Inverse Time Research Articles

New non-contrast MRI of endolymphatic hydrops in Ménière's disease considering inversion time.

Three-tesla MRI with gadolinium-based contrast agents is important in diagnosing Ménière's disease. However, contrast agents cannot be used in some patients. By using the compositional difference between the inner ear endolymph and perilymph, we performed basic and clinical research focused on potassium ions and protein to find the optimal parameters for visualizing endolymphatic hydrops on MRI without contrast. We then examined the relationship between severity stage and visualization rate of endolymphatic hydrops. In phantom experiments simulating the endolymph and perilymph, we explored MRI parameters that could be used to separate endolymph from perilymph by gradually changing the inversion time. We then used these parameters to perform both new non-contrast MRI and contrast MRI on the same day in Ménière's disease patients, and we compared the visualization rates of endolymphatic hydrops under the two modalities. Fifty patients were selected from 478 patients with Ménière's disease of different severity stages; 12 patients had asthma and allergy to contrast agents. The higher the disease stage, the higher the endolymphatic hydrops visualization rate. The new non-contrast MRI gave significantly higher (p < .01) visualization rates of endolymphatic hydrops on the affected side in patients at Stage 3 or above than in Stages 1 and 2 combined. New non-contrast MRI with parameters focusing on the endolymph-perilymph difference in the density of protons surrounding the potassium ions and protein can produce images consistent with endolymphatic hydrops. We believe that this groundbreaking method will be useful for diagnosing Ménière's disease in patients. Clinical studies are at evidence level 3 in non-randomized controlled trials.

Dielectric characterisation and intermolecular interactions in propionitrile-dimethyl sulphoxide mixtures using a time domain reflectometry

ABSTRACT Complex permittivity spectra of propionitrile (PN) – dimethyl sulphoxide (DMSO) mixtures in the 10 MHz–30 GHz frequency range were investigated using time-domain reflectometry (TDR). The Debye model was employed to fit the measured dielectric spectra, allowing for the calculation of dielectric parameters like the static dielectric constant (ε 0 ) and relaxation time (τ). Excess dielectric constant (ε 0 E ) and excess inverse relaxation time (1/τ) E were subsequently calculated from the measured ε 0 and τ. The observed variations in these excess parameters provide evidence for intermolecular interactions within the PN-DMSO mixtures. Theoretical estimates of the ε 0 were obtained using Luzar’s hydrogen-bonded model.

Leaves to Measure Light Intensity.

Quantitative measurement of light intensity is a key step in ensuring the reliability and the reproducibility of scientific results in many fields of physics, biology, and chemistry. The protocols presented so far use various photoactive properties of manufactured materials. Here, leaves are introduced as an easily accessible green material to calibrate light intensity. The measurement protocol consists in monitoring the chlorophyll fluorescence of a leaf while it is exposed to a jump of constant light. The inverse of the characteristic time of the initial chlorophyll fluorescence rise is shown to be proportional to the light intensity received by the leaf over a wide range of wavelengths and intensities. Moreover, the proportionality factor is stable across a wide collection of plant species, which makes the measurement protocol accessible to users without prior calibration. This favorable feature is finally harnessed to calibrate a source of white light from exploiting simple leaves collected from agarden.

A new fault detection and relay coordination technique for low voltage DC microgrid

Integration of various distributed generation units in the DC distribution network creates an outsized impact on magnitude and direction of fault current. The specific behaviour of fault current during a short circuit fault demands quick and reliable detection and isolation of fault, which poses a significant challenge in developing an effective protection and coordination scheme for DC microgrids. In this regard, a new fault detection and relay coordination algorithm is proposed for a Low voltage DC (LVDC) microgrid, which utilizes inverse time relay characteristics based on variance of fault current. While the relay responds and picks up during both internal and external faults, its operation is automatically limited upon operation of the downstream circuit breaker without the need for any communication channel. The efficacy of the proposed strategy is evaluated by modelling a mesh-connected DC microgrid network using PSCAD/EMTDC software. The protection coordination problem is developed as a constraint non-linear optimization problem to determine optimal relay settings. The proposed approach has been tested by simulating various types of faults at different fault locations and fault resistances along with various microgrid operating modes. The results suggest that the proposed strategy stands out by optimizing relay operating time and providing backup protection while upholding relay coordination. A comparative assessment of the proposed technique with existing ones proves its effectiveness in terms of swift relay tripping time, better immunity against noise, and independency against microgrid operating mode and topology.

