Two-dimensional Density Research Articles

Robustness in half-metallicity, thermophysical and structural properties of Co2YAl (Y = Pd, Ag) Heuslers: a first-principles perspective

Heusler materials validates an extensive range of properties characteristically for the prospective high-tech interests. The extensive tunability of these materials through chemical substitutions as well as structural designs sorts the family explicitly fascinating. Self-consistent density functional theory, based on the full-potential linearized augmented plane wave is employed within the WIEN2k simulation code to investigate the structural, magneto-electronic and thermophysical properties of Co2PdAl and Co2AgAl Heusler compounds. The steadiness in P–V plot designates the absence of any structural phase transition from a cubic symmetry structure towards other structural phase. The GGA allows calculating the exchange-correlation energy. The proposed compounds are found stable in a Cu2MnAl prototype phase state. The cohesive and formation energy calculations approve the thermodynamic stability of compounds. The optimized lattice parameters in the stable Fm-3m phase are 5.90 Å for Co2PdAl and 5.97 Å for Co2AgAl respectively. At Fermi level, these alloys exhibit 100% spin polarization and display half-metallicity. The charge shearing among the constituents’ atoms is presented through the two-dimensional (2D) electron density plot with unique color contrast. The low anharmonicity possessed by the material is favored by the thermal parameters and compounds is predicted stable at a wider range of pressure and temperatures.

MCIBox: a toolkit for single-molecule multi-way chromatin interaction visualization and micro-domains identification.

The emerging ligation-free three-dimensional (3D) genome mapping technologies can identify multiplex chromatin interactions with single-molecule precision. These technologies not only offer new insight into high-dimensional chromatin organization and gene regulation, but also introduce new challenges in data visualization and analysis. To overcome these challenges, we developed MCIBox, a toolkit for multi-way chromatin interaction (MCI) analysis, including a visualization tool and a platform for identifying micro-domains with clustered single-molecule chromatin complexes. MCIBox is based on various clustering algorithms integrated with dimensionality reduction methods that can display multiplex chromatin interactions at single-molecule level, allowing users to explore chromatin extrusion patterns and super-enhancers regulation modes in transcription, and to identify single-molecule chromatin complexes that are clustered into micro-domains. Furthermore, MCIBox incorporates a two-dimensional kernel density estimation algorithm to identify micro-domains boundaries automatically. These micro-domains were stratified with distinctive signatures of transcription activity and contained different cell-cycle-associated genes. Taken together, MCIBox represents an invaluable tool for the study of multiple chromatin interactions and inaugurates a previously unappreciated view of 3D genome structure.

An Improved Method for the Inversion of Backscatter Ionograms by Using Neighborhood-Aided and Multistep Fitting

To solve the problem that a parameter search easily falls into a local optimum and the two-dimensional electron density profile construction error is large in the process of backscatter ionogram inversion, an improved method using neighborhood-aided and multistep fitting is proposed. The ionospheric parameter inversion results in the adjacent space are combined and reconstructed by using the neighborhood-aided correction method. The introduction of auxiliary information sources addresses the defects of the conventional genetic algorithm. The local region multistep fitting method is used to describe the local uniformity and global inhomogeneity of the two-dimensional electron density profile by dividing the fitting region. The experimental results show that the proposed method can improve the accuracy of backscatter ionogram inversion and provide reliable support for tracking radio ray trajectories.

Two-dimensional plasma density evolution local to the inversion layer during sawtooth crash events using beam emission spectroscopy.

We present methods for analyzing Beam Emission Spectroscopy (BES) data to obtain the plasma density evolution associated with rapid sawtooth crash events at the DIII-D tokamak. BES allows coverage over a 2D spatial plane, inherently local measurements, with fast time responses, and, therefore, provides a valuable new channel for data during sawtooth events. A method is developed to remove sawtooth-induced edge-light pulses contained in the BES data. The edge light pulses appear to be from the Dα emission produced by edge recycling during sawtooth events, and are large enough that traditional spectroscopic filtering and data analysis techniques are insufficient to deduce physically meaningful quantities. A cross-calibration of 64 BES channels is performed by using a novel method to ensure accurate measurements. For the large-amplitude density oscillations observed, we discuss and use the non-linear relationship between the BES signal δI/I0 and the plasma density variation δne/ne0. The 2D BES images cover an 8 × 20cm2 region around the sawtooth inversion layer and show large-amplitude density oscillations, with additional significant spatial variations across the inversion layer that grows and peaks near the time of the temperature crash. The edge light removal technique and method of converting large-amplitude δI/I0 to δne/ne0 presented here may help analyze other impulsive MHD phenomena in tokamaks.

