameworks that capture cross-sectoral dependencies, impact assessment, support causal reasoning, and adapt to diverse data sources. We present a methodological approach for Digital Twinning that combines backward-chaining analysis with Directed Acyclic Graphs (DAGs) to identify, decompose, and interlink key performance indicators across three critical planning domains: drinking water supply, urban heat stress, and housing provision. Building on a comprehensive indicator review, we selected key targets and traced each to its fundamental components using the DPSIR framework (Driving Forces, Pressures, States, Impacts, Responses). We quantified relational dependencies and network centrality metrics to construct sector-specific DAGs, then merged overlapping elements into an integrated cross-sector model, revealing critical causal pathways. This enables scenario testing across diverse drivers, including climate change, demographic shifts, and credit conditions. Our results demonstrate how infrastructural and natural system interventions propagate through water reliability, thermal comfort, and housing affordability. The framework clarifies data requirements and analytic workflows for Digital Planning tools while offering a scalable template for cross-sectoral impact analysis. By structuring complex interactions into a unified causal schema, planners can use this approach to evaluate trade-offs and derive evidence for decision-making.
The European Union’s transition toward climate neutrality is accelerating demand for lithium-ion batteries to electrify the automotive and household sectors. This makes the European region susceptible to vulnerabilities in material supply, end-of-life management, and recycling capacity. Despite growing research on battery circularity, existing models typically address single countries or isolated end-of-life pathways, lacking a comprehensive EU-wide, multi-pathway perspective. This study develops a system dynamics model to assess the long-term evolution of the vehicle and stationary battery market, end-of-life flows, and material recovery across all EU-27 countries up to 2050, integrating country-level heterogeneity and cathode chemistry transitions. The analysis quantifies how the interplay of extended use, remanufacturing, repurposing, and recycling shapes future battery demand, material dependency, and self-sufficiency. Results show that electric vehicles will dominate battery demand by 2050. Phase-out policies and high repurposing rates lower stationary demand, but increase reliance on imports for electric vehicle batteries. Collection and recycling capacity must expand substantially to meet regulatory targets, although recycling infrastructure remains concentrated in a few countries. Under coordinated expansion efforts, recovery of critical raw materials such as lithium, nickel, and cobalt could partially meet European self-sufficiency targets. The findings of this study highlight that excessive second-life deployment delays material recovery, whereas balanced integration of remanufacturing and recycling improves circularity and resource resilience. To meet European sustainability objectives, it is essential to implement flexible, chemistry-specific, and regionally integrated strategies. These strategies should connect battery design, collection, and recycling infrastructure to balance circular economy goals with resource security.
Noise pollution is an escalating global challenge with profound impacts on health, wellbeing, and ecosystems. To reduce noise, two material categories are of particular interest: absorbers, which reduce sound reflections, and insulators, which exhibit a high sound transmission loss (STL). In this review, the focus is placed on sound insulation and STL as the primary performance metric, while absorption-related mechanisms are considered only insofar as they contribute to reducing transmitted sound. The literature on acoustic insulators that exhibit a high STL over a broad frequency range is fragmented, which impedes direct comparison of existing approaches and cross-fertilization within or between material categories. Therefore, here we review the literature on acoustically insulating dense-solid materials, porous materials, and metamaterials. Traditional materials are reviewed first, including solid walls, sandwich panels, and perforated plates. Subsequently, porous materials including foams, fibrous materials, and aerogels are covered. Finally, metamaterials of the membrane- and labyrinthine-type are reviewed. By comparing mechanisms such as mass-law reflection, viscous and thermal dissipation, and local resonances, we identify critical trends, limitations, and hybrid strategies that enhance STL performance. Particular attention is given to how shape, density, dimensions, and hierarchical design influence broadband insulation. This review also integrates insights across categories, offering a comparative framework and highlighting opportunities in hybrid and adaptive designs. This perspective serves as both a reference guide for material selection and a roadmap for interdisciplinary innovation toward lighter, tunable, and sustainable solutions for future noise control.
