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DOE OSTI2021

Recommended methods for conducting human factors experiments on the subjective evaluation of colour rendition

This article explores the best practices for conducting psychophysical experiments that investigate how colour rendition influences the perception of architectural environments. Here, we offer guidance that covers all stages of research from preliminary development to publication, focusing especially on experiments that investigate qualities such as perceived naturalness, vividness, preference or acceptability in response to changes in the spectral power distribution of light sources. This article is intended to be a consolidated guide for researchers and reviewers of this type of research. Key recommendations include: (1) New work should be motivated by clearly expressed research questions and, when possible, explicit hypotheses that build on the existing body of knowledge, (2) visual stimuli comprising spectral power distributions and visual targets should be deliberately engineered to probe the research questions, (3) experiments should be designed to lessen potential biases, (4) reporting of experimental conditions and statistical analyses should be thorough and (5) Results should be contextual, resisting overgeneralization that cannot be supported by the data. Our motivation is to encourage high-quality research that is credible and discourage poor quality research that slows scientific progress and misuses resources.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2017

ProteoSign: an end-user online differential proteomics statistical analysis platform

Profiling of proteome dynamics is crucial for understanding cellular behavior in response to intrinsic and extrinsic stimuli and maintenance of homeostasis. Over the last 20 years, mass spectrometry (MS) has emerged as the most powerful tool for large-scale identification and characterization of proteins. Bottom-up proteomics, the most common MS-based proteomics approach, has always been challenging in terms of data management, processing, analysis and visualization, with modern instruments capable of producing several gigabytes of data out of a single experiment. Here, we present ProteoSign, a freely available web application, dedicated in allowing users to perform proteomics differential expression/abundance analysis in a userfriendly and self-explanatory way. Although several non-commercial standalone tools have been developed for post-quantification statistical analysis of proteomics data, most of them are not end-user appealing as they often require very stringent installation of programming environments, third-party software packages and sometimes further scripting or computer programming. To avoid this bottleneck, we have developed a user-friendly software platform accessible via a web interface in order to enable proteomics laboratories and core facilities to statistically analyse quantitative proteomics data sets in a resource-efficient manner. ProteoSign is available at http://bioinformatics.med.uoc.gr/ProteoSign and the source code at https://github.com/yorgodillo/ ProteoSign.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

The Missing Satellite Problem outside of the Local Group. II. Statistical Properties of Satellites of Milky Way–like Galaxies

We present a new observation of satellite galaxies around seven Milky Way (MW)–like galaxies located outside of the Local Group (LG) using Subaru/Hyper Suprime-Cam imaging data to statistically address the missing satellite problem. We select satellite galaxy candidates using magnitude, surface brightness, Sérsic index, axial ratio, FWHM, and surface brightness fluctuation cuts, followed by visual screening of false positives such as optical ghosts of bright stars. We identify 51 secure dwarf satellite galaxies within the virial radius of nine host galaxies, two of which are drawn from the pilot observation presented in Paper I. We find that the average luminosity function of the satellite galaxies is consistent with that of the MW satellites, although the luminosity function of each host galaxy varies significantly. We observe an indication that more massive hosts tend to have a larger number of satellites. Physical properties of the satellites such as the size–luminosity relation are also consistent with the MW satellites. However, the spatial distribution is different; we find that the satellite galaxies outside of the LG show no sign of concentration or alignment, while that of the MW satellites is more concentrated around the host and exhibits a significant alignment. As we focus on relatively massive satellites with M V < –10, we do not expect that the observational incompleteness can be responsible here. This trend might represent a peculiarity of the MW satellites, and further work is needed to understand its origin.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Nonlocal Means Denoising of Self-Gated and k-Space Sorted 4-Dimensional Magnetic Resonance Imaging Using Block-Matching and 3-Dimensional Filtering: Implications for Pancreatic Tumor Registration and Segmentation