Automatic Detection of Local Obstacles in a Long Length RE-123 Coated Conductor by Deep Learning Based Image Classification in Reel-to-Reel Magnetic Microscopy

We have succeeded in detecting local obstacles automatically in a 200-m-long RE-123 coated conductor (CC) by introducing deep-learning based image recognition in reel-to-reel scanning Hall probe microscopy (RTR-SHPM). Longitudinal critical current ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_c</i> ) homogeneity in CCs is one of the most important requirements for practical applications. Usually, such properties as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_c</i> variation as a function of longitudinal coordinate is characterized by magnetization measurements adopting Hall probe array as a de facto standard characterization method for ensuring uniformity in long CCs. In this measurement, local <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_c</i> drop indicates the existence of current blocking obstacles. The group of authors also developed a magnetic microscopy applicable to reel-to-reel continuous measurements, RTR-SHPM, which makes it possible to visualize two-dimensional critical current density ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J_c</i> ) in the tape plane because of its high-resolution imaging along the tape width. As a result, more elaborate defect detection has been enabled. However, in the conventional technique, the observation depends on the human eye in order for characterizing detailed features of the obstacles such as shape and size especially in case with a small <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> drop, therefore, there was a limit to analyze the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J_c</i> mapping with such detailed information extending to thousands of images in the long tape of several 100 of meters. In this study, the image classification based on the deep learning method was introduced in our magnetic microscopy. The analytical model classifies the input image into the defect position and the normal position, respectively, together with a heat map and a score of confidence in the recognition. As a result, we have succeeded in detecting obstacles automatically from more than 4,000 of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J_c</i> images with a high accuracy of 98.5%. Furthermore, we revealed the existence of the obstacles which were not distinguishable by the local <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_c</i> criterion. This method allows us to clarify the origin of the instability of long CC wire and will have a strong impact as an evaluation technique for dramatically improving the reliability of the CCs.

Even allocation of benefits stabilizes microbial community engaged in metabolic division of labor.

Microbial communities execute metabolic pathways to drive global nutrient cycles. Within a community, functionally specialized strains can perform different yet complementary steps within a linear pathway, a phenomenon termed metabolic division of labor (MDOL). However, little is known about how such metabolic behaviors shape microbial communities. Here, we derive a theoretical framework to define the assembly of a community that degrades an organic compound through MDOL. The framework indicates that to ensure community stability, the strains performing the initial steps should hold a growth advantage (m) over the "private benefit" (n) of the strain performing the last step. The steady-state frequency of the last strain is then determined by the quotient of n and m. Our experiments show that the framework accurately predicts the assembly of our synthetic consortia that degrade naphthalene through MDOL. Our results provide insights for designing and managing stable microbial systems for metabolic pathway optimization.

Compilation of Load Spectrum of PHEV Transmission Assembly and Its Simulation Application

