Continuous Inverse Research Articles

Continuous Chiral Inversion by Coupling Enzymatic Racemization with Enantioselective Fluidized Bed Crystallization

Continuous Chiral Inversion by Coupling Enzymatic Racemization with Enantioselective Fluidized Bed Crystallization

Phonon-mediated spin transport in quantum paraelectric metals

The concept of ferroelectricity is now often extended to include continuous inversion symmetry-breaking transitions in various metals and doped semiconductors. Paraelectric metals near ferroelectric quantum criticality, which we term ‘quantum paraelectric metals,’ possess soft transverse optical phonons which can have Rashba-type coupling to itinerant electrons in the presence of spin-orbit coupling. We find through the Kubo formula calculation that such Rashba electron-phonon coupling has a profound impact on electron spin transport. While the spin Hall effect arising from non-trivial electronic band structures has been studied extensively, we find here the presence of the Rashba electron-phonon coupling can give rise to spin current, including spin Hall current, in response to an inhomogeneous electric field even with a completely trivial band structure. Furthermore, this spin conductivity displays unconventional characteristics, such as quadrupolar symmetry associated with the wave vector of the electric field and a thermal activation behavior characterized by scaling laws dependent on the phonon frequency to temperature ratio. These findings shed light on exotic electronic transport phenomena originating from ferroelectric quantum criticality, highlighting the intricate interplay of charge and spin degrees of freedom.

Discrete inverse problem for a hyperbolic equation, properties of the solution to a discrete direct and auxiliary discrete problems

This paper considers the formulation of a discrete inverse problem for a hyperbolic equation. First, the continuous inverse problem is reduced to a convenient form for research. In the inverse problem, the required function is considered even. Since the Dirac delta function is present in the problem data, the structure of a generalized solution to the Cauchy problem for a hyperbolic equation is determined. The solution to the Cauchy problem for a hyperbolic equation is determined only for positive values in time, therefore the solution to the Cauchy problem for negative values in time is determined using odd continuation. After some transformations, the formulation of the continuous inverse problem is reduced to a form convenient for research. A grid domain is introduced, and for all functions in the problem statement the corresponding grid functions and a discrete analogue of the Dirac delta function are determined. Differential operators, initial conditions and additional data of the inverse problem are approximated by finite differences. Assuming that a solution to the discrete inverse problem exists, we prove the data lemma of the discrete inverse problem. In order to study the discrete inverse problem for a hyperbolic equation, a theorem on the existence and uniqueness of the discrete direct problem is proved, as well as on the properties of the solution to this discrete problem. In the course of proving the theorem, a discrete analogue of d'Alembert's formula for solving the Cauchy problem for a hyperbolic equation was obtained. The theorem on the existence of a unique solution to the auxiliary discrete problem and its properties is proved.

A local surjection theorem with continuous inverse in Banach spaces

In this paper, we prove a local surjection theorem with continuous right-inverse for maps between Banach spaces, and we apply it to a class of inversion problems with loss of derivatives.

On some generalizations of Hadamard's inversion theorem beyond differentiability

A recognized trend of research investigates generalizations of the Hadamard's inversion theorem to functions that may fail to be differentiable. In this vein, the present paper explores some consequences of a recent result about the existence of global Lipschitz continuous inverse by translating its assumptions of metric nature in terms of nonsmooth analysis constructions. This exploration focuses on continuous, but possibly not locally Lipschitz mappings, acting in finite-dimensional Euclidean spaces. As a result, sufficient conditions for global invertibility are formulated by means of strict estimators, ∗ -difference of convex compacta and regular/basic coderivatives. These conditions qualify the global inverse as a Lipschitz continuous mapping and provide quantitative estimates of its Lipschitz constant in terms of the above constructions.

Energy-Based Continuous Inverse Optimal Control.

The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energy-based model (EBM), where the observed expert trajectories are assumed to be random samples from a probability density function defined as the exponential of the negative cost function up to a normalizing constant. The parameters of the cost function are learned by maximum likelihood via an "analysis by synthesis" scheme, which iterates: 1) synthesis step: sample the synthesized trajectories from the current probability density using the Langevin dynamics via backpropagation through time and 2) analysis step: update the model parameters based on the statistical difference between the synthesized trajectories and the observed trajectories. Given the fact that an efficient optimization algorithm is usually available for an optimal control problem, we also consider a convenient approximation of the above learning method, where we replace the sampling in the synthesis step by optimization. Moreover, to make the sampling or optimization more efficient, we propose to train the EBM simultaneously with a top-down trajectory generator via cooperative learning, where the trajectory generator is used to fast initialize the synthesis step of the EBM. We demonstrate the proposed methods on autonomous driving tasks and show that they can learn suitable cost functions for optimal control.