Purpose: To denoise self-gated k-space sorted 4-dimensional magnetic resonance imaging (SG-KS-4D-MRI) by applying a nonlocal means denoising filter, block-matching and 3-dimensional filtering (BM3D), to test its impact on the accuracy of 4D image deformable registration and automated tumor segmentation for pancreatic cancer patients. Methods and Materials: Nine patients with pancreatic cancer and abdominal SG-KS-4D-MRI were included in the study. Block-matching and 3D filtering was adapted to search in the axial slices/frames adjacent to the reference image patch in the spatial and temporal domains. The patches with high similarity to the reference patch were used to collectively denoise the 4D-MRI image. The pancreas tumor was manually contoured on the first end-of-exhalation phase for both the raw and the denoised 4D-MRI. B-spline deformable registration was applied to the subsequent phases for contour propagation. The consistency of tumor volume defined by the standard deviation of gross tumor volumes from 10 breathing phases (σ-GTV), tumor motion trajectories in 3 cardinal motion planes, 4D-MRI imaging noise, and image contrast-to-noise ratio were compared between the raw and denoised groups. Results: Block-matching and 3D filtering visually and quantitatively reduced image noise by 52% and improved image contrast-to-noise ratio by 56%, without compromising soft tissue edge definitions. Automatic tumor segmentation is statistically more consistent on the denoised 4D-MRI (σ-GTV = 0.6 cm{sup 3}) than on the raw 4D-MRI (σ-GTV = 0.8 cm{sup 3}). Tumor end-of-exhalation location is also more reproducible on the denoised 4D-MRI than on the raw 4D-MRI in all 3 cardinal motion planes. Conclusions: Block-matching and 3D filtering can significantly reduce random image noise while maintaining structural features in the SG-KS-4D-MRI datasets. In this study of pancreatic tumor segmentation, automatic segmentation of GTV in the registered image sets is shown to be more consistent on the denoised 4D-MRI than on the raw 4D-MRI.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

On the accuracy of Hipparcos using binary stars as a calibration tool

Stellar binary systems, specifically those that present the most accurate available orbital elements, are a reliable tool to test the accuracy of astrometric observations. We selected all 35 binaries with these characteristics. Our objective is to provide standard uncertainties for the positions and parallaxes measured by Hipparcos relative to this trustworthy set, as well as to check supposed correlations between several parameters (measurement residuals, positions, magnitudes, and parallaxes). In addition, using the high-confidence subset of visual–spectroscopic binaries, we implemented a validation test of the Hipparcos trigonometric parallaxes of binary systems that allowed the evaluation of their reliability. Standard and non-standard statistical analysis techniques were applied in order to achieve well-founded conclusions. In particular, errors-in-variables models such as the total least-squares method were used to validate Hipparcos parallaxes by comparison with those obtained directly from the orbital elements. Previously, we executed Thompson's τ technique in order to detect suspected outliers in the data. Furthermore, several statistical hypothesis tests were carried out to verify if our results were statistically significant. A statistically significant trend indicating larger Hipparcos angular separations with respect to the reference values in 5.2 ± 1.4 mas was found at the 10{sup −8} significance level. Uncertainties in the polar coordinates θ and ρ of 1.°8 and 6.3 mas, respectively, were estimated for the Hipparcos observations of binary systems. We also verified that the parallaxes of binary systems measured in this mission are absolutely compatible with the set of orbital parallaxes obtained from the most accurate orbits at least at the 95% confidence level. This methodology allows us to better estimate the accuracy of Hipparcos observations of binary systems. Indeed, further application to the data collected by Gaia should yield a standard procedure to compare both data sets.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

The one-dimensional power spectrum from the SDSS DR14 Lyα forests

In this paper, we present a measurement of the 1D Lyα forest flux power spectrum, using the complete Baryon Oscillation Spectroscopic Survey (BOSS) and first extended-BOSS (eBOSS) quasars at z qso > 2.1, corresponding to the fourteenth data release (DR14) of the Sloan Digital Sky Survey (SDSS). Our results cover thirteen bins in redshift from z Lyα = 2.2 to 4.6, and scales up to k = 0.02 (km/s) -1 . From a parent sample of 180,413 visually inspected spectra, we selected the 43,751 quasars with the best quality; this data set improves the previous result from the ninth data release (DR9), both in statistical precision (achieving a reduction by a factor of two) and in redshift coverage. We also present a thorough investigation of identified sources of systematic uncertainties that affect the measurement. Finally, the resulting 1D power spectrum of this work is in excellent agreement with the one from the BOSS DR9 data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Atomic-scale interface engineering of Majorana edge modes in a 2D magnet-superconductor hybrid system

Topological superconductors are predicted to harbor exotic boundary states—Majorana zero-energy modes—whose non-Abelian braiding statistics present a new paradigm for the realization of topological quantum computing. Using low-temperature scanning tunneling spectroscopy, here, we report on the direct real-space visualization of chiral Majorana edge states in a monolayer topological superconductor, a prototypical magnet-superconductor hybrid system composed of nanoscale Fe islands of monoatomic height on a Re(0001)-O(2 × 1) surface. In particular, we demonstrate that interface engineering by an atomically thin oxide layer is crucial for driving the hybrid system into a topologically nontrivial state as confirmed by theoretical calculations of the topological invariant, the Chern number.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2019

