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Constructability-aware Physics-Informed Graph Neural Networks for Surrogate-Assisted optimization design of rebar in concrete beams
No full textConstructability-based optimization design of reinforcing bar (rebar) in Reinforced Concrete (RC) structures is crucial for more sustainable practices in the construction industry. To make the process time-efficient, the use of surrogate models is necessary, especially with Graph Neural Networks (GNNs). However, the adoption of GNNs alone can be limited for large RC buildings, due to their characterization as data-hungry models. In this context, Physics-Informed Neural Networks become relevant. Their implementation, however, remains unexploited for this task, where constructability constraints are as preponderant as the physics behind. This paper presents a Constructability-Aware Physics-Informed Graph Neural Network (PIGNN) for surrogate-assisted optimization design of rebar in concrete beams (CPyRO-GraphNet-Beams). Its testing and application for fixed-end supported beams is presented, as a comparison with Plain GNNs. It is demonstrated that CPyRO-GraphNet-Beams outperforms Plain GNNs, highlighting its greater capability to learn constructable features from datasets, enhancing, in turn, more practical and sustainable optimum rebar designs
A unified gas-kinetic framework from Boltzmann to Navier-Stokes scales
No full textNeither molecular kinetics nor continuum fluid dynamics alone is adequate to describe multiscale gas flows across different regimes. Bridging these regimes within a single self-consistent framework has long been a central challenge in fluid mechanics. We propose a unified gas kinetic framework that classifies molecules by their collision histories over an observation timescale. This formulation recovers the Boltzmann and Navier–Stokes equations as limiting cases, providing a transparent connection between kinetic and hydrodynamic descriptions. Beyond practical advantages for multiscale modeling, this framework offers a new perspective on Hilbert’s sixth problem by linking microscopic dynamics to continuum mechanics through a tunable observational scale.</p
Wearable Sensors Fabricated by 3D-Printed Composite Hydrogel with 2D Fillers
No full textFlexible sensors demonstrate exceptional adaptability across human-computer interaction, health monitoring, and robotic systems. However, sensing materials suffer from inadequate conformation capability and microstructural inaccuracies, resulting in function deficiencies. This review examines composite hydrogel formulations that incorporate conductive nanofillers, with particular emphasis on 2D nanomaterials, whose functional tunability enables precise regulation of electrical and interfacial properties. The strategic integration of microstructures further improves sensor sensitivity, durability, and environmental adaptability. We also examine implementation of flexible sensors based on 3D-printed hydrogel in emerging applications including pH monitoring, glucose detection, and food safety assessment. We suggest that future development prioritize elucidating sensing mechanisms, achieving multifunctional integration, advancing material engineering, and refining precision manufacturing. Particularly promising research directions include developing intelligent tactile feedback systems for humanoid robots and creating capsule robot-integrated platforms for gastrointestinal disease monitoring.</p
DOO: Integrated Multi-Omics Resources for Deep Ocean Organisms
No full textThe deep ocean is one of Earth’s most vast and least explored frontiers, characterised by extreme conditions such as high pressure, limited light and nutrient scarcity. These environments pose unparalleled challenges to life, making them invaluable for studying genetic and molecular adaptations to extreme conditions. Emerging omics resources have recently provided significant insights into the advanced understanding of deep ocean ecosystems and evolution. However, a centralised resource for deep ocean multi-omics data remains lacking. To bridge this gap, the Deep Ocean Omics (DOO, https://DeepOceanOmics.org) database, a multi-omics atlas for deep ocean organisms, is presented. DOO integrates diverse omics resources from 68 species across seven phyla and 16 classes, encompassing 72 genomes, 950 bulk transcriptomes, 15 single-cell transcriptomes and 1112 metagenomes, alongside functional support toolkits for functional and comparative analysis. DOO provides a systematic view of genomic information, including genome assembly, phylogeny, gene annotation, BUSCO genes, transcription factors/ubiquitin family, gene cluster, symbiont and mitochondrial genomes and fossil records. Moreover, DOO offers co-expression networks with expression views across different tissues and developmental stages and micro- and macrosynteny analyses to elucidate the pan-evolutionary features of genome structure. As the first comprehensive multi-omics resource dedicated to deep ocean organisms, DOO serves as a pivotal platform for uncovering multi-omics underpinnings of deep ocean organisms and offering insights into the understanding of deep ocean biodiversity, evolution and genetic adaptation under extreme conditions
Chemical design of monolayer altermagnets
No full textThe crystal-symmetry-paired spin-momentum locking (CSML), arising from the intrinsic crystal symmetry that connects different magnetic sublattices in altermagnets, enables many exotic spintronics properties, such as unconventional piezomagnetism and non-collinear spin currents. However, the shortage of monolayer altermagnets restricts further exploration of dimensionally confined phenomena and applications of nanostructured devices. Here, we propose general chemical design principles inspired by sublattice symmetry of the layered altermagnet V2(Se,Te)2O through symmetry-preserving structural modification and valence-adaptive chemical substitutions. In total, we construct 2600 candidates across four structural frameworks, M2A2B1,0 and their Janus derivatives. High-throughput calculations identify 612 potential altermagnets with Néel-ordered ground states, among which 79 exhibit CSML Dirac cones that enable spin-polarized ultra-fast transport. These materials also feature different ground-state magnetic orderings and demonstrate diverse electronic behaviors, ranging from semiconductors and metals to half-metals and Dirac semimetals. This work not only reveals abundant monolayer altermagnets, but also establishes a rational principle for their design, opening the gates to the exploration of confined magnetism and spintronics in atomically thin systems.</p
Optimizing Sustainable Mobility and Energy Systems: Infrastructure Planning and Policy Design
No full textOn the Viability of Mobility-as-a-Service Integration: Equilibrium Analyses and Empirical Evidence
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