Physical Measurement Laboratory

Accurately estimating urban floodwater depth is a critical step in enhancing urban resilience and strengthening disaster prevention and mitigation capabilities. Traditional methods relying on hydrological monitoring stations and numerical simulations suffer from limitations such as sparse spatial coverage, insufficient validation data, limited accuracy, and delayed fast performance. In contrast, social media data—characterized by its vast volume and fast availability, can effectively compensate for these shortcomings. When processed using artificial intelligence (AI) algorithms, such data can significantly improve credibility, disaster perception speed, and water depth estimation accuracy. To address these challenges, this paper proposes a robust and widely applicable method for rapid urban flood depth perception. The approach integrates AI technology and social media data to construct an AI framework capable of perceiving urban physical parameters through multimodal big data fusion without costly model training. By leveraging the near real-time and widespread nature of social media, an automated web crawler collects flood images and their textual descriptions (including reference objects), eliminating the need for additional hardware investments. The framework uses predefined prompts and pre-trained models to automatically perform relevance verification, duplicate filtering, object detection, and feature extraction, requiring no manual data annotation or model training. With only a minimal amount of water depth annotated data and compressed cross-modal feature vectors as training input, a lightweight Multilayer Perceptron (MLP) achieves high-precision depth estimation based on reference objects. This method avoids the need for large-scale model fine-tuning, allowing rapid training even on devices without GPUs. Experiments demonstrate that the proposed method reduces the Mean Square Error (MSE) by over 80%, processes each image in less than 0.5 s (more than 20 times faster than existing large-model approaches), and exhibits strong robustness to changes in perspective and image quality. The solution is fully compatible with existing infrastructure such as surveillance cameras, offering an efficient and reliable approach for fast flood monitoring in urban hydrology and water engineering applications.

Abstract Heterogeneity in component morphology and distribution, inherent in modern electrochemical devices, frequently limits device performance and durability. However, accurately characterizing heterogeneity is challenging as it requires high‐contrast detection of evolving multi‐material components and associated interfaces, and this often bottlenecks rational design. In this study, new insights into spatio‐operational heterogeneity are quantitatively revealed within multi‐component electrochemical systems using simultaneous neutron and X‐ray tomography (NeXT). In operando fuel cells, this technique uniquely offers independent yet simultaneous and correlated characterization of material distribution and morphology. This enables accurate contextualization of liquid water within all key component interfaces in sufficient detail to resolve previously unidentified 4D heterogeneity. First, 4D heterogeneity in membrane thickness and water content is found to depend strongly upon location and operating conditions, with membrane thickness variations up to 80 µm and membrane water content variation from dry to hydrated at 21 . Second, a direct experimental link is established between anisotropic humidification and local anisotropic swelling of the membrane. The observations lend unique insights into degradation mechanisms of the membrane and have notable implications on the practical durability of fuel cells. The proposed methodology is highly relevant to advancing multi‐material electrochemical devices (with evidence of applicability to batteries provided).

Owing to its unique mechanical properties, chemical resistance,and ion conductivity, Nafion is one of the most widely used polymerelectrolytes. In hydrogen fuel cells, it constitutes both the macroscopicmembrane separating the anode and the cathode, and as a thin film,Nafion appears as a binder in the catalyst layer where conductiveionic pathways must intimately interface with platinum catalyst particles,electrically conductive carbon particles, and porous surfaces thatfacilitate the transport of gases. Residing at the intersection ofthis diverse range of materials, the ionomer’s interfacialstructure influences interfacial impedance and thus device performance.This interface structure has been widely investigated on model surfaceswith neutron reflectometry and other techniques, resulting in thediscovery of a multilamellar structure at the interface with hydrophilicmaterials, or a single water-rich layer at the interface with, e.g.,metals, favoring tangential vs perpendicular ion transport, respectively.Here we demonstrate that self-assembled monolayers, SAMs, which cancoat various surfaces, can control whether single or multiple lamellaeoccur. These interfacial structures can be further modified throughacid–base interactions by protonating the terminal amine groupof a SAM at low pH. This establishes a methodology to control theinterfacial ionic transport pathways in Nafion and determine the interfacialimpedance.

Abstract Breast cancer screening is essential for reducing mortality, yet current modalities face significant barriers, including high costs, limited accessibly, and reliance on ionizing radiation, which leads many women to forego regular screenings. Magnetic resonance imaging (MRI) offers a radiation-free alternative, but its adoption for screening is constrained by cost, availability, and the need for IV contrast administration. In this study, we demonstrate the feasibility of ultra-low field (ULF) unilateral breast MRI for screening applications. ULF MRI was performed on 11 healthy women in a prone position. Three breast radiologists could reliably delineate breast outlines and distinguish fibroglandular tissue (FGT) from adipose tissue. Tissue patterns (fatty, scattered, heterogeneous, and extreme FGT) were consistently identified. In two patients with prior breast cancer, ULF MRI effectively eliminated magnetic susceptibility artifacts from surgical biopsy clips and in one of these patients revealed post-surgical changes following lumpectomy. Additionally, a benign mass was detected in another patient. These findings highlight ULF breast MRI as a potential low-cost, accessible, and contrast-free alternative for breast cancer screening, with the promise of expanding early detection to underserved populations globally.

The connection state of waveguides (WGs) is one of the major error factors in RF measurements. The stability of WG connections during operation strongly depends on the operator's skill. However, WG connections in the terahertz band up are particularly challenging, making it difficult to perform proper connections. This paper proposes a detection technique for the WG connection state in the frequency range up to 1.1 THz. The proposed technique enables stable measurement operation by integrating the algorithm into a terahertz measurement system. Rectangular WGs and RF probes with onwafer measurement systems were used for the demonstrations. The proposed method utilizes the local outlier factor method and linear least-mean-square learning. This technique can detect the connection state of the WG even without requiring training data from the device under test (DUT) itself.