Inversion Technique Research Articles

Quantifying Methane Emissions Using Satellite Data: Application of the Integrated Methane Inversion (IMI) Model to Assess Danish Emissions

After stabilizing in the mid-2000s, atmospheric methane (CH4) levels have accelerated over the past decade. In response, satellite-based inversion techniques have been employed to meet the increasing demands of the climate community. In this study, the Integrated Methane Inversion (IMI) model, a novel approach based on the TROPOspheric Monitoring Instrument (TROPOMI), is used to quantify CH4 emissions across Denmark. Over 900,000 TROPOMI observations from spring to early autumn of 2018–2022 were used to inform the inversions. Overall, TROPOMI CH4 concentrations within the inversion domain showed an upward trend of approximately 12.71 ppb per year, reflecting the global trend. Excluding 2022, which included only four months of data, the inversions suggest an underestimation of emissions by 190(160–215) × 103 tonnes, or 66(56–75)% of prior estimates. Northern and southern Jutland, along with the Copenhagen metropolitan area, were identified as key sources of CH4 emissions. Additionally, the inversions indicated a decline in emissions during the COVID-19 pandemic, despite stable activity data. This study demonstrates the feasibility of using the IMI model to monitor CH4 emissions in small countries like Denmark, offering a satellite-based perspective to better identify and mitigate these emissions.

A merging approach for hole identification with the NMM and WOA-BP cooperative neural network in heat conduction problem

Defect identification is an important issue in structural health monitoring. Herein, originated from inverse techniques, a merging approach is established by the numerical manifold method (NMM) and whale optimization algorithm-back propagation (WOA-BP) cooperative neural network to identify hole defects in heat conduction problems. On the one hand, the NMM can simulate varying hole configurations on a fixed mathematical cover, which eases the generation of “big data” for the training of neural network to a large extent. On the other hand, the WOA, a global optimization algorithm, is adopted to optimize the initial weights and thresholds of the BP neural network to alleviate its frequently encountered local optimum phenomenon. The boundary temperatures of sampling points by the NMM and the associated hole geometries are used for the learning of WOA-BP neural network, which is then applied to predict the hole defects. Numerical examples concerning the detection of circular/ elliptical holes demonstrate that the proposed method possesses higher accuracy and satisfying robustness in holes prediction compared with standard BP network under the same condition. The present work provides a convenient pathway and great potential in application of structural health monitoring.

Microstructural material design of pearlitic steel lamella for desired mechanical properties

Designing materials for a targeted mechanical property is a complex problem due to its structure and property relationship nature, traditionally relying on experimental methods. Computational approaches offer a promising alternative, but they are often computationally intensive. This limits their applicability for solving the goal-based structure design problem. This study focuses on the design of microstructure to achieve specific mechanical properties by combining generative adversarial networks, deep neural networks, and genetic algorithm optimization techniques. In this work to demonstrate the effectiveness of the proposed approach we select a widely used pearlitic steel lamella microstructure where the phase fraction of ferrite and cementite and the lamella distribution significantly influence the strength and ductility. A method for designing the pearlite microstructure features for the targeted mechanical properties, such as yield strength (σy), ultimate strength (σut), and fracture strain (εf) is developed by applying inverse microstructure design techniques to achieve the targeted mechanical properties, specifically in fully pearlitic steel materials. The findings hold great promise, particularly when coupled with additive manufacturing, in offering an efficient and versatile framework for structural materials design for targeted material applications.