This paper presents a method for compiling the load spectrum of the transmission assembly of plug-in hybrid electric vehicles (PHEVs). Based on the analysis of the control strategy of the test vehicle, the power flow transmission route in the transmission assembly is different under different operation modes, so it is necessary to divide different load spectrum blocks according to the operation mode. Based on the big data survey of China’s national standard, it is determined that the typical working conditions are urban road working conditions, high-speed road working conditions, provincial road working conditions and poor road conditions. The mileage proportion of the various working conditions is 55:30:10:5, and the mileage of one cycle is 300 km. A total of three cycles are collected. After data processing and time-domain verification, based on the principle of maximum damage, the cycle with the largest pseudo damage is selected as the sample load data for load spectrum extrapolation. The rain flow counting method is used to count the sample load, and a two-dimensional kernel density estimation mathematical model with adaptive bandwidth is established to estimate the probability density function of the data. The extrapolated rain flow matrix is obtained through Monte Carlo simulation. The load spectrum of the two-dimensional rain flow matrix is transformed into a one-dimensional eight-stage program load spectrum by using a variable mean method, Goodman equation and equal damage principle theory. Finally, the fatigue life of the transmission assembly is simulated and calculated under the environment of Romax Designer simulation software. The two-dimensional kernel density estimation model with adaptive bandwidth is used to fit and extrapolate the load rain flow matrix of each hybrid mode of the PHEV, which solves the problem wherein the shape of the rain flow matrix of each hybrid mode of the hybrid electric vehicle is complex and difficult to fit. Finally, taking the after-sales maintenance data of this model from 2020 to the present as auxiliary proof, the failure components and the failure mileage life of the simulation test results are consistent with the results used by the actual users. This shows that the kernel density estimation model proposed in this paper can well fit the rain flow matrix of the PHEV load spectrum. The extrapolated load spectrum based on this model has high accuracy and authenticity. The method of compiling the load spectrum of the transmission assembly of a hybrid electric vehicle in this paper is effective.

Quantitative flow diagnostics of shock trains by rainbow schlieren deflectometry

AbstractA new measurement technique to reconstruct the density field of the shock-wave/boundary-layer interaction (SWBLI) in a confined duct is proposed. With this technique, it is possible to quantitatively capture in detail the structures of the density field both in the regions of the shock-systems in the central core and boundary-layer flows near the duct wall concurrently. The novel feature of the proposed technique is to make use of the schlieren images with the rainbow filters of the vertical and horizontal cutoff settings and then to reconstruct the two-dimensional density field integrated over the line-of-sight direction using the corresponding filter calibration curves. The proposed technique is applied for the first time to a shock train in a constant-area straight duct under the upstream condition of the shock train: the freestream Mach number is 1.42, the incoming boundary layer thickness normalised by the duct half height is 0.175, and the corresponding unit Reynolds number $Re/m$ is $2.99 \times 10^7$ m-1. The calculated isopycnic field depicts the streamwise and transverse density variations inside the shock train, the mixing region after the shock train, and the boundary-layer of the interaction region. This technique is shown to be capable of identifying the locations of shocks in a shock train more precisely than a conventional approach measuring the static pressure distribution along the duct wall. In addition, various quantitative visual representations such as a shadowgraphy and a bright-field schlieren can be extracted from the density field acquired by the present approach, and the spatial evolution of the shape and strength of each shock constituting the shock train as well as the boundary layer flow properties can be quantitatively clarified.

Investigation of Electron Effective Mass in AlGaN/GaN Heterostructures by THz Spectroscopy of Drude Conductivity

Terahertz time domain spectroscopy (TDS) of the two-dimensional (2-D) electrons in various commercial AlGaN/GaN heterostructures was performed in the frequency range of 0.1–2.0 THz at selected temperatures of 80 and 300 K. Experimental transmission spectra were analyzed using analytical model derived for the thin conductive layer on dielectric substrate assuming a high-frequency Drude response of electrons. Two-dimensional electron mobility and density values found from the best fit parameters were compared with the results of independent Hall experiment demonstrating good agreement within uncertainty range of 10%. Thermal renormalization of the electron effective mass was revealed in all investigated samples manifesting as the increase of up to 1.45 ± 0.13 fold of nominal value obtained at cryogenic temperatures. Fast method based on measurement of the ratio of transmission spectra at two temperatures (80 and 300 K) was proposed to monitor the renormalization phenomenon in the samples with thin Drude conductivity layer where carrier density is relatively independent on temperature.

Effect of surface roughness on partition of ionic liquids in nanopores by a perturbed-chain SAFT density functional theory.

