Inversion Process Research Articles

TiO2-embedded dual-layer mullite hollow fiber membranes for efficient removal of Persistent organic pollutants from wastewater

Persistent organic pollutants (POPs) are toxic pollutants that harm the environment and ecosystems. This study presents a novel approach to develop a dual-layer hollow fiber ceramic membrane for phenolic compound removal. A co-extrusion-based phase inversion process and co-sintering techniques were employed to fabricate the membrane, and the effects of TiO2 loadings on the outer layer were investigated. Characterization techniques, including FTIR, SEM, EDX, and zeta potential analysis, were used to analyze the mullite and TiO2 powders and membranes. The membrane’s performance was evaluated through rejection tests of POPs at various concentrations (10, 500, and 1000 ppm) and fouling assessments were conducted. The results showed that the M−MT0.6 membrane, with a mean pore size of 0.0245 μm, effectively rejected benzoic acid (81.45 %), gallic acid (91.45 %), and hydroquinone (74.49 %) from wastewater, achieving the highest permeate flux (1320.50 L/m2·h) for gallic acid at 10 ppm. Additionally, M−MT0.6 exhibited minimal fouling over a 1-h filtration cycle, with the lowest fouling rate (35.1 %) recorded at 1000 ppm for BA, attributed to effective backwashing. However, higher fouling rates were observed at 10 ppm for BA (90.10 %) and HQ (83.50 %). Overall, this study demonstrates the potential of the novel membrane for effective removal of POPs from industrialwastewater.

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study

Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone (>20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>20\\%$$\\end{document}) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000–8500 m/s∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$*$$\\end{document} g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires 88%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88\\%$$\\end{document} more time than the latter.

Divergence of Critical Fluctuations on Approaching Catastrophic Phase Inversion in Turbulent Emulsions.

Catastrophic phase inversion, the breakdown of a concentrated emulsion characterized by the most puzzling sudden feature, is crucial in numerous industrial applications. Here we combine well-controlled experiments and fully resolved numerical simulations to study the critical dynamics of catastrophic phase inversion in oil-water emulsions under turbulent flow as the phase-inversion volume fraction is approached. We reveal that the phase inversion is characterized by the critical power-law divergence of fluctuations in the global drag force. We determine the enhanced dynamical heterogeneity in the local droplet structures at approaching the phase inversion, and tightly connect it to the diverging drag fluctuations. Moreover, we show that near to the critical point the phase inversion is triggered as a stochastic process by large fluctuations at both large and small scales. Our findings pave the way to modeling the phase inversion process as an out-of-equilibrium critical-like phenomena.

DermCompressNet: integrated CD-ConvNet and discrete cosine transform for dermoscopic images compression

AbstractTelemedicine has a critical role in healthcare by supporting the information exchange between the patients and the physicians as well as between the physicians for consultation. Such technology has urgent requirements for storage reduction and efficient use of the transmission channel bandwidth. Such requirements can be achieved efficiently via developing accurate medical image compression techniques. For accurate diagnosis, lossless compression methods are recommended. However, the tradeoff between the compression ratio (CR) and the preservation of the image quality is still challenging. On the other hand, the advantages of the convolutional neural networks inspired this work to design a novel proposed system for dermoscopic image compression based on the integration of the compression direction-ConvNet (CD-ConvNet) and decompression direction-ConvNet (DD-ConvNet) with discrete cosine transform (DCT) and Huffman coding, called DermCompressNet. To reconstruct a high-quality image at the receiver, the inverse processes using the DD-ConvNet network were followed. The proposed system was evaluated by measuring several image quality metrics, namely the mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), along with compression quality metrics, namely CR, and computational time (CT). The experimental results achieved 34.6 dB, 2.5, 0.85, and 56% of PSNR, MSE, SSIM, and CR, respectively. A comparison studies with the JPEG and state-of-the-art methods proved the superiority of the proposed system, showing23%, 16%, 4.3%, and 1.8% improvements in the PSNR, MSE, SSIM, and CR, respectively, compared to the JPEG.