Impact Analysis of Orthogonal Circular-Polarized Interference on GNSS Spatial Anti-Jamming Array

With the continuous advancement of electromagnetic countermeasures, new types of interference signals (e.g., multi-polarization suppression interference) pose a significant threat to conventional Global Navigation Satellite System (GNSS) services, even when the receiver employs a right-handed circularly polarized (RHCP) anti-jamming array. This paper proposes a receiving signal model for orthogonal circularly polarized (OCP) interference signals based on conventional arrays, following an analysis of the non-ideal characteristics of actual arrays. Furthermore, the mechanism by which OCP interference signals affect anti-jamming performance is examined. Power inversion (PI) and linear constrained minimum variance (LCMV) techniques, applied to both uniform linear arrays and central circular arrays, are utilized to verify the impact of these interference signals. Simulation and physical testing demonstrate that OCP interference significantly affects the interference subspace of the conventional RHCP array, potentially leading to a reduction in the anti-jamming performance of the receiver. To effectively suppress multi-polarization interference, anti-jamming GNSS receivers must either ensure the consistency of cross-polarization among the elements of the array or adopt polarization-sensitive arrays.

ESTIMATING OPERATIONAL COSTS FOR ENHANCED CONTINUITY IN WATER SUPPLY AND SANITATION SERVICES: AN INVERSE DATA ENVELOPMENT ANALYSIS APPROACH IN CHILEAN UTILITIES

Study regionThe largest 23 water utilities in Chile providing water and sanitation services to 97.9% of total urban customers. Study focusThis study introduces an innovative methodological framework based on inverse data envelopment analysis technique to quantify the additional operational costs required for water utilities to enhance their water supply and sanitation continuity while maintaining their techno-economic efficiency. This framework is pivotal for setting realistic continuity targets and assessing potential impacts on water and sanitation tariffs, thereby aiding in policy and operational planning within the water sector. New hydrological insights for the regionThe analysis was performed across four distinct continuity scenarios revealing substantial cost variability contingent on continuity targets, current service levels, and the efficiency of each utility. Results showed a median operational cost increase that varied from 28.41% in the most stringent scenario to just 0.27% in the most lenient. The financial implications of meeting these costs through water and sanitation tariffs would necessitate a median monthly increase in water bills, ranging from $17.24 per household in the most demanding scenarios to only $0.01 per household in the more lenient scenarios. This research provides a valuable tool for water sector regulators and managers, enabling informed decision-making by balancing service improvement objectives with economic considerations and customer willingness to pay for enhanced service continuity.

Development of a Negatively Charged Polyether Sulfone Membrane Enriched with MIL-101(Cr)-Modified Magnetic Activated Pyrolytic Coke for Enhanced Dye Removal, Permeability, and Antifouling Properties

Developing high-performance nanofiltration membranes for effectively treating wastewater contaminated with organic pollutants has become increasingly important. Given the hazardous nature of these contaminants, proper treatment of wastewater is crucial before it is released into natural water bodies. This study synthesized a novel composite membrane by incorporating Fe3O4@APC@MIL-101(Cr) nanocomposites into a polyether sulfone (PES) matrix using a phase inversion technique. The inclusion of 0.5wt.% Fe3O4@APC@MIL-101(Cr) significantly enhanced the membrane's hydrophilicity, porosity, and surface characteristics. The optimized membrane achieved an impressive pure water flux (PWF) of 90.24L/m²h, reflecting a 400% improvement over pristine PES membranes. Moreover, it exhibited excellent rejection rates of 97.21% for crystal violet (CV) and 99.43% for methylene blue (MB), alongside a rejection efficiency of 57.73% for the anionic dye methyl orange (MO). The antifouling performance was notably improved, as evidenced by an increase in the flux recovery ratio (FRR) from 46% for bare PES membranes to 81.11% for hybrid membranes. Additionally, the effects of various salt types on the membrane's separation performance were investigated. The Fe3O4@APC@MIL-101(Cr) modified PES membranes demonstrate significant potential as effective candidates for treating wastewater contaminated with dye pollutants.