In this work, the distribution and partition behavior of ionic liquids (ILs) in nanopores with rough surfaces are investigated by a two-dimensional (2D) classical density functional theory model. The model is consistent with the equation of state that combines the perturbed-chain statistical associating fluid theory and the mean spherical approximation theory for bulk fluids. Its performance is verified by comparing the theoretical predictions with the results from molecular simulations. The fast Fourier transform and a hybrid iteration method of Picard iteration and Anderson mixing are used to efficiently obtain the solution of density profile for the sizable 2D system. The molecular parameters for IL-ions are obtained by fitting model predictions to experimental densities of bulk ILs. The model is applied to study the structure and partition of the ILs in nanopores. The results show that the peak of the density profile of counterions near a rough surface is much higher than that near a smooth surface. The adsorption of counterions and removal of co-ions are enhanced by surface roughness. Thus, the nanopore with a rough surface can store more charge. At low absolute surface potential, the partition coefficient for ions on rough surfaces is lower than that on smooth surfaces. At high absolute surface potential, increasing surface roughness leads to an increase in the partition coefficient for counterions and a decrease in the partition coefficient for co-ions.

Model evaluation of accuracy of obtaining total electron content by the method of spaced reception of low Earth orbit satellite signals

A model-based technique to estimate the accuracy of the ionospheric total electron content (TEC) based on the data from spaced receivers of low Earth orbit satellite signals has been suggested. That allows us to estimate the total electron content errors for both quiet ionosphere conditions and under the presence of a plasma ring. To estimate the error, the dynamic model of the electron concentration of the Earth's ionosphere has been used. For the selected input parameters (day of the year, time of day, etc.), a model two-dimensional electron density distribution has been calculated using this model. When calculating this two-dimensional distribution of the electron density, changes have been made that transformed it into a distribution that most closely matches the real ionosphere, for example, the presence of a plasma ring, a trough, etc. Using the obtained distribution, some model quantities (differential phase and total electron content) have been calculated. Then they are used for model estimation of errors in the calculation of the TEC by the spaced reception method. For quite conditions in the ionosphere, the relative error of determining the initial phase constant does not exceed 3 %, and in the case of the plasma ring - 13 %. For the vertical total electron content, the relative error in quite conditions does not exceed 7 %, and in the case of a plasma ring - 60 %. Large values of the relative error are in areas of sharp change of the vertical total electron content.

Control and Modulation of Photoinduced Charge Transfer in a Rigid Donor-Bridge-Acceptor System by Electric Fields.

This article theoretically studies the photoinduced charge transfer (CT) of rigid D-B-A molecules in two-photon absorption (TPA) adjusted by the external electric fields. Using a visualization method, the dynamic changes of light-induced CT in different channels of TPA are presented through a two-dimensional (2D) transition density matrix and a three-dimensional (3D) charge different density. Here, we prove the controllability of TPA on CT under the induction of a strong electric field. Adjusting the field direction and intensity significantly affects the position of the strong absorption peak in the TPA spectra, thereby further changing the electron-hole coherence length and the degree of dispersion. Our results can promote the recognition of the optical properties of the D-B-A system in synthetic molecules and provide an idea for increasing the proportion of excited states for CT in the molecule.

Assessment of the Clusterability of Data Using a Multimodal Convolutional Neural Network

Clustering assigns data points into groups called clusters, which define the characteristics of similar data points. Our work defines a model to identify and assess the presence of a clusterable structure initially in a two-dimensional density grid of a data set which is respectively expanded into a multidimensional density grid according the dimensionality of the data set. Clusterability is defined as the tendency of a data set having a structure for successful clustering. Our approach consists of a multimodal, convolutional neural network to assess the clusterability of a data set. Multimodality is the utilization of multiple sources of information. The output of our approach, the created model, also identifies the type of the clusterable structure (none, centroid and density). Our approach does not require an initial clustering of the data to define its clusterability. In the assessment of the clusterability of high-dimensional data, we utilize random rotations accompanied with an ensemble approach. The multiple experiments of various clustering problems illustrate that our proposed approach is capable of assessing the clusterability of data and of identifying the type of the clusterable structure.