Versatile performance of hydrophilic PVDF/CuO nanocomposite membranes for TDS removal in RO reject

ABSTRACT In the current study, by using the phase inversion process, PVDF/CuO nanocompositmembranes with diversified concentrations of copper oxide (CuO) nanoparticles were fabricated to reduce the TDS in RO reject. Consequently, compared to pure PVDF membrane, the hydrophilicity of PVDF membrane bearing 2 wt% of chemically synthesized CuO nanoparticles is higher. This was justified through the contact angle which diminished from 110.9° to 58.2°. The improved hydrophilicity, porosity, and pore size are the vital drivers for the fabricated nanocomposite membranes to exhibit an 81% surge in pure water flux and a 75% rise in permeate flux. Fouling of PVDF/CuO nanocomposite membranes was mitigated by incorporating CuO nanoparticles, decreasing the surface roughness. The antifouling capabilities of the fabricated nanocomposite membranes are enhanced by a higher flux recovery ratio of 97.28. Additionally, the outcomes demonstrated that the nanocomposite membrane performs better in TDS removal, with 89% efficiency in RO reject. Thus, the PVDF/CuO nanocomposite membrane has great prospects in applications necessitating the filtration process.

Onco-Ontogeny of Squamous Cell Cancer of the First Pharyngeal Arch Derivatives.

Head and neck squamous cell carcinoma (H&NSCC) is an anatomic, biological, and genetic complex disease. It involves more than 1000 genes implied in its oncogenesis; for this review, we limit our search and description to the genes implied in the onco-ontogeny of the derivates from the first pharyngeal arch during embryo development. They can be grouped as transcription factors and signaling molecules (that act as growth factors that bind to receptors). Finally, we propose the term embryo-oncogenesis to refer to the activation, reactivation, and use of the genes involved in the embryo's development during the oncogenesis or malignant tumor invasion and metastasis events as part of an onco-ontogenic inverse process.

Assessment of the effects of winter wheat scattering on SAR backscatter for soil moisture estimation based on a radiative transfer model

ABSTRACT This study evaluated the potential of the Single Scattering Radiative Transfer (SSRT) model coupled with the Oh model to retrieve surface soil moisture (SSM) in two drip-irrigated wheat fields in the Tensift al Haouz plain, over three growing seasons (2016–2019). This research focuses on evaluating isotropic scattering and Rayleigh scattering by calibrating the coupled SSRT_Oh model, using data gathered on the calibrated field. The data includes measured SSM at 5 cm depth, Leaf Area Index (LAI) and vegetation height (H). The aim is to fit the extinction coefficient through comparing simulated and Sentinel-1 backscatter, at VV and VH polarizations. The validated field data were used to retrieve SSM. Calibration revealed the best retrieval for Rayleigh scattering, at VV polarization, with an RMSE of 1.25 dB, a correlation coefficient (r) of 0.86, and a bias of 0.1 dB, whereas, the isotropic approach yielded r, RMSE and bias values of 0.83, 1.37 dB and −0.01 dB, respectively. Rayleigh’s performance remained unchanged throughout the inversion process for SSM retrieval, at VV polarization, with r of 0.87 against 0.86 for isotropic scattering. To enhance our findings, the introduction of dynamic extinction parameter and exclusive use of Sentinel-1 data to describe vegetation could improve SSM retrieval.

Inverse muon decay in a circularly polarized laser field

In this paper, we investigate the effect of a circularly polarized laser field on the inverse muon decay process [Formula: see text] in electroweak theory with the exchange of a boson vector [Formula: see text]. Our method accounts for the dressing of incident electron and scattered muon by introducing the first-order laser-matter interaction of perturbation theory and using the relativistic Dirac–Volkov formalism. We have analyzed the behavior of the total cross-section (TCS) with charged-current as a function of the center-of-mass energy and the laser field parameters, such as the number of photons exchanged, the laser field strength and frequency. We have found that the circularly polarized laser field significantly affects the inverse muon decay process by reducing the TCS. We have also shown that the photonic energy transfer envelopes are perfectly symmetric with respect to the number of photons exchanged and rapidly drop when the indices of the Bessel functions are close to their arguments.