Function Preserving Projection

With function preserving projection (FPP), we aim to address high-dimensional data exploration challenges common in many scientific applications. Instead of preserving the global or local geometric structure of a dataset like traditional dimensionality reduction methods (e.g., principal component analysis, t-stochastic neighborhood embedding), the proposed approach finds a linear projection that reveal interpretable (yet potentially nonlinear) pattern of the properties of interests defined on a highdimensional domain (e.g., simulation yield defined on the input parameter space). The key insight driving FPP is the fact that while non-linear embeddings are difficult to interpret, humans are highly skilled in understanding complex visual patterns. FPP lets users select the complexity of a pattern they consider interpretable, allows to find a joint projection that explains multiple functions, and provides a confidence statistic (akin to a p-value) indicating how salient the resulting pattern is. We demonstrate the efficacy of the proposed method on several synthetic and scientific datasets for both continuous (regression) and discrete (classification) problems of interests and illustrate the scalability of FPP by finding an optimal projection for close to 40 gigabytes in around 10 minutes.

Liu, SHUSEN↗

DOE OSTI2025

Imaging Bragg Edge Analysis TooLs for Engineering Structures (iBeatles)

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) provides pulsed neutrons with energies varying from epithermal to cold. In preparation for VENUS, the neutron imaging beamline to be located at beam port 10, we have performed a series of experiments focused on wavelength-dependent radiography and computed tomography for a broad range of applications, from materials science to biological tissues.One of the time-of-flight (TOF) techniques that is of interest to the scientific community is the 2-dimensional mapping of phases and average crystalline plane orientation in samples both ex-situ and during applied stresses such as tensile loading and heating. This technique is known as Bragg edgeimaging and relies on the identification of changes of transmission values, fitting of the edge to measure its displacement, and thus identify the shift in lattice parameter due to stresses. One of the challenges of TOF imaging measurements is the amount of data and the inability to observe Bragg edge shifts in real time during an experiment. Thus, we have been focusing on creating a Python-based interface that allows fast data processing and instantaneous mapping and fitting of the Bragg edges, and their evolution through time. Python libraries and Jupyter notebooks have been implemented to facilitate decision making during an experiment. The advantage of the notebooks is the possibility to guide an experiment as they can quickly process and display Bragg edge data. These notebooks can be used independently, or can be combined in a Python Graphical User Interface (GUI) tool called iBeatles. This interface permits visualization and fitting of the Bragg edges, and ultimately back-projects the fitting results onto the radiographs to display a strain map. Assuming data collection has sufficient statistics, the strain mapping analysis can be performed on a pixel-by-pixel basis. This development is a step forward toward a better user experience at the future VENUS beamline in terms of live feedback and productivity. Analysis that used to take days of switching between different applications can now be done in minutes within the

Bilheux, JeanChristophe [Oak Ridge National Labora↗

DOE OSTI2017

Status of a Deep Learning Based Measurement of the Inclusive Muon Neutrino Charged-current Cross Section in the NOvA Near Detector

NOvA is a long-baseline neutrino oscillation experiment. It uses the NuMI beam from Fermilab and two sampling calorimeter detectors placed off-axis from the beam. The 293 ton Near Detector measures the unoscillated neutrino energy spectrum, which can be used to predict the neutrino energy spectrum observed at the 14 kton Far Detector. The Near Detector also provides an excellent opportunity to measure neutrino interaction cross sections with high statistics, which will benefit current and future long-baseline neutrino oscillation experiments. This analysis implements new algorithms to identify $\nu_{\mu}$ charge-current events by using visual deep learning tools such as convolutional neural networks. We present the status of a measurement of the inclusive $\nu_{\mu}$ CC cross section in the NOvA Near Detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗

DOE OSTI2021

ARMADA. I. Triple Companions Detected in B-type Binaries α Del and ν Gem

Ground-based optical long-baseline interferometry has the power to measure the orbits of close binary systems at ∼10 μas precision. This precision makes it possible to detect “wobbles” in the binary motion due to the gravitational pull from additional short-period companions. We started the ARrangement for Micro-Arcsecond Differential Astrometry (ARMADA) survey with the Michigan Infra-Red Combiner (MIRC)/MIRC-X instrument at the Center for High Angular Resoloution Astronomy (CHARA) array for the purpose of detecting giant planets and stellar companions orbiting individual stars in binary systems. We describe our observations for the survey, and introduce the wavelength calibration scheme that delivers precision at the tens of microarcseconds level for <02 binaries. We test our instrument performance on a known triple system, κ Peg, and show that our survey is delivering a factor of 10 better precision than previous similar surveys. We present astrometric detections of tertiary components to two B-type binaries: a 30 day companion to α Del, and a 50 day companion to ν Gem. We also collected radial velocity data for α Del with the Tennessee State University Automated Spectroscopic Telescope at Fairborn Observatory. We are able to measure the orbits and masses of all three components in these systems. We find that the previously published radial velocity orbit for the inner pair of ν Gem is not consistent with our visual orbit. The precision achieved for these orbits suggests that our ARMADA survey will be successful at discovering new compact triple systems to A/B-type binary systems, leading to better statistics of hierarchical system architectures and formation history.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Arms Down Cone Beam CT Hepatic Angiography Performance Assessment: Vascular Imaging Quality and Imaging Artifacts

IntroductionThe practice of positioning patients’ arms above the head during catheter-injected hepatic arterial phase cone beam CT (A-CBCT) imaging has been inherited from standard CT imaging due to image quality concerns, but interrupts workflow and extends procedure time. We sought to assess A-CBCT image quality and artifacts with arms extended above the head versus down by the side.MethodsWe performed an IRB approved retrospective evaluation of reformatted and 3D-volume rendered images from 91 consecutive A-CBCTs (43 arms up, 48 arms down) acquired during hepatic tumor arterial embolization procedures. Two interventional radiologists reviewed all A-CBCT imaging and assigned vessel visualization scores (VVS) from 1 to 5, ranging from non-diagnostic to optimal visualization. Streak artifacts across axial images were rated from 1 to 3 based on resulting image quality (none to significant). Presence of respiratory or cardiac motion during acquisition, body mass index and radiation dose area product (DAP) were also recorded and analyzed. Univariate and multivariate analyses were used to assess the impact of arm position on VVS and imaging artifacts.ResultsVVS were not significantly associated with arm position during A-CBCT imaging. One reader reported more streak artifacts across axial images in the arms down group (p = 0.005). DAP was not statistically different between the groups (23.9 Gy cm{sup 2} [6.1–73.4] arms up, 26.1 Gy cm{sup 2} [4.2–102.6] arms down, p = 0.54).ConclusionA-CBCT angiography performed with the arms above the head is not superior for clinically relevant hepatic vascular visualization compared to imaging performed with the arms by the patient’s side.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

Anomalous nanoparticle surface diffusion in LCTEM is revealed by deep learning-assisted analysis

Significance Liquid cell transmission electron microscopy (LCTEM) is an emerging technique, which enables nanoscale visualization and tracking of single nanoparticles near interfaces with unprecedented spatial resolution. Here, we studied the diffusion of nanoparticles in LCTEM experiments using techniques powered by deep neural networks and statistical tests. We observed two underlying regimes of diffusive behavior which are governed by the interaction of the electron beam, the nanoparticle, the nearby substrate, and the liquid environment. This understanding forms the foundation to use LCTEM for single-nanoparticle tracking for a broad range of nanoparticles, interfaces, and liquids.

Jamali, Vida↗

DOE OSTI2021

VAINE: Visualization and AI for Natural Experiments

Natural experiments are observational studies where the assignment of treatment conditions to different populations occur by chance ``in the wild''. Researchers from fields such as economics, healthcare, and the social sciences leverage natural experiments to conduct hypothesis testing and causal effect estimation for treatment and outcome variables that would otherwise be costly, infeasible, or unethical. In this paper, we introduce VAINE (Visualization and AI for Natural Experiments), a visual analytics tool for identifying and understanding natural experiments from observational data. We then demonstrate how VAINE can be used to validate causal relationships, estimate average treatment effects, and identify statistical phenomena such as Simpson’s paradox through two use cases.

Guo, Grace↗

DOE OSTI2025

Impacts of PV Module Connector Failures on Cost and Performance of Utility Scale Photovoltaic Systems