Laser-scan based pose monitoring for guiding erection of precast concrete bridge piers

Precast construction creates cost-effective structures by rapidly assembling prefabricated components. The emerging trend in increasing weights and scales of precast members, poses new challenges on installation quality. This study proposes an efficient and accurate pose measurement technique for guiding the erection of precast bridge piers using terrestrial laser scanning techniques. An arbitrary position of the scanner is allowed which is pre-calibrated by a single scan before the erection. For data analysis, a novel coarse-to-fine registration method is proposed to automatically align the scan model with the design model. A feature descriptor based on two-dimensional local kernel density map is used for fast coarse registration. An objected-oriented iterative closest point (ICP) registration based on virtual scan generator and nearest neighbour search is adopted for accurate fine registration. The proposed system was evaluated in the construction of an expressway viaduct and shows the potential for improving the installation efficiency and accuracy.

Characterization of trap states in AlN/GaN superlattice channel high electron mobility transistors under total-ionizing-dose with 60Co γ-irradiation

In this Letter, the effects of trap states in AlN/GaN superlattice channel HEMTs (high electron mobility transistors) under total ionizing dose with γ-irradiation have been systematically investigated. After 1 Mrad γ-irradiation with a dose rate of 50 rad/s, negative drifts in threshold voltage and C–V characteristics are observed. Simultaneously, the two-dimensional electron gas sheet density of the upper channel increases from 5.09 × 1012 to 5.47 × 1012 cm−2, while that of the lower channel decreases from 4.41 × 1012 to 3.86 × 1012 cm−2, respectively. Furthermore, frequency-dependent capacitance and conductance measurements are adopted to investigate the evolution of trap states in an electron channel. The trap state density (DT = 0.21–0.88 × 1013 cm−2 eV−1) is over the ET range from 0.314 to 0.329 eV after irradiation for the upper channel, while the trap state in the lower channel decreases from 4.54 × 1011 cm−2 eV−1 at ET = 0.230 eV to 2.38 × 1011 cm−2 eV−1 at ET = 0.278 eV. The density (1.39–1.54 × 1011 cm−2 eV−1) of trap states with faster τT (0.033–0.037 μs) generated in a lower channel is located at shallower ET between 0.227 and 0.230 eV. The results reveal the mechanism of trap states in the channel, affecting the performance of HEMTs, which can provide a valuable understanding for hardening in space radiation.

Modeling the arc and ring structures in the HD 143006 disk

Rings and asymmetries in protoplanetary disks are considered as signposts of ongoing planet formation. In this work, we conduct three-dimensional radiative transfer simulations to model the intriguing disk around HD 143006 that has three dust rings and a bright arc. A complex geometric configuration, with a misaligned inner disk, is assumed to account for the asymmetric structures. The two-dimensional surface density is constructed by iteratively fitting the ALMA data. We find that the dust temperature displays a notable discontinuity at the boundary of the misalignment. The ring masses range from 0.6 to 16Mearth that are systematically lower than those inferred in the younger HL Tau disk. The arc occupies nearly 20% of the total dust mass. Such a high mass fraction of dust grains concentrated in a local region is consistent with the mechanism of dust trapping into vortices. Assuming a gas-to-dust mass ratio of 30 that is constant throughout the disk, the dense and cold arc is close to the threshold of being gravitationally unstable, with the Toomre parameter Q~1.3. Nevertheless, our estimate of Q relies on the assumption for the unknown gas-to-dust mass ratio. Adopting a lower gas-to-dust mass ratio would increase the inferred Q value. Follow-up high resolution observations of dust and gas lines are needed to clarify the origin of the substructures.