Seismic Facies-Controlled Porosity Prediction in a Tight Sandstone Reservoir Based on the XGBoost Algorithm

Porosity is one of the most important properties for evaluating hydrocarbon reservoirs. There is a strong nonlinearity between seismic data and rock physical properties. Machine learning methods possess the powerful ability to capture complex nonlinear relationships between these two parameters, holding significant potential for porosity prediction in tight sandstone reservoirs. In this study, we propose a seismic facies-controlled porosity prediction method based on the extreme gradient boosting algorithm. Through seismic meme inversion method,we obtain high-resolution P-impedance data. In the inversion process, lateral variations of seismic waveforms were utilized instead of the variogram function. We create labels for model training using porosity curves and borehole side P-impedance data, applying them to the extreme gradient boosting regressor. To demonstrate its feasibility, we apply the model to a real 3D case from an oil field in the Songliao Basin. Our application results demonstrate that this method can provide porosity predictions for tight sandstone reservoirs, maintaining consistency with seismic waveforms and adhering to seismic facies control patterns. The method uses only acoustic travel time, density, gamma rays, and spontaneous potential well-log data and post-stack 3D seismic data as inputs, allowing for quick porosity predictions in the field. Compared to the seismic facies-controlled inversion combined with support vector machine and multi-Layer perceptron methods, the method proposed in this study provides more reliable porosity predictions, offering insights and references for the exploration and development of tight sandstone reservoirs.

Cross-gradient joint inversion and clustering of ERT and SRT data on structured meshes incorporating topography

Summary The combination of electrical resistivity and seismic refraction tomography is a common practice for the characterization of subsurface features. Presently, the cross-gradient inversion scheme stands out as one of the most robust joint approaches, and some authors modified it to manage complex topographies on unstructured meshes even if at the expense of introducing additional parameters in the inversion process. We propose in this work a cross-gradient algorithm for jointly inverting electrical and seismic tomographic data on structured meshes in cases with non-flat topography. The proposed approach preserves the benefit of the classical cross-gradient approach without the need to impose physical length scales, as for irregular meshes. The quality of the results is evaluated in comparison with independent inversion through a new standardized cross-gradient index and a fuzzy c-means analysis that provides an assessment of the reconstruction accuracy through the membership function. The proposed method was applied to both synthetic models and field-scale examples located in Central Italy, where an accurate geophysical reconstruction is needed for the rehabilitation of existing dams. For all cases, joint inversion yielded superior results compared to independent inversion, demonstrating better agreement with available borehole data. The effectiveness of the joint approach was also demonstrated by the post-inversion tools, where the new cross-gradient index highlighted changes in structural similarity whilst fuzzy c-means clustering allowed for a quantitative reconstruction (position and shape) of the main units at the sites, facilitating the detection of site layering modifications.

Three-dimensional interpretation of magnetotelluric data at Fogo Volcano, Azores Islands

The resistivity structure of Fogo volcano and the seismically active Congro region of São Miguel Island has been determined by 3-D inversion from 44 magnetotelluric soundings to yield new insights into the internal architecture of this volcanic island. Following comprehensive testing of processing codes to yield optimum magnetotelluric responses from the collected time-series, a robust electrical resistivity model was obtained. Sensitivity analysis of various features from the inversion process was used to determine their reliability, and aid geological interpretation. The magnetotelluric data imaged, and provided new structural insights into the Ribeira Grande geothermal system on the northern flank of Fogo volcano, where a shallow low resistivity (1 - 5Ωm) region has strong correlation with borehole data, and is shown to be an excellent proxy for mapping temperature and clay alteration mineralogy. Beneath the central edifice of Fogo volcano and also throughout the Congro region, the geology is very resistive, however the new magnetotelluric observations do not yield any further constraints on the origin of the seismicity that poses threats to the local populations residing on this hazardous island.

