Inverse Method Research Articles

Deep learning-based geological parameterization for history matching CO2 plume migration in complex aquifers

History matching is crucial for reliable numerical simulation of geological carbon storage (GCS) in deep subsurface aquifers. This study focuses on inferring highly complex aquifer permeability fields with multi- and intra-facies heterogeneity to improve the characterization of CO2 plume migration. We propose a deep learning (DL)-based parameterization strategy combined with the ensemble smoother with multiple data assimilation (ESMDA) algorithm to formulate an integrated inverse framework. The DL model is employed to parameterize non-Gaussian permeability fields using low-dimensional latent variables in a Gaussian distribution, thereby mitigating the non-Gaussianity issue faced by the ensemble-based ESMDA inverse method and simultaneously alleviating the computational burden of high-dimensional inversion. The efficacy of the integrated DL-ESMDA inverse framework is demonstrated using a 3-D GCS model, where it estimates the non-Gaussian permeability field characterized by multi- and intra-facies heterogeneity. Results show that the DL model is able to represent the highly complex and high-dimensional permeability fields using low-dimensional latent vectors. The DL-ESMDA framework sequentially updates these low-dimensional latent vectors instead of the original high-dimensional permeability field to obtain posterior estimations of the permeability field. The resulting CO2 plume migration closely matches historical measurements, suggesting a significantly improved model reliability after history matching. Additionally, a substantial reduction in uncertainty for future plume migration predictions beyond the history matching period is observed. The proposed framework provides an effective approach for reliable characterization of CO2 plume migration in highly heterogeneous aquifers, enhancing GCS project operation and risk analysis.

Colchicine in patients with coronary disease undergoing coronary artery bypass surgery: a meta-analysis of randomized controlled trials

Abstract Background Recent randomized evidence has shown that low dose colchicine lowers the risk of cardiovascular events in patients with chronic coronary artery disease (CAD). Colchicine has also been used in coronary artery bypass grafting (CABG) with individual studies suggesting protective effects for postoperative atrial fibrillation (POAF). We performed a meta-analysis of studies assessing the effect of colchicine on outcomes in CABG surgery. Purpose To determine the potential of colchicine for reducing post-operative atrial fibrillation or other adverse events in patients undergoing CABG. Methods We systematically searched three libraries (MEDLINE, Web of Science and The Cochrane Library) selecting all randomized control trials (RCT) including patients who underwent CABG and were randomized for pre- or perioperative administration of colchicine versus standard of care. Primary outcome was incidence of POAF. Inverse variance method (DerSimonian&Laird) and random effects model were performed. The leave-one-out analysis was carried out as a sensitivity analysis to address possible outliers. Results From 205 screened studies, five met the inclusion criteria and were selected. The data from 839 patients were included in the final analysis. Included studies were published between 2014 and 2022. The perioperative administration of colchicine was associated with reduction of POAF rates after CABG when compared to standard of care (relative risk; RR= 0.54, 95% confidence interval; CI, 0.40-0.73, p<0.01 – Fig. 1). The leave-one-out analysis confirmed the robustness of the analysis, with minimal variations of the confidence interval (Fig. 2). Conclusions This meta-analysis of randomized studies suggests that the perioperative administration of colchicine is associated with significant reduction of POAF rates after CABG.Forest plotLeave-one-out analysis

Cardiovascular prevention with GLP1 agonists in high or very-high cardiovascular risk irrespective of diabetes