Bayesian Inference on the Bimodality of the Generalized von Mises Distribution

This article introduces Bayesian inference on the bimodality of the generalized von Mises (GvM) distribution for planar directions (Gatto and Jammalamadaka in Stat Methodol 4(3):341–353, 2007). The GvM distribution is a flexible model that can be axial symmetric or asymmetric, unimodal or bimodal. Two inferential approaches are analysed. The first is the test of null hypothesis of bimodality and Bayes factors are obtained. The second approach provides a two-dimensional highest posterior density (HPD) credible set for two parameters relevant to bimodality. Based on the identification of the two-dimensional parametric region associated with bimodality, the inclusion of the HPD credible set in that region allows us to infer on the bimodality of the underlying GvM distribution. A particular implementation of the Metropolis–Hastings algorithm allows for the computation of the Bayes factors and the HPD credible sets. A Monte Carlo study reveals that, whenever the samples are generated under a bimodal GvM, the Bayes factors and the HPD credible sets do clearly confirm the underlying bimodality.

Three-Dimensional Numerical Characterization of High-Temperature Superconductor Bulks Subjected to Rotating Magnetic Fields

High-temperature superconductor (HTS) bulks have shown very promising potential for industrial applications due to the ability to trap much higher magnetic fields compared to traditional permanent magnets. In rotating electrical machines, the magnetic field is a combination of alternating and rotating fields. On the contrary, all studies on electromagnetic characterization of HTS presented in the literature so far have only focused on alternating AC magnetic fields and alternating AC loss due to the unavailability of robust experimental techniques and analytical models. This paper presents a numerical investigation on the characterization of HTS bulks subjected to rotating magnetic fields showing AC loss, current density distribution in three-dimensional axes, and trapped field analysis. A three-dimensional numerical model has been developed using H-formulation based on finite element analysis. An HTS cubic sample is magnetized and demagnetized with two-dimensional magnetic flux density vectors rotating in circular orientation around the XOY, XOZ, and YOZ planes.

Geometry of the Magma Chamber and Curie Point Depth Beneath Hawaii Island: Inferences From Magnetic and Gravity Data

This study used land gravity and airborne magnetic data to investigate the depth to the magmatic chamber and map the heat flow distribution beneath the active volcanoes of Hawaii Island using the Curie point depth (CPD) and gravity modeling. Obtaining some of the ground-based geophysical measurements was problematic due to accessibility limitations; therefore, this study used available data. The CPD and magnetic data were used to map the depth to the bottom of the magnetic layer by calculating the depth to the Curie isotherm (540°C) beneath Hawaii Island. The spectral peak method was used to calculate the depths to the shallow and deep magnetic sources for the entire island, and the CPD was calculated using the centroid method. A two-dimensional density model for two Earth layers was constructed using forward modeling of the gravity data. A large plume of dense intrusive material was observed beneath the three adjacent volcanoes of Mauna Loa, Mauna Kea, and Kilauea, and two small chambers were found to be located beneath the Kohala and Hualalai volcanoes. Based on the gravity modeling results, the depth to the magma layer varied from 0.5 to 10 km, and the heat flow was higher close to the volcanic eruption zones. The current study is informative and cost effective for the world’s most active volcanic areas.

Ionospheric Sounding Based on Spaceborne PolSAR in P-Band

The signal of spaceborne low-frequency full-polarization synthetic aperture radar (full-pol SAR) contains abundant ionospheric information. Phased Array L-band Synthetic Aperture Radar (PALSAR) working in the L-band has been verified as an emerging ionospheric sounding technology. Aiming for a future P-band SAR system, this paper investigates the ability of the P-band SAR system in ionospheric one-dimensional and two-dimensional detection. First, considering different systematic error levels, the total electron content (TEC) retrieval in L/P-band is studied by using three typical full-pol SAR data sets based on a circular polarization algorithm. Second, the TEC data retrieved by SAR are fused with the ionosonde, and the joint retrieval of ionospheric electron density is performed. Results show that the P-band TEC retrieval is approximately twice as accurate as the L-band retrieval under the same conditions, and possesses excellent robustness. In addition, the TEC obtained by L/P-band SAR can be used to correct the electron density of the topside on the ionosonde. Results also show that compared with the topside correction accuracy of L-band SAR, that of the P-band SAR is improved by more than 20%. SAR has natural high-resolution characteristics and the P-band signal contains more obvious ionospheric information than the L-band signal. Therefore, future spaceborne P-band SAR has many advantages in two-dimensional fine ionospheric observation and one-dimensional electron density retrieval.