Multivariate Stochastic Volatility with Co-Heteroscedasticity

Abstract A new methodology that decomposes shocks into homoscedastic and heteroscedastic components is developed. This specification implies there exist linear combinations of heteroscedastic variables that eliminate heteroscedasticity; a property known as co-heteroscedasticity. The heteroscedastic part of the model uses a multivariate stochastic volatility inverse Wishart process. The resulting model is invariant to the ordering of the variables, which is shown to be important for volatility estimation. By incorporating testable co-heteroscedasticity restrictions, the specification allows estimation in moderately high-dimensions. The computational strategy uses a novel particle filter algorithm, a reparameterization that substantially improves algorithmic convergence and an alternating-order particle Gibbs that reduces the amount of particles needed for accurate estimation. An empirical application to a large Vector Autoregression (VAR) is provided, finding strong evidence for co-heteroscedasticity and that the new method outperforms some previously proposed methods in terms of forecasting at all horizons. It is also found that the structural monetary shock is 98.8 % homoscedastic, and that investment and the SP 500 index are nearly 100 % determined by fat tail heteroscedastic shocks. A Monte Carlo experiment illustrates that the new method estimates well the characteristics of approximate factor models with heteroscedastic errors.

Integrated seismic, petrophysical, and geochemical studies for evaluating the petroleum system of the Upper Bahariya-Abu Roash G sequence in the Karama Field, Abu Gharadig Basin, North Western Desert, Egypt

The main aim of this study is to delineate the hydrocarbon potential and evaluate the petroleum system elements of the Cenomanian Abu Roash G (AR/G) and the Upper Bahariya Members in the Karama Field. It lies at the southeast borders of the Abu Gharadig Basin, a well-known basin in the W.D. to the NE of Africa. We accomplish this study by analyzing a total of thirty 2D seismic profiles, a complete data set of well logs for five wells, and their geochemical data. The workflow starts with illustrating the dominant subsurface structural features, defining the main potential reservoirs and their parameters, and checking the maturity of the probable source rocks. The seismic interpretation indicated that the research area had been influenced by a NE-SW anticlinal structure accompanied by a set of WNW-ESE and NW-SE normal faults that are controlled by the positive compression inversion process that dominated during the Late Cretaceous. Analyzing and processing the well log data sets suggest that the reservoirs of the Abu Roash G (AR/G) and the Upper Bahariya Members are characterized by poor to good reservoir settings with net-pay thickness reaching up to 13–50 feet in the different wells (av. Effective porosity (∅eff) = 17.7 % and 15.6% for the AR/G and the Upper Bahariya Members, av. Shale volume (Vsh) = 17.4 % and 13.6 %, av. Water saturation (Sw) = 38.9 % and 39.8 %, while av. Hydrocarbon saturation values (So) = 60.2 % and 61.1%, respectively). The geochemical and maturity analyses assisted in determining the potential mature source rocks of the Jurassic Khatatba Shale (TOC = 0.70–5.67%; S1+S2 = 0.43–5.97 mg/g, Ro = 0.54–1.06 %) with some contribution from the Cretaceous sources (Alam El-Bueib and Bahariya Formations). Studying elements of the petroleum regime of the Karama Field indicates that the trapped hydrocarbons are structurally controlled by three-way dip closures, horst blocks, sealing faults, and vertical sealing by impervious shale and limestone beds. This case study could be applied to similar analogs in other oil fields in the Egyptian Western Desert to delineate their hydrocarbon potential and structural setting.

Ambulatory ECG noise reduction algorithm for conditional diffusion model based on multi-kernel convolutional transformer.

Ambulatory electrocardiogram (ECG) testing plays a crucial role in the early detection, diagnosis, treatment evaluation, and prevention of cardiovascular diseases. Clear ECG signals are essential for the subsequent analysis of these conditions. However, ECG signals obtained during exercise are susceptible to various noise interferences, including electrode motion artifact, baseline wander, and muscle artifact. These interferences can blur the characteristic ECG waveforms, potentially leading to misjudgment by physicians. To suppress noise in ECG signals more effectively, this paper proposes a novel deep learning-based noise reduction method. This method enhances the diffusion model network by introducing conditional noise, designing a multi-kernel convolutional transformer network structure based on noise prediction, and integrating the diffusion model inverse process to achieve noise reduction. Experiments were conducted on the QT database and MIT-BIH Noise Stress Test Database and compared with the algorithms in other papers to verify the effectiveness of the present method. The results indicate that the proposed method achieves optimal noise reduction performance across both statistical and distance-based evaluation metrics as well as waveform visualization, surpassing eight other state-of-the-art methods. The network proposed in this paper demonstrates stable performance in addressing electrode motion artifact, baseline wander, muscle artifact, and the mixed complex noise of these three types, and it is anticipated to be applied in future noise reduction analysis of clinical dynamic ECG signals.