Study and application of a continuous inversion model of coal seam gas pressure in front area of heading face

The gas pressure in front area of heading face is essential to dynamically evaluate coal and gas outburst during coal mining. In this work, a novel inversion model of gas pressure in front area of the heading face was established on premise of the hypothesis that a time-dependent zone of steady flow exists within newly exposed face. The key parameters in the inversion model were obtained based on the gas emission models and field data of gas emission rate in different times, which were used to calculate the volumes of gas emission from different sources. The results show that the percentage of gas emission from the heading face, coal wall and collapsed coal ranges from 7% to 47%, 47% to 82% and 2% to 11%, respectively. Based on the calculated volumes of gas emission and gas pressure inversion model, the gas pressure was obtained and transformed to the gas content. The absolute errors between the gas content tested and transformed in every hour is 0.4%–33%, which proved the rationality of gas pressure inversion model. Furthermore, the daily drifting footage, the radius of gas pressure boundary and the gas permeability coefficient of coal seam were confirmed to have a great effect on the result of gas pressure inversion. The inversion results verify that the speedy excavation can increase the risk of coal and gas outburst. This work produces a useful method for gas disaster prevention and control that converts the gas emission rate to an index of gas pressure within coal seam.

High‐Resolution Characterization of Excavation‐Induced Fracture Network Using Continuous and Discrete Inversion Schemes

AbstractThe Meuse/Haute‐Marne Underground Research Laboratory provides the location for the experiment designed to investigate the induced fracture network around open or sealed galleries and drifts. The objective of this study is to investigate and reconstruct the hydraulic properties and the geometry of the induced fracture network to improve the insights and validate the conceptual model of the induced fracture network due to the stress redistribution during tunnel excavations. Within the presented study, the cross‐hole responses of the pneumatic tests were analyzed in the first step with an equivalent porous media—3D travel time‐based tomographic approach. In the next step selected 2D profiles of the 3D model domain were inverted using a discrete fracture network inversion approach. The database of the tomographic analysis is based on 18 gas injection tests and 151 pressure interferences, which were recorded between nine closely spaced boreholes. The travel time‐based inversion approach allowed for the reconstruction of the 3D gas diffusivity distribution between nine boreholes with high‐resolution. The applied discrete fracture network inversion approach is based on a transdimensional Markov Chain Monte Carlo (MCMC) methodology and it operates with reversible model updates (jumps) that change the problem dimensions, that is, the number, length and position of fractures within the model domain after each iteration step. The synthesis of the results between the reconstructed 3D diffusivity tomogram and the 2D fracture tomograms improved the insights into the spatial geometry of the induced fracture network around galleries.

Dissolved Oxygen Inversion Based on Himawari-8 Imagery and Machine Learning: A Case Study of Lake Chaohu

Dissolved oxygen (DO) concentration is a widely used and effective indicator for assessing water quality and pollution in aquatic environments. Continuous and large-scale inversion of water environments using remote sensing imagery has become a hot topic in water environmental research. Remote sensing technology has been extensively applied in water quality monitoring, but its limited sampling frequency necessitates the development of a high-frequency dynamic water quality monitoring model. In this study, we utilized Lake Chaohu as a case study. Firstly, we constructed a dynamic water quality inversion model for monitoring DO concentrations using machine learning methods, with Himawari-8 (H8) satellite imagery as input data and DO concentrations in Lake Chaohu as output data. Secondly, the developed DO concentration inversion model was employed to estimate the overall grid-based DO concentration in the Lake Chaohu region for the years 2019 to 2021. Lastly, Pearson correlation analysis and significance tests were performed to examine the correlation and significance between the estimated grid-based DO concentration and the ERA5 reanalysis dataset. The results demonstrate that the Random Forest (RF) model performs best in DO concentration inversion, with a high R2 score of 0.84, and low RMSE and MAE values of 0.69 and 0.54, respectively. Compared to other models, the RF model improves average performance with a 38% increase in R2, 13% decrease in RMSE, and 33% decrease in MAE. The model accurately predicts DO concentrations. Furthermore, the inversion results reveal seasonal differences in DO concentrations in Lake Chaohu from 2019 to 2021, with higher concentrations in spring and winter, and lower concentrations in summer and autumn. The average DO concentrations in the northwest, central-south, and northeast regions of Lake Chaohu are 10.12 mg/L, 9.98 mg/L, and 9.96 mg/L, respectively, with higher concentrations in the northwest region. Pearson correlation analysis indicates a significant correlation (p < 0.01) between DO concentrations and temperature, surface pressure, latent heat flux from the atmosphere to the surface, and latent heat flux from the surface to the atmosphere, with correlation coefficients of −0.615, 0.583, −0.480, and 0.444, respectively. The results verify the feasibility of using synchronous satellites for real-time inversion of DO concentrations, providing a more efficient, economical, and accurate means for real-time monitoring of DO concentrations. This study has practical value in improving the efficiency and accuracy of water environmental monitoring.

Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China’s Diverse Latitudes

Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, up until now, there has been no proposed remote sensing fusion product that offers high spatial-temporal resolution specifically for farmland monitoring. Therefore, focusing on the demands of farmland remote sensing monitoring, identifying quantitative conversion relationships between multiple sensors, and providing high spatial-temporal resolution products is the first step that needs to be addressed. In this study, a fused Landsat 8 (L8) Operational Land Imager (OLI) and Sentinel-2 (S-2) multi-spectral instruments (MSI) data product for regional monitoring of farmland at high, mid, and low latitudes in China is proposed. Two image pairs for each study area covering different years were acquired from simultaneous transits of L8 OLI and S-2 MSI sensors. Then, the isolation forest (iForest) algorithm was employed to remove the anomalous pixels of image pairs and eliminate the influence of anomalous data on the conversion relationships. Subsequently, the adjustment coefficients for multi-source sensors at mixed latitudes with high spatial resolution were obtained using an ordinary least squares regression method. Finally, the L8-S-2 fused dataset based on the adjustment coefficients is proposed, which is suitable for different latitude farming areas in China. The results showed that the iForest algorithm could effectively improve the correlation between the corresponding spectral bands of the two sensors at a spatial resolution of 10 m. After the removal of anomalous pixels, excellent correlation and consistency were obtained in three study areas, and the Pearson correlation coefficients between the corresponding spectral bands almost all exceeded 0.88. Furthermore, we mixed the six image pairs of the three latitudes to obtain the adjustment coefficients derived for integrated L8 and S-2 data with high-spatial-resolution. The significance and accuracy quantification of the adjustment coefficients were thoroughly examined from three dimensions: qualitative and quantitative analyses, and spatial heterogeneity assessment. The obtained results were highly satisfactory, affirming the validity and precision of the adjustment coefficients. Finally, we applied the adjustment coefficients to crop monitoring in three latitudes. The normalized difference vegetation index (NDVI) time-series curves drawn by the integrated dataset could accurately describe the cropping system and capture the intensity changes of crop growth within the high, middle, and low latitudes of China. This study provides valuable insights into enhancing the application of multi-source remote sensing satellite data for long-term, continuous quantitative inversion of surface parameters and is of great significance for crop remote sensing monitoring.

Minimal soil disturbance combined with spring cropping can halt soil seedbank accumulation of Alopecurus myosuroides

AbstractThe basic mechanism of soil inversion tillage for control of annual weeds is based on the vertical translocation of weed seeds from the soil surface to deeper soil layers. Buried weed seeds either remain dormant in the soil seedbank and are exposed to biological and chemical decay mechanisms, or they germinate but the seedlings cannot reach the soil surface (fatal germination). However, depending on the seed biology of the respective target species, frequent inversion tillage can lead to a build‐up of the soil seedbank. For soil seedbank depletion based on available knowledge of the biology of Alopecurus myosuroides seeds, soil inversion tillage is suggested to be reduced to every third or fourth year with reduced or even no‐tillage (direct seeding) in between (rotational inversion tillage systems). Including spring crops in the crop rotation could further help dampening the population growth and hence the seed return into the seedbank. This study investigated the effect of rotational inversion tillage in combination with reduced tillage or direct seeding on the soil seedbank and population development of A. myosuroides. In a long‐term field trial, set up in 2012, these tillage strategies were compared with continuous inversion tillage in a 3‐year crop rotation with two consecutive years of winter wheat (Triticum aestivum) followed by spring barley (Hordeum vulgare). The results showed a significant decline in the soil seedbank following the spring crop, irrespective of the tillage system. The continuous inversion tillage system and inversion tillage before spring cropping with reduced tillage (shallow tillage with a disc harrow) before winter wheat both led to accumulation of seeds in the soil seedbank. In contrast, inversion tillage before spring cropping with direct seeding of winter wheat depleted the soil seedbank significantly after only one crop rotation. Although only covering one intensively studied field site, these findings highlight the need for diversified cropping systems and indicate potential avenues for reducing soil tillage while controlling economically important weeds.