Abstract Introduction Patients with established cardiovascular disease have an increased risk of stroke and myocardial infarction, even in the absence of atrial fibrillation. Several trials have analyzed the effect of glucagon-like peptide-1 receptor (GLP1) agonists in patients with high cardiovascular risk and lately this effect has been studied even in the absence of diabetes. Methods A meta-analysis of randomized clinical trials (RCT) comparing GLP1 agonists with placebo was performed. The primary objective of the study was to analyze the composite endpoint of cardiovascular death, non-fatal myocardial infarction and non-fatal stroke (MACE). As secondary objectives, cardiovascular death, non-fatal myocardial infarction and non-fatal stroke have been studied separately. The meta-analysis was performed using the inverse variance method, using the fixed effects model when there was no heterogeneity and the random effects model when heterogeneity is significant. In the case of significant heterogeneity in the primary objective, a stratified analysis with exposure time have been performed to explain this outcome. Results A sum of 9 randomized controlled trials were included where a GLP1 agonist such as albiglutide, dulaglutide, exenatide, liraglutide, lixinatide or semaglutide was compared with placebo. 77684 patients were incorporated, while 39497 were treated with a GLP1 agonist. The mean age was of 63.7 years (SD ± 1.97 years), 60080 (77.3%) suffered from diabetes and 60552 (77.9%) from cardiovascular disease. As showed in figure 1, the GLP1 agonists showed a reduction of 13% of the MACE (RR: 0.87, 95% CI 0.81-0.92; p<0.001). Heterogeneity could be explained by exposure time and concentration in the blood, considering that it disappears within each group when applying this condition. Treatment with GLP1 agonist also reduced cardiovascular death by 12% (RR: 0.88, 95% CI 0.82-0.94; p<0.001), non-fatal strokes by 14% (RR: 0.86, 95%CI 0.79-0.95; p<0.001) and non-fatal miocardial infarction by 13% (RR: 0.87, 95% CI 0.78-0.97; p<0.001). Conclusion Treatment with a GLP1 agonist reduced the incidence of the MACE by 13% and cardiovascular death by 12%, as well as it decreased the incidence of nonfatal stroke by 14% and of nonfatal myocardial infarction by 13% in different RCTs involving patients with high or very-high cardiovascular risk with or without diabetes.

Heart failure and renal outcomes in patient treated with GLP1 agonists

Abstract Introduction Glucagon-like peptide-1 receptor (GLP1) agonists are emerging as a primordial treatment for diabetes and to reduce cardiovascular burden, both of them factors that play an important role in the developing of renal chronic disease and heart failure, and vice versa. The debut of renal chronic disease and the manifestation of acute renal injury/failure can lead to the discontinuation of treatment, which in patients suffering from heart failure who tend to be polymedicated could end up in lessening their survival. Methods A metanalysis of randomized clinical trials (RCT) comparing GLP1 agonists with placebo was performed. The endpoint of the study was to analyze all-cause mortality, hospitalization or urgent medical visit for heart failure, a kidney outcome composite, which is slightly heterogenic depending on the RCT, and the manifestation of acute renal injury/failure as an adverse effect or that lead to the discontinuation of treatment. The meta-analysis was performed using the inverse variance method, using the fixed effects model when there was no heterogeneity and the random effects model when heterogeneity is significant. Results A sum of 9 randomized controlled trial were included where a GLP1 agonists such as albiglutide, dulaglutide, exenatide, liraglutide, lixinatide or semaglutide compared with placebo. 77684 patients were incorporated, while 39497 were treated with a GLP1 agonists. The mean age was of 63.7 years (SD ± 1.97 years), 60080 (77.3%) suffered from diabetes, 60552 (77.9%) from cardiovascular disease and 14367 from chronic heart failure (18.4%). The mean eGFR was 77.3 mL/min (SD ± 3.3 mL/min). As showed in the forest plots, treatment with GLP1 agonists reduced all-cause mortality by 12% (RR: 0.88, 95% CI 0.83-0.92; p<0.001), hospitalization or urgent medical visit for heart failure by 10% (RR: 0.90, 95% CI 0.83-0.98; p<0.001), the kidney composite outcome by 22% (RR: 0.78, 95% CI 0.70-0.87; p<0.001), while it showed no statistical reduction in acute renal injury/failure (RR: 0.89, 95% CI 0.79-1.01; p=0.069). Conclusion Treatment with a GLP1 agonists reduced the incidence of all-cause mortality by 12%. In addition, hospitalization or urgent medical visit for heart failure was decreased by 10%. In terms of renal asessment, the composite of kidney function was reduced by 22%, while the manifestation of acute renal injury/failure, though no statistical signification was accomplished, showed some evidence of beneficial effect but more RCTs are needed to prove it. Hence, GLP1 analogues appear as a possible new pillar of treatment for heart failure and renal disease in determined patients.

