A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

Prediction of early recurrence of hepatocellular carcinoma after liver transplantation based on computed tomography radiomics nomogram

Background:Early recurrence results in poor prognosis of patients with hepatocellular carcinoma(HCC)after liver transplantation(LT).This study aimed to explore the value of computed tomography(CT)-based radiomics nomogram in predicting early recurrence of patients with HCC after LT.Methods:A cohort of 151 patients with HCC who underwent LT between December 2013 and July 2019 were retrospectively enrolled.A total of 1218 features were extracted from enhanced CT images.The least absolute shrinkage and selection operator algorithm(LASSO)logistic regression was used for dimension reduction and radiomics signature building.The clinical model was constructed after the analysis of clin-ical factors,and the nomogram was constructed by introducing the radiomics signature into the clinical model.The predictive performance and clinical usefulness of the three models were evaluated using re-ceiver operating characteristic(ROC)curve analysis and decision curve analysis(DCA),respectively.Cali-bration curves were plotted to assess the calibration of the nomogram.Results:There were significant differences in radiomics signature among early recurrence patients and non-early recurrence patients in the training cohort(P<0.001)and validation cohort(P<0.001).The nomogram showed the best predictive performance,with the largest area under the ROC curve in the training(0.882)and validation(0.917)cohorts.Hosmer-Lemeshow testing confirmed that the nomogram showed good calibration in the training(P=0.138)and validation(P=0.396)cohorts.DCA showed if the threshold probability is within 0.06-1,the nomogram had better clinical usefulness than the clinical model.Conclusions:Our CT-based radiomics nomogram can preoperatively predict the risk of early recurrence in patients with HCC after LT.

Survey of plasmonic gaps tuned at sub-nanometer scale in self-assembled arrays

Creating nanoscale and sub-nanometer gaps between noble metal nanoparticles is critical for the applications of plasmonics and nanophotonics. To realize simultaneous attainments of both the op- tical spectrum and the gap size, the ability to tune these nanoscale gaps at the sub-nanometer scale is particularly desirable. Many nanofabrication methodologies, including electron beam lithography, self-assembly, and focused ion beams, have been tested for creating nanoscale gaps that can de- liver significant field enhancement. Here, we survey recent progress in both the reliable creation of nanoscale gaps in nanoparticle arrays using self-assemblies and in the in-situ tuning techniques at the sub-nanometer scale. Precisely tunable gaps, as we expect, will be good candidates for future investigations of surface-enhanced Raman scattering, non-linear optics, and quantum plasmonics.