The use of medicinal plants for combating breast cancer: A comprehensive review

Breast carcinoma is a common illness among females. Various therapies, including hormone therapy, surgery, radiotherapy, chemotherapy, and targeted treatment, have been available to treat existing breast cancer. These therapies can potentially halt the development and spread of cancer, especially if the disease is at an early stage, but all these treatments have various adverse effects on human health. Cancer cells proliferate more rapidly than most normal cells, so chemotherapy is the most suitable treatment. Certain medications can cease dividing cells by destroying the cell's control center region. Other drugs can inhibit the chemical processes essential for cell division. On the contrary, because cancer is frequently identified at a late phase, treating the disease is extraordinarily challenging. Therefore, it is advisable to avoid this fatal condition from occurring. Multiple studies have revealed a continuous inverse connection between cancer and natural materials, such as plant extracts, their fractions, and active principles. These bioactive phytochemicals' have synergistic or cumulative effects in the treatment of cancer disease. This review article examined the effect of various extracts/fractions/active principles obtained from diverse plant origins against breast cancer disease. Information regarding the most commonly used plants, including Alpina galaga, Urtica dioica, Annona muricata, Rosmarinus officinalis, Ficus carica, Nigella sativa, Murraya koenigii, and Urtica dioica have been presented in this study. Owing to the information in this study, these plants exhibited anticancer activities in preclinical MCF-7 carcinoma models by decreasing cell proliferation, inducing programmed cell death, and triggering cell-cycle arrest. The information generated from this review will significantly contribute to developing knowledge of the scientific and medical communities in developing innovative breast cancer treatments.

Hybrid continuous inverse class‐F high‐efficiency power amplifier based on phase shift analysis

AbstractIn this paper, a hybrid continuous inverse class‐F high‐efficiency power amplifier based on phase shift analysis is proposed. A broadband and high‐efficiency microwave power amplifier are designed by adopting load pull, source pull technique, and low‐pass filter matching structure. The measured results show that the drain efficiency ranges from 67.5% to 77.8%, the saturated output power ranges from 40.2 to 42.4 dBm, and the saturated power gain ranges from 10.2 to 12.4 dB in the working frequency band from 2 to 3 GHz. The power amplifier proposed in this paper fits the current broadband microwave communication technology and plays an important role in the transmission of high data flow signals by wireless transmitters.

Determination of the right-hand side in elliptic equations

ABSTRACT The problem of determining a term in the right-hand side of elliptic equations from an observation on a part of the boundary is investigated. The inverse problem is formulated as an operator equation and then stabilized by Tikhonov regularization method. The regularized problem is discretized based on Hinze's variational discretization concept and the regularization parameter is chosen guaranteeing that when noise level and the discretization mesh size tend to zero, the solution of the discretized regularized problem converges to the f ∗ -minimum norm solution of the continuous inverse problem. Some numerical examples are presented for illustrating the performance of the proposed method.

Spatiotemporally Continuous Reconstruction of Retrieved PM2.5 Data Using an Autogeoi-Stacking Model in the Beijing-Tianjin-Hebei Region, China

Aerosol optical depth (AOD) observations have been widely used to generate wide-coverage PM2.5 retrievals due to the adverse effects of long-term exposure to PM2.5 and the sparsity and unevenness of monitoring sites. However, due to non-random missing and nighttime gaps in AOD products, obtaining spatiotemporally continuous hourly data with high accuracy has been a great challenge. Therefore, this study developed an automatic geo-intelligent stacking (autogeoi-stacking) model, which contained seven sub-models of machine learning and was stacked through a Catboost model. The autogeoi-stacking model used the automated feature engineering (autofeat) method to identify spatiotemporal characteristics of multi-source datasets and generate extra features through automatic non-linear changes of multiple original features. The 10-fold cross-validation (CV) evaluation was employed to evaluate the 24-hour and continuous ground-level PM2.5 estimations in the Beijing-Tianjin-Hebei (BTH) region during 2018. The results showed that the autogeoi-stacking model performed well in the study area with the coefficient of determination (R2) of 0.88, the root mean squared error (RMSE) of 17.38 µg/m3, and the mean absolute error (MAE) of 10.71 µg/m3. The estimated PM2.5 concentrations had an excellent performance during the day (8:00–18:00, local time) and night (19:00–07:00) (the cross-validation coefficient of determination (CV-R2): 0.90, 0.88), and captured hourly PM2.5 variations well, even in the severe ambient air pollution event. On the seasonal scale, the R2 values from high to low were winter, autumn, spring, and summer, respectively. Compared with the original stacking model, the improvement of R2 with the autofeat and hyperparameter optimization approaches was up to 5.33%. In addition, the annual mean values indicated that the southern areas, such as Shijiazhuang, Xingtai, and Handan, suffered higher PM2.5 concentrations. The northern regions (e.g., Zhangjiakou and Chengde) experienced low PM2.5. In summary, the proposed method in this paper performed well and could provide ideas for constructing geoi-features and spatiotemporally continuous inversion products of PM2.5.