Assessment of Aquifer Resistivity, Depth, and Thickness using Vertical Electrical Sounding (VES) in Parts of Bori Metropolis for Groundwater Exploration

Groundwater exploration is vital to guaranteeing a reliable water supply in both urban and rural locations. In Bori Metropolis, the growing demand for potable water necessitates a thorough examination of the aquifer system. Therefore, the purpose of this study is to determine the groundwater potential in Bori Metropolis by assessing aquifer resistivity, depth, and thickness using Vertical Electrical Sounding (VES). A total of 13 VES surveys were completed in the study region using the Schlumberger array. The apparent resistivity measurements were analyzed using 1D inversion techniques to determine aquifer depth and thickness. The results showed that aquifer resistivity values ranged from to at depths ranging from to . Aquifer thicknesses ranged from to , indicating a high groundwater potential in the area. The results indicate that the Bori Metropolis area comprises a well-defined aquifer system with a high potential for groundwater extraction. These findings give important information for future water resource management in the region.

Utilizing the Gravity Method to Detect the Potential Effect of the Salt Dome in the Nahr Omar Oilfield, Southern Iraq

Geophysics is one of the branches of Earth sciences and deals with studying the Earth's interior by studying the variation of physical properties within rock layers. Applied geophysics depends on procedures that involve the measurements of potential fields, such as the gravitational method. One of the significant oil fields in southern Iraq is represented by the Nahr Omar structure. A power spectrum analysis (SPA) technique was used to collect gravity data within the chosen oil field area in order to confirm the salt dome in the subsurface layers. The analysis of SPA resulted from six surfaces representing the gravity variation values of the depths (m)14300, 3780, 3290, 2170, 810, and 93.5. Gravity surfaces have been converted to density surfaces to determine density and velocity models from these six depth slices. The inverted data were consistent with the Nafe and Drake standard curves of converting densities to average velocities. The current study illustrated the benefit/assist of the inversion technique of gravity data to average velocity, to a large extent, in detecting the salt dome effects on the subsurface strata for the Nahr Omar oilfield. In comparison, the decrease in the average velocity of the salt dome area is related to this effect. The acquired inversion results were supported by available seismic data within the Nahr Omar area and utilized the seismic attributes processing technique.

Deep learning for predicting porosity in ultra-deep fractured vuggy reservoirs from the Shunbei oilfield in Tarim Basin, China

Deep and ultra-deep carbonate reservoirs in China, which account for 34% of the country’s oil and gas reserves, pose significant challenges for porosity prediction due to their complex geological features, including extensive burial depth, weak seismic signals, and high heterogeneity. To address these challenges, this study develops an advanced deep learning approach specifically designed for ultra-deep, fault-controlled, fractured-vuggy reservoirs in the Tarim Basin. The study utilizes a three-dimensional seismic dataset and applies Principal Component Analysis (PCA) to select five key features from eight seismic attributes. Additionally, seismic phase-controlled constraints are incorporated into the model. Using deep learning technology, a porosity prediction model for ultra-deep carbonate reservoirs has been constructed. Validation using blind wells from the Shunbei oilfield shows that this approach achieves a 76% reduction in Mean Square Error (MSE) compared to traditional impedance inversion techniques, highlighting its high predictive accuracy. Through SHapley Additive exPlanations (SHAP) analysis, the attributes LAMBDA_AAGFIL and PHASE_ANT are identified as the most influential, highlighting their importance in representing karst cave and fracture structures within the reservoir. These findings underscore the innovation and substantial improvement of the proposed method over conventional techniques, offering a robust and high-precision approach for porosity prediction in ultra-deep carbonate reservoirs.