Optimal Coordination of Inverse Time Overcurrent Relays using COA and GSA Heuristic Algorithms

Optimal Coordination of Inverse Time Overcurrent Relays using COA and GSA Heuristic Algorithms

Inverse design of topological photonic time crystals via deep learning

Photonic time crystals are a new kind of photonic system in modern optical physics, leading to devices with new properties in time. However, so far, it is still a challenge to design photonic time crystals with specific topological states due to the complex relations between time crystal structures and topological properties. Here, we propose a deep-learning-based approach to address this challenge. In a photonic time crystal with time inversion symmetry, each band separated by momentum gaps can have a non-zero quantized Berry phase. We show that the neural network can learn the relationship between time crystal structures and Berry phases, and then determine the crystal structures of photonic time crystals based on given Berry phase properties. Our work shows a new way of applying machine learning to the inverse design of time-varying optical systems and has potential extensions to other fields, such as time-varying phononic devices.

Alternating spin splitting of electronic and magnon bands in two-dimensional altermagnetic materials

Abstract Unconventional antiferromagnetism dubbed as altermagnetism was firstly discovered in rutile structured magnets, which is featured by spin splitting even without the spin-orbit coupling effect. This interesting phenomenon has led to the discovery of more altermagnetic materials. In this work, we explore two-dimensional altermagnetic materials by studying two series of two-dimensional magnets, including MF4 with M covering all 3d and 4d transition metal elements, as well as TS2 with T = V, Cr, Mn, Fe. Through the magnetic symmetry operation of the RuF4 and MnS2, it was verified that breaking the time inversion is a necessary condition for spin splitting. Based on symmetry analysis and first-principles calculations, we found that the electronic bands and magnon dispersion experience alternating spin splitting along the same path. This work paves the way for exploring altermagnetism in two-dimensional materials.

2D MnTiX2 (X = F/Cl/Br) monolayers: Robust valley-polarized quantum anomalous Hall insulators with high transition temperatures and wide bandgaps

The valley-polarized quantum anomalous Hall (VP-QAH) materials, which combine valley polarization and the quantum anomalous Hall (QAH) effect, are of both fundamental and technological importance due to their potential applications for nanoscale devices. Here, we explored the stability, magnetic, and topological properties of two-dimensional MnTiX2 (X = F/Cl/Br) monolayers based on first-principles calculations. Our results show that all the structures have robust antiferromagnetic orders with large magnetic anisotropic energies and high transition temperatures (480–670 K). In the absence of spin–orbital coupling (SOC), the MnTiX2 monolayers represent antiferromagnetic quadratic crossing semimetals. In the presence of SOC, the quadratic crossing points of the systems are opened with sizable bandgaps (&amp;gt; 0.5 eV), and they are transformed to be QAH insulators with |C| = 1. Moreover, the MnTiF2 monolayer is found to be a spontaneous VP-QAH semiconductor due to the time reversal symmetry and inversion symmetry being broken. These insights provide an ideal platform for achieving VP-QAH materials for dissipationless transport and quantum computing.

I/O-efficient iterative matrix inversion with photonic integrated circuits

Photonic integrated circuits have been extensively explored for optical processing with the aim of breaking the speed and energy efficiency bottlenecks of digital electronics. However, the input/output (IO) bottleneck remains one of the key barriers. Here we report a photonic iterative processor (PIP) for matrix-inversion-intensive applications. The direct reuse of inputted data in the optical domain unlocks the potential to break the IO bottleneck. We demonstrate notable IO advantages with a lossless PIP for real-valued matrix inversion and integral-differential equation solving, as well as a coherent PIP with optical loops integrated on-chip, enabling complex-valued computation and a net inversion time of 1.2 ns. Furthermore, we estimate at least an order of magnitude enhancement in IO efficiency of a PIP over photonic single-pass processors and the state-of-the-art electronic processors for reservoir training tasks and multiple-input and multiple-output (MIMO) precoding tasks, indicating the huge potential of PIP technology in practical applications.

The irreversibility of microscopic motions

People have long had a problem: the equations of motion that reflect the laws of physics are invariant under time inversion, while there always are irreversible processes for gases composed of microscopic particles. This article solves the problem. The point is that we should distinguish between the concepts of the equation of motion and concrete motion. We also need to distinguish between the concepts of time-inverse motion and reverse motion. The former is anticlockwise, which is a fictional motion, while the latter is clockwise. For the single-particle motions in classical mechanics and in quantum mechanics, we present mathematical expressions for time-inversion motion and reverse motion, respectively. We demonstrate that single-particle motion is irreversible. The definition of the reversibility of two-particle collisions is given. According to the definition, the two-particle collision as a microscopic motion process is irreversible. Consequently, for a gas consisting of a large number of particles colliding with each other, its movement should be irreversible, unless the condition of detailed balance is met. We provide a physical explanation for detailed balance, which does not concern the meaning of microscopic reversibility. The detailed balance means that after a pair of reciprocal collisions occur, the distribution function of the particles remains unchanged. Therefore, microscopic two-particle collision events are irreversible, but the statistical average of a large number of collision events makes it possible for the macroscopic process of a gas to be reversible. Conclusively, we clarify the microscopic mechanism of the irreversible process of gases.