Optimization design of fragment-type filtering matching network for continuous inverse class-F power amplifier

Optimization design of fragment-type filtering matching network for continuous inverse class-F power amplifier

Rotation Bounds for Hölder Continuous Homeomorphisms with Integrable Distortion

We obtain sharp rotation bounds for the subclass of homeomorphisms f:{mathbb {C}}rightarrow {mathbb {C}} of finite distortion which have distortion function in L^p_{loc}, p>1, and for which a Hölder continuous inverse is available. The interest in this class is partially motivated by examples arising from fluid mechanics. Our rotation bounds hereby presented improve the existing ones, for which the Hölder continuity is not assumed. We also present examples proving sharpness.

Inversion of airborne transient electromagnetic data based on reference point lateral constraint

The airborne transient electromagnetic method (ATEM) has become a popular tool in mineral and resource exploration due to its speed and high efficiency. The distance between two adjacent measured points is minuscule for a flight line because of the oversampling in the ATEM, which results in a sharp change in the horizontal direction. The conventional lateral constraint inversion method accumulates errors and has a significant calculation cost. Therefore, a laterally constrained, segmented inversion method based on a reference point is proposed to reduce errors and improve accuracy. Electrical information about the known logging data is employed as the lateral constraint of the reference point; alternatively, the inversion results of previous point can be used as lateral constraints, which guarantees a continuous inversion profile. The golden-section method is applied to solve the Lagrange multiplier to ensure a quick and effective convergence of inversions. Constraint inversions of a one-dimensional model show that the strategy converges quickly, and the inversion results are closer to the true resistivity of the model. Constraint inversion of a 2D model indicates that lateral constraints considerably reduce the discontinuity of a single-point inversion and smooths the inversion's resistivity pseudo-section. Finally, the effectiveness of the proposed algorithm is verified by the inversion of the airborne transient electromagnetic survey data in Xinjiang province.

Core-shell Ag@C spheres derived from Ag-MOFs with tunable ligand exchanging phase inversion for electromagnetic wave absorption

It has become greatly significant to achieve structurally tunable electromagnetic wave absorption materials (EMAs) derived from metal-organic frameworks (MOFs) via controllably continuous phase inversion. Herein, a series of core–shell Ag@C EMAs were successfully fabricated from Ag-MOFs via adjustable phase inversion. Replacing terephthalic acid (H2BDC) with 2-methylimidazole (Hmim) continuously led to the gradual transformation of Ag-MOF-5 structure into ZIF-L, which determined the crystal and morphological structure of Ag@C EMAs. In addition, due to the optimization of relaxation loss, the minimum reflection loss (RLmin) of S2 reached −50.14 dB with a thickness of 3.0 mm. The EMA derived from the original Ag-MOF had the widest absorption bandwidth (fE) of 5.44 GHz and RLmin of −47.36 dB at only 2.2 mm, respectively. This work can shed new light on the core–shell EMAs derived from phase inversion MOFs, and provide guidance to design novel high-performance EMAs.

Sequential ISAR Target Classification Based on Hybrid Transformer

To make full use of the sequential information obtained by continuous inverse synthetic aperture radar (ISAR) imaging, this article proposes a sequential ISAR target classification network based on hybrid transformer (HT). First, a temporal–spatial encoder based on the attention mechanism is designed to extract long-term and global features from sequential images. Meanwhile, a local feature encoder based on the 3-D convolution neural network is designed to extract short-term and local features. Then, the above two features are fused and the classification labels are obtained by a channel encoder–decoder. In 4-satellite target classification experiments, the proposed HT shows high accuracy and robustness to the unknown image scaling, rotation, and combined deformations.