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. We applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

The impact of additives and dope composition on hollow fiber ultrafiltration membrane for pure water permeability

Abstract In the current work, a dry-jet wet spinning process was used for preparing the polysulfone (PS) polymer hollow fiber ultrafiltration membrane. The polysulfone (PS) polymer was prepared by phase inversion technique with two distinct additives: glycerol and polyethylene glycol (PEG). The HF membranes were characterized and their performance was compared for pure water permeability (PWP). While characterizing, scanning electron microscopy (SEM) analysis on the internal section and cross-sectional area of membranes exhibited thin finger-like structures and thick sponge-like structures, respectively. With aid of an atomic force microscopic (AFM), the average surface roughness (Ra) of extruded HF membranes was found to be 61.253 nm and 81.086 nm, respectively for the membranes prepared using glycerol and PEG as additives Wettability studies revealed that the both membranes were hydrophilic. Further, they subjected to ascertained for the average pore size, surface porosity, stretch length, and breakage load. In addition, an investigation through X-ray diffraction (XRD) analysis exhibited that the acquired fibers were amorphous. The results of these findings showed that an increase in pressure caused a rise in water flow. Though PEG-supported HF membrane was an additive that has been shown to provide a greater water flux than glycerol additive HF, its structural stability suggests that it might be employed for higher pressure applications to satisfy the necessary demand for water processing.

Efficient Trans-Dimensional Bayesian Inversion of C-Response Data from Geomagnetic Observatory and Satellite Magnetic Data

To investigate deep Earth information, researchers often utilize geomagnetic observatories and satellite data to obtain the conversion function of geomagnetic sounding, C-response data, and employ traditional inversion techniques to reconstruct subsurface structures. However, the traditional gradient-based inversion produces geophysical models with artificial structure constraint enforced subjectively to guarantee a unique solution. This method typically requires the model parameterization knowledge a priori (e.g., based on personal preference) without uncertainty estimation. In this paper, we apply an efficient trans-dimensional (trans-D) Bayesian algorithm to invert C-response data from observatory and satellite geomagnetic data for the electrical conductivity structure of the Earth’s mantle, with the model parameterization treated as unknown and determined by the data. In trans-D Bayesian inversion, the posterior probability density (PPD) represents a complete inversion solution, based on which useful inversion inferences about the model can be made with the requirement of high-dimensional integration of PPD. This is realized by an efficient reversible-jump Markov-chain Monte Carlo (rjMcMC) sampling algorithm based on the birth/death scheme. Within the trans-D Bayesian algorithm, the model parameter is perturbated in the principal-component parameter space to minimize the effect of inter-parameter correlations and improve the sampling efficiency. A parallel tempering scheme is applied to guarantee the complete sampling of the multiple model space. Firstly, the trans-D Bayesian inversion is applied to invert C-response data from two synthetic models to examine the resolution of the model structure constrained by the data. Then, C-response data from geomagnetic satellites and observatories are inverted to recover the global averaged mantle conductivity structure and the local mantle structure with quantitative uncertainty estimation, which is consistent with the data.

Subsurface S-wave velocity structure inversion using particle swarm optimization based on horizontal site response extracted using generalized inversion technique

A new methodology, including a mature algorithm with adjustments and a new target for the subsurface sedimentary inversion problem was introduced. This study adopted the particle swarm optimization (PSO) algorithm as a global optimization algorithm. PSO incorporates the patterns embedded in natural bird foraging behaviors to invert the subsurface S-wave velocity structure. Using the concept of particle velocity, PSO involves particle individual inertia, individual experience, and social experience of the swarm, pursuing the global optimum solution in a multidimensional abstract space. The inversion target was the horizontal site amplification factor (HSAF), which was extracted using the generalized inversion technique for the observed strong ground motions. HSAF is an appropriate target in the S-wave velocity structure inversion problem as it directly represents the site response to the incident S-wave at the seismological bedrock. We validated the convergence ability of the PSO and applied it to three field earthquake observation stations. From the perspective of the misfit between the target and estimation velocity profile, improvements exceeding 95 % and 70 % can be confirmed in the validation case and practical applications, respectively. The use of HSAF as the target and PSO as the algorithm is currently limited but is demonstrably effective. This study introduces a methodology that has significant potential for solving velocity structure inversion problems at subsurface levels.