Probing ultralight scalar, vector and tensor dark matter with pulsar timing arrays

Pulsar timing arrays (PTAs) are sensitive to oscillations in the gravitational potential along the line-of-sight due to ultralight particle pressure. We calculate the probing power of PTAs for ultralight bosons across all frequencies, from those larger than the inverse observation time to those smaller than the inverse distance to the pulsar. We show that since the signal amplitude grows comparably to the degradation in PTA sensitivity at frequencies smaller than inverse observation time, the discovery potential can be extended towards lower masses by over three decades, maintaining high precision. We demonstrate that, in the mass range 10−26−10−23 eV, existing 15-year PTA data can robustly detect or rule out an ultralight component down to O(1−10)% of the total dark matter. Non-detection, together with other bounds in different mass ranges, will imply that ultralight scalar/axion can comprise at most 1−10% of dark matter in the 10−30−10−17 eV range. With 30 years of observation, current PTAs can extend the reach down to 0.1−1%, while next-generation PTAs such as SKA can attain the 0.01−0.1% precision. We generalize and derive predictions for ultralight spin-1 vector (i.e. dark photon) and spin-2 tensor dark components.

Joint inversion of induced polarization and hydraulic tomography data for hydraulic conductivity imaging

SUMMARY For accurate modelling of groundwater flow and transport processes within an aquifer, precise knowledge about hydraulic conductivity K and its small-scale heterogeneities is fundamental. Methods based on pumping tests, such as hydraulic tomography (HT), allow for retrieving reliable K-estimates, but are limited in their ability to image structural features with high resolution, since the data from time-consuming hydraulic tests are commonly sparse. In contrast, geophysical methods like induced polarization (IP) can potentially yield structural images of much higher resolution, but depend on empirical petrophysical laws that may introduce significant uncertainties to the K-estimation. Therefore, this paper presents a joint inversion procedure for both HT and IP data, which allows for combining the complementary abilities of both methods. Within this approach, a traveltime inversion is applied to the HT data, while the IP inversion is based on a full-decay time-domain forward response, as well as a reparametrization of the Cole–Cole model to invert for K directly. The joint inversion is tested on a synthetic model mimicking horizontally layered sediments, and the results are compared with the individual HT and IP inversions. It is shown that jointly inverting both data sets consistently improves the results by combining the complementary sensitivities of the two methods, and that the inversion is more robust against changes in the experimental setups. Furthermore, we illustrate how a joint inversion approach can correct biases within the petrophysical laws by including reliable K-information from hydraulic tests and still preserving the high-resolution structural information from IP. The different inversion results are compared based on the structural similarity index (SSIM), which underlines the robustness of the joint inversion compared to using the data individually. Hence, the combined application of HT and IP within field surveys and a subsequent joint inversion of both data sets may improve our understanding of hydraulically relevant subsurface structures, and thus the reliability of groundwater modelling results.

A Rapid Detection Method for Coal Ash Content in Tailings Suspension Based on Absorption Spectra and Deep Feature Extraction

Traditional visual detection methods that employ image data are often unstable due to environmental influences like lighting conditions. However, microfiber spectrometers are capable of capturing the specific wavelength characteristics of tail coal suspensions, effectively circumventing the instability caused by lighting variations. Utilizing spectral analysis techniques for detecting ash content in tail coal appears promising as a more stable method of indirect ash detection. In this context, this paper proposes a rapid detection method for the coal ash content in tailings suspensions based on absorption spectra and deep feature extraction. Initially, a preprocessing method, the inverse time weight function (ITWF), is presented, focusing on the intrinsic connection between the sedimentation phenomena of samples. This enables the model to learn and retain spectral time memory features, thereby enhancing its analytical capabilities. To better capture the spectral characteristics of tail coal suspensions, we designed the DSFN (DeepSpectraFusionNet) model. This model has an MSCR (multi-scale convolutional residual) module, addressing the conventional models’ oversight of the strong correlation between adjacent wavelengths in the spectrum. This facilitates the extraction of relative positional information. Additionally, to uncover potential temporal relationships in sedimentation, we propose a CLSM-CS (convolutional long-short memory with candidate states) module, designed to strengthen the capturing of local information and sequential memory. Ultimately, the method employs a fused convolutional deep classifier to integrate and reconstruct both temporal memory and positional features. This results in a model that effectively correlates the ash content of suspensions with their absorption spectral characteristics. Experimental results confirmed that the proposed model achieved an accuracy of 80.65%, an F1-score of 80.45%, a precision of 83.43%, and a recall of 80.65%. These results outperformed recent coal recognition models and classical temporal models, meeting the high standards required for industrial on-site ash detection tasks.