Full Waveform Inversion Based on Dynamic Time Warping and Application to Reveal the Crustal Structure of Western Yunnan, Southwest China

AbstractWe develop a 3D full waveform inversion (FWI) method based on dynamic time warping (DTW) to address the issue of cycle‐skipping, which can prohibit the convergence of conventional FWI methods. DTW globally compares data samples at different time steps in 2D matrices against the time shifts of waveforms. We introduce the concept of shape descriptors into softDTW, creating a soft‐shapeDTW objective function within our waveform inversion process to improve alignment accuracy. Additionally, including constraints from Sakoe‐Chiba bands in the inversion further enhances efficiency and overall performance. A synthetic test has shown that the soft‐shapeDTW inversion outperforms conventional waveform inversions in overcoming the cycle‐skipping challenges that arise from poor initial models. This method was applied to fit observed seismograms to reveal western Yunnan's crustal structure. Seismic waveforms were recorded by 88 broadband stations from 10 local earthquakes, which were then denoised using a continuous wavelet transform method. Generalized cut and paste waveform inversions were used to determine the source parameters of these seismic events. Our inversion well‐aligned various seismic phases in the selected time windows of seismograms, and the resolved velocity models well associate with local geological structure. Results suggest that the soft‐shapeDTW inversion offers a robust alternative to FWI, reducing the reliance on accurate starting models.

A new automated procedure to obtain reliable moment tensor solutions of small to moderate earthquakes (3.0 ≤ M ≤ 5.5) in the Bayesian framework

SUMMARY The complete catalogue of moment tensor (MT) solutions is essential for a wide range of research in solid earth science. However, the number of reliable MT solutions for small to moderate earthquakes (3.0 ≤ M ≤ 5.5) is limited due to uncertainties arising from data and theoretical errors. In this study, we develop a new procedure to enhance the resolvability of MT solutions and provide more reliable uncertainty estimates for these smaller to moderate earthquakes. This procedure is fully automatic and efficiently accounts for both data and theoretical errors through two sets of hybrid linear–non-linear Bayesian inversions. In the inversion process, the covariance matrix is estimated using an empirical approach: the data covariance matrix is derived from the pre-event noise and the theoretical covariance matrix is derived from the residuals of the initial solution. We conducted tests using synthetic data generated from the 3-D velocity model and interference from background seismic noise. The tests found that using a combination of the non-Toeplitz data covariance matrix and the Toeplitz theoretical covariance matrix improves the solution and its uncertainties. Test results also suggest that including a theoretical covariance matrix when analysing MT in complex tectonic regions is essential, even if we have the best 1D velocity model. The application to earthquakes in the northern region of the Banda Arc resulted in the first published Regional Moment Tensor (RMT) catalogue, containing more than three times the number of trusted solutions compared to the Global Centroid Moment Tensor (GCMT) and the Indonesian Agency for Meteorology Climatology and Geophysics Moment Tensor (BMKG-MT) catalogue. The comparison shows that the trusted solutions align well with the focal mechanism of the GCMT and BMKG-MT, as well as with the maximum horizontal stress of the World Stress Map, and tectonic conditions in the study area. The newly obtained focal mechanisms provide several key findings: (i) they confirm that the deformation in the northern and eastern parts of Seram Island is influenced by oblique intraplate convergence rather than by the subduction process; (ii) they validate the newly identified Amahai Fault with a greater number of focal mechanisms and (iii) they reveal an earthquake Mw 4.7 with the same location and source mechanism 6 yr before the 2019 Ambon-Kairatu earthquake (Mw 6.5) which occurred on a previously unidentified fault.

