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We perform a state-of-the-art global study of the cosmological thermal histories of a simple Yukawa model, and find higher perturbative orders to be important for determining both the presence and strength of strong first-order phase transitions. Using high-temperature effective field theory, we calculate the free energy density of the model up to $\mathcal{O}(y^5T^4)$, where $y$ is the Yukawa coupling and $T$ is the temperature. The locations of phase transitions are found using the results of lattice Monte-Carlo simulations, and the strength of first-order transitions are evaluated within perturbation theory, to 3-loop order. This is the first global study of any model at this order. Compared to a vanilla 1-loop analysis, accurate to $\mathcal{O}(y^2 T^4)$, reaching such accuracy enables on average a five-fold reduction in the relative error in the predicted critical temperature $T_\text{c}$, and an additional $\sim50\%$ strong first-order transitions with latent heat $L/T_\text{c}^4 > 0.1$ to be identified in our scan., Comment: 35 pages, 13 figures

Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters. To leverage the benefits of different architectural designs (e.g. CNNs and ViTs), we propose to alternate structurally different types of blocks to generate a new architecture, mimicking how Lego blocks can be assembled together. Using two CNN-based and one SwinViT-based blocks, we investigate three variations to the so-called LegoNet that applies the new concept of block alternation for the segmentation task in medical imaging. We also study a new clinical problem which has not been investigated before, namely the right internal mammary artery (RIMA) and perivascular space segmentation from computed tomography angiography (CTA) which has demonstrated a prognostic value to major cardiovascular outcomes. We compare the model performance against popular CNN and ViT architectures using two large datasets (e.g. achieving 0.749 dice similarity coefficient (DSC) on the larger dataset). We evaluate the performance of the model on three external testing cohorts as well, where an expert clinician made corrections to the model segmented results (DSC>0.90 for the three cohorts). To assess our proposed model for suitability in clinical use, we perform intra- and inter-observer variability analysis. Finally, we investigate a joint self-supervised learning approach to assess its impact on model performance. The code and the pretrained model weights will be available upon acceptance., Comment: 12 pages, 5 figures, 4 tables

A multiphase flowmeter (MPFM) is used in the upstream oil and gas industry for continuous, in-line, real-time, oil-gas-water flow measurement without fluid separation. An MPFM typically consists of phase-fraction (holdup) and velocity (or flow rate) measurements. It is desirable to have homogeneous flow at the measurement location so that the phase-fraction measurement is representative. A horizontal blind-tee pipe-section is often installed to homogenize flow in the downstream vertical Venturi-based flowmeters; however, little information is available on the effect of horizontal blind-tee depth (HBD) on flow homogeneity. In addition, the Venturi vertical entrance length (VEL) leading to the Venturi inlet from the horizontal blind-tee outlet is another design parameter that may potentially affect the downstream phase distribution. The phase-fraction measurement principle requires liquid properties (e.g. water salinity). The local liquid richness makes the horizontal blind-tee an ideal location for measuring liquid properties; however, an excessive HBD may affect the reliability of the measurements of liquid properties, because local vortices may degrade liquid measurement representativeness if the local liquid velocity is too low. This study uses a computational fluid dynamics approach to evaluate the effect of HBD and VEL on multiphase flow measurement, including the Venturi differential-pressure, the Venturi inlet and the throat phase-fraction, and the local liquid-property at the end of a horizontal blind-tee. The computational results are validated with experimental data collected in a multiphase flow facility. Appropriate HBD and VEL are recommended.

Real-time in-line interpretation of liquid properties is important for multiphase flow measurements. Therefore, it would be desirable to have a sensor, such as a microwave sensor, which can continuously measure the salinity of a flow. In addition to salinity measurement, the microwave sensor can also measure water fraction, which is required for a multiphase flowmeter based on single-energy gamma-ray attenuation; however, choosing a suitable probe sensing location for a microwave salinity sensor in a multiphase flowmeter can be challenging, as the sensor needs to be located at near-wall liquid-rich region to accommodate a wide range of flow conditions. Currently, a microwave sensor is installed in the lower area of a horizontal blind-tee inlet spool of a multiphase flowmeter for salinity measurement. Integrating the microwave sensor into the vertically mounted multiphase flowmeter can reduce the flowmeter (carbon) footprint and manufacturing costs and can improve water-to-liquid ratio measurement due to faster local oil-water mixing. The associated challenge is that the sensor needs to be located at near-wall liquid-rich region to accommodate a wide range of flow conditions, including high gas-volume-fraction flows, where the near-wall liquid layer present in the vertical pipe is usually very thin. In this study, computational fluid dynamics modeling is used to evaluate the suitability of four different sensing locations along the vertical cross-section of a Venturi-based multiphase flowmeter based on near-wall liquid-richness. The results show that the Venturi inlet is the most suitable location for microwave sensor measurement, compared to the mid-convergence section, the mid-divergence section, and the Venturi outlet for a range of inlet liquid-volume-fractions. The findings have been validated by experimental microwave sensor measurements in a multiphase flow loop facility.

The microstructure is an essential part of materials, storing the genes of materials and having a decisive influence on materials' physical and chemical properties. The material genetic engineering program aims to establish the relationship between material composition/process, organization, and performance to realize the reverse design of materials, thereby accelerating the research and development of new materials. However, tissue analysis methods of materials science, such as metallographic analysis, XRD analysis, and EBSD analysis, cannot directly establish a complete quantitative relationship between tissue structure and performance. Therefore, this paper proposes a novel data-knowledge-driven organization representation and performance prediction method to obtain a quantitative structure-performance relationship. First, a knowledge graph based on EBSD is constructed to describe the material's mesoscopic microstructure. Then a graph representation learning network based on graph attention is constructed, and the EBSD organizational knowledge graph is input into the network to obtain graph-level feature embedding. Finally, the graph-level feature embedding is input to a graph feature mapping network to obtain the material's mechanical properties. The experimental results show that our method is superior to traditional machine learning and machine vision methods.