Far-from-equilibrium slow modes and momentum anisotropy in an expanding plasma

The momentum distribution of particle production in heavy-ion collisions encodes information about thermalization processes in the early-stage quark-gluon plasma. We use kinetic theory to study the far-from-equilibrium evolution of an expanding plasma with an anisotropic momentum-space distribution. We identify slow and fast degrees of freedom in the far-from-equilibrium plasma from the evolution of moments of this distribution. At late times, the slow modes correspond to hydrodynamic degrees of freedom and are naturally gapped from the fast modes by the inverse of the relaxation time, τR−1. At early times, however, there are an infinite number of slow modes with a gap inversely proportional to time, τ−1. From the evolution of the slow modes we generalize the paradigm of the far-from-equilibrium attractor to vector and tensor components of the energy-momentum tensor, and even to higher moments of the distribution function that are not part of the hydrodynamic evolution. We predict that initial-state momentum anisotropy decays slowly in the far-from-equilibrium phase and may persist until the relaxation time. Published by the American Physical Society 2024

Josephson diode effect in junctions of superconductors with band asymmetric metals

At interfaces connecting two superconductors (SCs) separated by a metallic layer, an electric current is induced when there is a disparity in the phases of the two superconductors. We elucidate this phenomenon based on the weights of the Andreev bound states associated with the states carrying currents in forward and reverse directions. Typically, current phase relation (CPR) in Josephson junctions is an odd function. When time reversal and inversion symmetries are broken at the junction, CPR ceases to be an odd function and the system may exhibit Josephson diode effect. This phenomenon has been studied in spin orbit coupled systems under an external Zeeman field wherein the magnetochiral anisotropy is responsible for the Josephson diode effect. Recently introduced the band asymmetric metal (BAM) model presents a novel avenue, featuring an asymmetric band structure. We investigate DC Josephson effect in SC-BAM-SC junctions and find that band asymmetry can lead to Josephson diode effect and anomalous Josephson effect. We explain the mechanism behind these effects based on interference of plane wave modes within the Bogoliubov de-Genne formalism. We calculate diode effect coefficient for different values of the parameters.

Enhancing T1 signal of normal-appearing white matter with divided subtracted inversion recovery: A pilot study in mild traumatic brain injury.

Magnetic resonance imaging (MRI) and cognitive profiles in patients with mild traumatic brain injury (mTBI) are often discordant. Conventional MRI seldom captures the full extent of pathological changes in the normal-appearing white matter (NAWM). The divided subtracted inversion recovery (dSIR) technique may enhance T1 differences in NAWM, making them easily visible. We aimed to implement dSIR on a clinical scanner and tested results in mTBI patients. To produce dSIR images, Inversion Recovery-Turbo Spin Echo sequences were modified using six different inversion times (TI) on a 3-T scanner in healthy participants and patients with mTBI. The multiple TIs determined normal white (TIshort) and gray matter (TIlong) nulling points in healthy subjects, which were used to create dSIR images. In one patient, the protocol was repeated at 3 months to identify changes after rehabilitation. Diffusion tensor imaging (DTI)-derived mean diffusivity (MD) and fractional anisotropy (FA) maps were aligned to dSIR images to ensure that signal was not artefactual. Ten healthy participants (five females; age 24 ± 3 [95% CI: 21, 26] years) were included. TIshort and TIlong were set at 450 and 750 ms, respectively. In both patients (one male, age 17 years; one female, age 14 years), dSIR images revealed areas with increased T1 in the NAWM not visible on conventional MRI. dSIR-based hyperintensities corresponded to elevated MD and reduced FA. Substantial changes were found at follow-up with improvement in DTI-based parameters. dSIR images enhance subtle changes in the NAWM of patients with mTBI by amplifying their intrinsic T1 signal.