Source-Scaling Comparison and Validation for Ridgecrest, California: Radiated Energy, Apparent Stress, and Mw Using the Coda Calibration Tool (2.6<Mw<7.1)

ABSTRACT The determination of accurate apparent stress, radiated energy, corner frequency, and their scaling with magnitude remains one of the most difficult seismological endeavors because of complicated 3D Earth structure, complex rupture, and limited broadband recordings. This study focuses on a comparison of four separate state-of-the-art methods that aim to compare and contrast common events using the well-recorded 2019 Ridgecrest, California, sequence, which was motivated in large part by the U.S. Geological Survey (USGS)/Southern California Earthquake Center (SCEC) Community Stress-Drop Validation Study group (Baltay et al., 2024). For this study, we calibrated the Ridgecrest and surrounding region using the Coda Calibration Tool (CCT) and compared them against recent generalized inversion technique (GIT) results of Bindi et al. (2021) (2.6<Mw<7.1) and two other state-of-the-art methods for moderate-sized events in the sequence (3.5<Mw<5.5). We find excellent agreement between the GIT and coda-derived results over a broad range of magnitudes, and for moderate-size events, we find equally good agreement with source estimates from finite-fault inversion method based on Dreger (1997) and a direct S-wave spectral method by Ji et al. (2024). As found in a recent comparative study in central Italy by Morasca et al. (2022) (3.5<Mw<6.3), we find that CCT and GIT results are in excellent agreement for events ranging between 2.6<Mw<7.1, and relative, weak-motion site terms are also in agreement. Although both approaches observe a modest increase in apparent stress with depth, the overall trend in apparent stress increasing with magnitude is supported by our findings. Finally, upon comparison with other regions, we find that the absolute apparent stress values from Ridgecrest are comparable to central Italy but significantly lower than both eastern Canada and the United Kingdom.

Root-Zone Salinity in Irrigated Arid Farmland: Revealing Driving Mechanisms of Dynamic Changes in China’s Manas River Basin over 20 Years

The risk of soil salinization is prevalent in arid and semi-arid regions, posing a critical challenge to sustainable agriculture. This study addresses the need for accurate assessment of regional root-zone soil salt content (SSC) and understanding of underlying driving mechanisms, which are essential for developing effective salinization mitigation and water management strategies. A remote sensing inversion technique, initially proposed to estimate root-zone SSC in cotton fields, was adapted and validated more widely to non-cotton farmlands. Validation results (with a coefficient of determination R2 > 0.53) were obtained using data from a three-year (2020–2022) regional survey conducted in the arid Manas River Basin (MRB), Xinjiang, China. Based on this adapted technique, we analyzed the spatiotemporal distributions of root-zone SSC across all farmlands in MRB from 2001 to 2022. Findings showed that root-zone SSC decreased significantly from 5.47 to 3.77 g kg−1 over the past 20 years but experienced a slight increase of 0.15 g kg−1 in recent five years (2017–2022), attributed to cultivated area expansion and reduced irrigation quotas due to local water shortages. The driving mechanisms behind root-zone SSC distributions were analyzed using an approach combined with two machine learning algorithms, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP), to identify influential factors and quantify their impacts. The approach demonstrated high predictive accuracy (R2 = 0.96 ± 0.01, root mean squared error RMSE = 0.19 ± 0.03 g kg−1, maximum absolute error MAE = 0.14 ± 0.02 g kg−1) in evaluating SSC drivers. Factors such as initial SSC, crop type distribution, duration of film mulched drip irrigation implementation, normalized difference vegetation index (NDVI), irrigation amount, and actual evapotranspiration (ETa), with mean (SHAP value) ≥ 0.02 g kg−1, were found to be more closely correlated with root-zone SSC variations than other factors. Decreased irrigation amount appeared as the primary driver for recent increased root-zone SSC, especially in the mid- and down-stream sections of MRB. Recommendations for secondary soil salinization risk reduction include regulation of the planting structure (crop choice and extent of planting area) and maintenance of a sufficient irrigation amount.