Integrating 3DVAR and ANN for inversion optimization of physical models of fuel-assembly bowing

This study introduces a novel inversion optimization method for the core physical model of fuel-assembly bowing, integrating measurement data based on three-dimensional variational (3DVAR) data assimilation and artificial neural network (ANN) techniques. Recognizing the deviation between theoretical physical models and actual core conditions due to factors like uneven coolant distribution and fuel-assembly bowing, this work aims to enhance model reliability and simulation accuracy. By generating a dataset through random perturbation and employing ANN for training, the method establishes a relationship between the core physical model and model-simulated values corresponding to measurement data. The construction of the cost function, essential for optimizing the inversion process, is executed using 3DVAR. To verify its validity, the method is implemented, and an inversion optimization process is developed using our self-developed Bamboo-C code system. The optimization process, validated through application in a commercial pressurized water reactor (PWR) in a nuclear power plant (NPP) in China, demonstrates a significant reduction in the relative errors of power distribution, thus affirming the method’s efficacy in achieving a more realistic model of fuel-assembly bowing. The integration of measurement data into the optimization process addresses the critical need for accurate core physical models in nuclear engineering, offering a promising avenue for enhancing the safety and efficiency of nuclear reactor operations.

A probabilistic machine learning framework for stiffness tensor estimation of carbon composite laminate

The potential uses of carbon composite material are vast, particularly in the civil, mechanical and aero-structures. Nonetheless, the practical utilization of carbon composite faces challenges due to complex experimental methods and imprecise algorithms for inverting material properties. Characterization of full set of stiffness tensor for an anisotropic material in particular orthotropic material can be considered as a complex non-linear inversion problem. The inversion process requires a robust optimization algorithm and forward models. The complexity of this inversion process impedes the practical utility in large-scale automation and in real-time structural health monitoring (SHM) application. To cater this, in this work an advanced probabilistic machine learning framework based on multi-output Gaussian process regression (moGPR) is proposed as an inversion algorithm. The inversion algorithm proposed is based on probabilistic framework that utilises the dispersion of Lamb waves as an input for optimal estimation of stiffness tensor. Experimental measurements of full wavefield data of propagating waves are conducted by scanning laser Doppler vibrometer. Further, an optimal training dataset is generated by employing a forward model based on stiffness matrix method (SMM). In the presence of measurement uncertainties, the effectiveness of the optimal prediction algorithm is showcased through a comparison between the estimated parameter and its ground truth. This comparison reveals that the proposed inversion algorithm is both efficient and robust, delivering satisfactory performance even in the presence of substantial measurement uncertainties.

Efficient Shift-and-Invert Preconditioning for Multi-GPU Accelerated Density Functional Calculations.

To accelerate the iterative diagonalization of electronic structure calculations, we propose a new inexact shift-and-invert (ISI) preconditioning method. The key idea is to improve shift values in the ISI preconditioning to be closer to the exact eigenvalues, leading to a significant boost in the convergence speed of the iterative diagonalization. Furthermore, we adopted a preconditioned conjugate gradient solver to rapidly evaluate an inversion process. Finally, we accelerated overall processes, including the proposed modification, with state-of-the-art graphical processing units (GPUs) and assessed its parallel efficiency with real-space density functional calculations of 1D, 2D, and 3D periodic systems. Our method attains both fast diagonalization convergence and high multi-GPU parallel efficiency. This is evident from the fact that single-point density functional calculations for hundreds of atom systems can be done in approximately 10 s using 8 GPUs. The proposed method can be generally applied to any electronic structure calculation methods involving large-scale diagonalizations.

Energy-efficient dynamic 3D metasurfaces via spatiotemporal jamming interleaved assemblies for tactile interfaces

Inspired by the natural shape-morphing abilities of biological organisms, we introduce a strategy for creating energy-efficient dynamic 3D metasurfaces through spatiotemporal jamming of interleaved assemblies. Our approach, diverging from traditional shape-morphing techniques reliant on continuous energy inputs, utilizes strategically jammed, paper-based interleaved assemblies. By rapidly altering their stiffness at various spatial points and temporal phases during the relaxation of the soft substrate through jamming, we enable the formation of refreshable, intricate 3D shapes with a desirable load-bearing capability. This process, which does not require ongoing energy consumption, ensures energy-efficient and lasting shape displays. Our theoretical model, linking buckling deformation to residual pre-strain, underpins the inverse design process for an array of interleaved assemblies, facilitating the creation of diverse 3D configurations. This metasurface holds notable potential for tactile displays, particularly for the visually impaired, heralding possibilities in visual impaired education, haptic feedback, and virtual/augmented reality applications.