The type-I, III nodal ring, type-I, III quadratic nodal point, and Dirac valley phonons in 2D kagome lattices M2C3 (M = As, Bi, Cd, Hg, P, Sb, Zn)

Topological phases in kagome systems have garnered considerable interest since the introduction of the colloidal kagome lattice. Our study employs first-principle calculations and symmetry analysis to predict the existence of ideal type-I, III nodal rings (NRs), type-I, III quadratic nodal points (QNPs), and Dirac valley phonons (DVPs) in a collection of two-dimensional (2D) kagome lattices M2C3 (M = As, Bi, Cd, Hg, P, Sb, Zn). Specifically, the Dirac valley points (DVPs) can be observed at two inequivalent valleys with Berry phases of +π and −π , connected by edge arcs along the zigzag and armchair directions. Additionally, the QNP is pinned at the Γ point, and two edge states emerge from its projections. Notably, these kagome lattices also exhibit ideal type-I and III nodal rings protected by time inversion and spatial inversion symmetries. Our work examines the various categories of nodal points and nodal ring phonons within the 2D kagome systems and presents a selection of ideal candidates for investigating topological phonons in bosonic systems.

Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates. To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2). Retrospective. 621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing. 3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI. Pathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models. Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. For ML models, the generated probability was used to construct the ROC curves. Peritumoral edema, the presence of multiple lesions and non-mass enhancement (NME) showed significant differences. For distinguishing HER2-zero from non-zero (low + positive), multiple lesions, edema, margin, and tumor size were selected, and the k-NN model achieved the highest AUC of 0.86 in the training set and 0.79 in the testing set. For differentiating HER2-low from HER2-positive, multiple lesions, edema, and margin were selected, and the DT model achieved the highest AUC of 0.79 in the training set and 0.69 in the testing set. BI-RADS features read by radiologists from preoperative MRI can be analyzed using more sophisticated feature selection and ML algorithms to build models for the classification of HER2 status and identify HER2-low. Stage 2.

Energy hierarchies governing quarkonium dynamics in heavy ion collisions

In this paper, we critically examine hierarchies between the energy scales that determine quarkonium dynamics in the quark gluon plasma. A particularly important role is played by the ratio of the binding energy of species (Eb) and the medium scales; temperature (T) and Debye mass (mD). It is well known that if these ratios are much larger than 1 then the dominant process governing quarkonium evolution is dissociation by thermal gluons (gluodissociation). On the other hand, if the ratio Eb/T is much smaller than one then quarkonium dynamics is dominated by screening and Landau damping of the exchanged gluons. Here we show that over most of the evolution, the scale hierarchies do not fall in either limit and one needs to use the full structure of the gluonic spectral function to follow the dynamics of the QQ¯ pair. This has a significant bearing when we follow the quantum dynamics of quarkonia in the medium. The inverse medium relaxation time is also ≈T and if Eb is comparable (or larger) in magnitude to T, the quantum evolution of QQ¯ is nonlocal in time within the Brownian approximation. Published by the American Physical Society 2024

Semimetallic state transition in Two-Dimensional carbon nitride covalent networks and enhanced electrocatalytic activity for nitrate to ammonia conversion

Single-atom catalysts (SACs), due to their maximum atomic utilization rate, show tremendous potential for application in the electrocatalytic synthesis of ammonia from nitrate. Yet, the development of superior supports that preserve the high selectivity, activity, and stability of SACs remains an imperative challenge. In this work, based on first-principles calculations and tight-binding (TB) model analysis, a new two-dimensional (2D) carbon nitride monolayer, C7N6, is proposed. The C7N6 structure exhibits a strong covalent network, with dynamical, thermal, and mechanical stability. Surprisingly, the structural transition from C9N4 to C7N6 corresponds to a semimetallic state transition. Further symmetry analysis unveils that the Dirac states in C7N6 are protected by space–time inversion symmetry, and the physical origin of the Dirac cone was confirmed using the TB model. Additionally, a non-zero Z2 invariant and significant topological edge states demonstrate its topologically nontrivial nature. Considering the excellent structural and topological properties of C7N6, a three-step screening strategy is designed to identify eligible SACs for electrochemical nitrate reduction reaction (NO3RR), and Ti@C7N6 is identified as possessing the best activity, with the last proton-electron coupling step *NH2→*NH3 being the potential-determining step (PDS), for which the limiting potential is 0.48 V. Moreover, a free energy diagram shows that the *NOH reaction pathway is energetically preferred on Ti@C7N6, and ab initio molecular dynamics (AIMD) calculations at 500 K confirm its good thermal stability. Our study not only provides excellent CN-based support material but also offers theoretical guidance for constructing highly active and selective SACs for nitrate reduction.