The reliability, cost and performance of electrical connectors are a concern in all types of electrical systems, and demands on connectors used on photovoltaic (PV) systems include that connectors maintain electrical conductivity and physical strength, endure ultraviolet sunlight and high ambient temperature, and resist moisture and chemical intrusion over a very long (>25 year) performance period. Connector failures increase operation and maintenance (O&M) costs and reduce plant production, but connector failure can also cause safety and liability problems, which are of greater concern. This work results from a three-year collaboration between Sandia National Laboratories (SNL), the Electric Power Research Institute (EPRI), and the National Renewable Energy Laboratory (NREL) and funded by the U.S. Department of Energy (DOE) Solar Energy Technology Office (SETO) under Agreements #39035 and #38531 "Connector Reliability Across the US Solar Sector." a multi-pronged investigation of PV connector health across the US (see https://energy.sandia.gov/pvconnectors/). This report presents derivation of a Techno-Economic Analysis (TEA) that models failure modes and frequencies (how often failure occurs), estimates O&M costs and lost production associated with connector failures, and then calculates the effect that PV module connectors can have on Levelized Cost of Energy (LCOE). The model is informed with initial data from quantitative assessment of failure rates, root causes and mechanisms, in-situ diagnostics and data collection, lab-based forensics, and interviews with PV connector manufacturers and plant operators. SNL conducted site inspections at multiple utility-scale sites in different climates and subjected field samples of new, used, and degraded connectors to visual and electrical characterization. EPRI conducted metallurgical analysis of the pin and sleeve conductors to study failure-induced morphological and compositional changes. There is in general a shortage of statistically valid data, but data from PVROM database maintained by SNL was sufficient to ascertain failure rates and lost production as well as provide qualitative insight in its curated maintenance records. This report details the structure of the mathematical model but the sources of data to inform the model will continue to evolve. Analysis of a 100 MW PV plant is provided as an example of the use of the model, with results indicating that connectors are responsible for Annualized O&M Costs of $\$$71,933/year; Annualized Unit O&M Costs of $\$$0.72/kW/year; that a Reserve Account of $\$$187,220 should be available to fund repairs related to connectors; that connectors add $\$$1,494,004 to the Net Present Value of the O&M Costs (project life); and that O&M related to connectors adds about $\$$0.00088/kWh to the Levelized Cost of Energy. The impact of this model is to provide a tool to make the US solar sector more robust by quantifying and monetizing the reliability risks to utility-scale PV systems posed by poorly installed, mismatched and/or poorly designed and manufactured connectors. The TEA provides a model incorporating failure statistics, O&M cost data, and lost production into a single figure of merit, informing decisions and enabling practitioners to optimize cost and performance trade-offs. Stakeholders include connector manufacturers, system designers and equipment specifiers, standards bodies, installers and O&M providers, investors and insurance underwriters. This report supports continued growth of PV predicated on assurances that properly installed and maintained PV system connectors are safe and reliable. The project team is proposing future work including accelerated testing of connectors and expanding the approach taken here to other PV system components, such as TEA for rapid shut-down devices.

14 SOLAR ENERGY↗

DOE OSTI2017

Scalable Analysis Methods and In Situ Infrastructure for Extreme Scale Knowledge Discovery

High performance computers have for many years been on a trajectory that gives them extraordinary compute power with the addition of more and more compute cores. At the same time, other system parameters such as the amount of memory per core and bandwidth to storage have remained constant or have barely increased. This creates an imbalance in the computer, giving it the ability to compute a lot of data that it cannot reasonably save out due to time and storage constraints. While technologies have been invented to mitigate this problem (burst buffers, etc.), software has been adapting to employ in situ libraries which perform data analysis and visualization on simulation data while it is still resident in memory. This avoids the need to ever have to pay the costs of writing many terabytes of data files. Instead, in situ enables the creation of more concentrated data products such as statistics, plots, and data extracts, which are all far smaller than the full-sized volume data. With the increasing popularity of in situ, multiple in situ infrastructures have been created, each with its own mechanism for integrating with a simulation. To make it easier to instrument a simulation with multiple in situ infrastructures and include custom analysis algorithms, this project created the SENSEI framework.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Accelerated Depth Computation for Surface Boxplots with Deep Learning

Functional depth is a well-known technique used to derive descriptive statistics (e.g., median, quartiles, and outliers) for 1D data. Surface boxplots extend this concept to ensembles of images, helping scientists and users identify representative and outlier images. However, the computational time for surface boxplots increases cubically with the number of ensemble members, making it impractical for integration into visualization tools. In this paper, we propose a deep-learning solution for efficient depth prediction and computation of surface boxplots for time-varying ensemble data. Our deep learning framework accurately predicts member depths in a surface boxplot, achieving average speedups of 6X on a CPU and 15X on a GPU for the 2D Red Sea dataset with 50 ensemble members compared to the traditional depth computation algorithm. Our approach achieves at least a 99% level of rank preservation, with order flipping occurring only at pairs with extremely similar depth values that pose no statistical differences. This local flipping does not significantly impact the overall depth order of the ensemble members.

Han, Mengjiao↗