A non-contact quantitative risk assessment framework for translational highway landslides: Integration of InSAR, geophysical inversion, and numerical simulation

Landslides frequently disrupt highway networks in mountainous regions globally, presenting a grave threat to the safety of vehicles and pedestrians. A quantitative assessment of landslide risk within the highway network is crucial for the implementation of targeted monitoring, early warning, and engineering interventions. In this study, a non-contact quantitative risk assessment framework for translational landslides is proposed, which integrates Interferometric Synthetic Aperture Radar (InSAR), geophysical inversion, and numerical simulation techniques. The framework can reliably estimate the surface velocity, subsurface geometry, volume, and potential spatial consequences of landslides, and assess the potential damages and losses resulting from the landslide failure. Taking Lashagou L6 landslide in Linxia City, China as a case study, the results demonstrate a robust agreement between the InSAR-derived two-dimensional (2D) displacements and Global Navigation Satellite System (GNSS) observations, which are suitable for the inversion of active landslide thickness. The minimum mean error between the inverted landslide thickness and the observed borehole thickness is 0.8 m. The maximum thickness of the inverted basal sliding surface is 4.1 m, corresponding to a landslide volume of 1.25 × 104 m3. Fully considering the uncertainties within the technical framework, an estimated 1.2–1.7 vehicles are projected to be impacted, resulting in 5.3–7.7 casualties. The proposed non-contact framework demonstrates high reliability and considerable application versatility, particularly beneficial in inaccessible mountainous regions.

Dual Photonics Probing of Nano- to Submicron-Scale Structural Alterations in Human Brain Tissues/Cells and Chromatin/DNA with the Progression of Alzheimer's Disease.

Understanding alterations in structural disorders in tissue/cells/building blocks, such as DNA/chromatin in the human brain, at the nano to submicron level provides us with efficient biomarkers for Alzheimer's detection. Here, we report a dual photonics technique to detect nano- to submicron-scale alterations in brain tissues/cells and DNA/chromatin due to the early to late progression of Alzheimer's disease in humans. Using a recently developed mesoscopic light transport technique, fine-focused nano-sensitive partial wave spectroscopy (PWS), we measure the degree of structural disorder in tissues. Furthermore, the chemical-specific inverse participation ratio technique (IPR) was used to measure the DNA/chromatin structural alterations. The results of the PWS and IPR experiments showed a significant increase in the degree of structural disorder at the nano to submicron scale at different stages of AD relative to their controls for both the tissue/cell and DNA cellular levels. The increase in the structural disorder in cells/tissues and DNA/chromatin in the nuclei can be attributed to higher mass density fluctuations in the tissue and DNA/chromatin damage in the nuclei caused by the rearrangements of macromolecules due to the deposition of the amyloid beta protein and damage in DNA/chromatin with the progress of AD.

A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions

This study compares the effectiveness of different methods for coal thickness identification, aiming to identify the most accurate approach and provide a reference for intelligent coalmine development. Focused on the No. 2 coal seam in a mining area in Shanxi, China, the analysis employs well log-constrained impedance inversion and seismic multi-attribute techniques. The results show that the back propagation (BP) neural network model, as part of the seismic multi-attribute approach, delivers prediction accuracy comparable to the well log-constrained inversion method. Specifically, after applying proper static corrections, a four-layer BP neural network was constructed using four optimized sensitive attributes as the input layer, achieving an error range of 0.11% to 1.36%, compared to 0.03% to 6.59% for the logging-based method. The BP neural network demonstrated strong applicability in complex geological environments. Empirical analysis further validated the BP neural network’s geological reliability and practicality in systematic coal thickness determination.

Can one hear the shape of a crystal?

Isospectrality is a general fundamental concept often involving whether various operators can have identical spectra, i.e., the same set of eigenvalues. In the context of the Laplacian operator, the famous question “Can one hear the shape of a drum?” concerns whether different shaped drums can have the same vibrational modes. The isospectrality of a lattice in d-dimensional Euclidean space Rd is a tantamount to whether it is uniquely determined by its theta series, i.e., the radial distribution function g2(r). While much is known about the isospectrality of Bravais lattices across dimensions, little is known about this question of more general crystal (periodic) structures with an n-particle basis (n≥2). Here, we ask what is nmin(d), the minimum value of n for inequivalent (i.e., unrelated by isometric symmetries) crystals with the same theta function in space dimension d? To answer these questions, we use rigorous methods as well as a precise numerical algorithm that enables us to determine the minimum multiparticle basis of inequivalent isospectral crystals. Our algorithm identifies isospectral four-, three- and two-particle bases in one, two, and three spatial dimensions, respectively. For many of these isospectral crystals, we rigorously show that they indeed possess identically the same g2(r)'s for all values of r. Based on our analyses, we conjecture that nmin(d)=4, 3, 2 for d=1, 2, 3, respectively. The identification of isospectral crystals enables one to study the degeneracy of the ground-state under the action of isotropic pair potentials. Indeed, using inverse statistical-mechanical techniques, we find an isotropic pair potential whose low-temperature configurations in two dimensions obtained via simulated annealing can lead to both of two isospectral crystal structures with n=3, the proportion of which can be controlled by the cooling rate. Our findings provide general insights into the structural and ground-state degeneracies of crystal structures as determined by radial pair information. Published by the American Physical Society 2024

Abstract 4138529: Acute Coronary Syndrome in a Patient with Situs Inversus Totalis

INTRODUCTION: Situs inversus totalis (SIT) occurs in 1 in 8000 births. 0.04% of cases involve ACS, posing a clinical challenge. We present a case of NSTE-ACS in a patient with SIT, where a universal diagnostic catheter was used to engage the coronary arteries. CASE PRESENTATION: 51-year-old Caucasian male with situs inversus totalis presented with worsening right-sided chest&right arm pain after shoveling snow. Initial examination was benign, but the EKG showed features of dextrocardia with T wave inversions in V2-V6. Laboratory results showed elevated CKMB (119.2)&up-trending troponin T 1.260 ng/ml. He was started on a Heparin drip and 81 mg of Aspirin with a plan for urgent cardiac catheterization. A coronary angiogram from the right radial with a 6 French Tiger Radial Catheter in the right anterior projection (RAO) 30 degrees showed minor luminal irregularities in the RCA, and 90% stenosis in the LAD. RCA was enganged anticlockwise and LCA clockwise. A 6 French XB 3.5 guide catheter was used to engage the LCA system, and the mid-LAD was stented. DAPT including Aspirin and Ticagrelor, was initiated. A TTE showed an EF of 25-30% with severe anterior, apical, and septal wall hypokinesis. The patient was discharged home with GDMT for coronary artery disease. DISCUSSION: CAD in dextrocardia can be challenging, requiring reversed limb and precordial leads and a right-sided ECG to show the extent of MI. Failure to recognize dextrocardia can lead to underestimating or missing MI. Typical findings include global negativity in lead I, positively deflected QRS in aVR, negative P-wave in lead II, reverse R-wave progression in precordial leads, and right-axis deviation. Our patient's EKG revealed polarity reversal in leads I and aVL, QS and rS patterns in precordial leads, and positive polarity in aVR, suggesting dextrocardia with T wave inversions, prompting further workup. Engaging the coronary arteries is challenging. We used a Tiger radial catheter with a reverse torque technique: anticlockwise for the RCA and clockwise for the LCA. Both arteries were engaged in the right anterior oblique view at 30 degrees. The cranial and caudal angulation remained the same, but the standard left anterior oblique view was switched with the right anterior oblique view and vice versa. The double inversion technique is useful during interventions when dealing with unfamiliar anatomy. It is not well-established whether dextrocardia is a risk factor for myocardial infarction.