Red blood cell distribution width to albumin ratio as novel prognostic biomarker in cardiovascular disease

Abstract Background The red blood cell distribution width-to-albumin ratio (RAR) is a novel, cost-effective, and accessible biomarker for inflammation. Although numerous studies have investigated the relationship between RAR values and mortality in cardiovascular diseases (CVD), the generalizability and robustness of these individual studies' results remain uncertain, underscoring the necessity for a meta-analysis. Purpose To explore the pooled association between RAR and all-cause mortality in several CVDs. Methods A systematic search for eligible studies was conducted across scientific databases, including Scopus, Web of Science, PubMed, and ProQuest, up to February 1, 2024. Mortality hazard ratios (HRs) for the highest versus lowest RAR categories were pooled using the Generic Inverse Variance method. A dose-response meta-analysis was performed using generalized least-squares regression to estimate trends of HRs across dose categories. Publication bias was assessed by inspecting funnel plots and performing Egger's test. Subgroup analyses were based on the underlying CVDs. Statistical analyses were executed in RStudio software, with the significance threshold set at p < 0.05. Results This systematic review and meta-analysis reviewed 16 high-quality studies published between 2021 and 2024 with 30,933 participants. The underlying diseases in the included studies were Acute Myocardial Infarction (AMI), Heart Failure (HF), Atrial Fibrillation (AF), aortic valvular disease, stroke and pulmonary embolism. The lowest quartile range for RAR was 0.80–1.20 ml/g, while the highest quartile ranged from 6.09 to 15.45 ml/g. Pooled results with a random-effect model showed that patients with higher RAR had significantly increased risk of all-cause mortality (HR 1.88, 95%CI 1.59–2.23, p<0.0001, I² = 91%). Non-linearity was observed in the dose-response relationship; thus, it was primarily illustrated using the restricted cubic spline model, with the linear model provided for comparison. A 1 ml/g increase in RAR corresponded to a 27% rise in the predicted HR for all-cause mortality (HR 1.27, 95%CI 1.16–1.39; p < 0.0001). Subgroup analyses based on the primary CVD diagnoses in the included studies confirmed a significant association between RAR values and all-cause mortality across all subgroups (AMI: HR 2.43, 95%CI 1.52–3.87, I² = 45%; HF: HR 1.78, 95%CI 1.13–2.81, I² = 94%; Stroke: HR 1.58, 95%CI 0.94–2.67, I² = 90%; Other CVD: HR 1.97, 95%CI 1.34–2.89, I² = 57%; p for interaction 0.24). Conclusion This is the first meta-analysis demonstrating the positive dose-response association between RAR values and all-cause mortality in CVD. Thus, RAR may be considered as a simple and practical prognostic biomarker in CVD.Forest plot based on CVD diagnosisDose-response RAR value to HR

Peer support interventions improve disease self-management after coronary heart disease diagnosis: a systematic review and meta-analysis

Abstract Background Patients with coronary heart disease (CHD) diagnoses often experience impacts on patient-reported outcomes (quality of life, depression, disease self-management and self-efficacy) at a time when secondary prevention is required. Peer support has proven benefits for these issues in many chronic conditions but synthesis of the evidence in CHD populations has not been undertaken. Purpose In this systematic review and meta-analysis, we aimed to explore the impact of peer support interventions on health-related quality of life, depression, anxiety, self-management and self-efficacy in CHD. Methods Electronic databases were searched from inception to November 2023 (MEDLINE, Embase, PsycINFO, CINAHL, Scopus, PubMed [non-MEDLINE], Web of Science, and the Cochrane Central Register of Controlled Trials). Randomised controlled trials (RCTs) evaluating peer support interventions (that enable experience sharing and/or support by others with the same condition) in populations with diagnosed CHD were eligible for inclusion. Risk of bias was assessed using the Cochrane risk of bias tool. Meta-analyses were undertaken in RevMan Version: 7.4.0 using the inverse variance method and a random effects model. Results In total, 16 papers reporting 14 unique RCTs were included. The total sample (n=1793) was 65% male with a mean age of 54.5±13.0 years. The diagnosis of the samples varied between studies, including coronary artery bypass graft(s), first time or newly diagnosed myocardial infarction and percutaneous coronary intervention. By 6-months follow-up, peer support interventions demonstrated no statistically significant improvements in health-related quality of life (SMD -0.38, 95% CI -1.84, 1.08), depression (SMD -0.09, 95% CI -0.26, 0.08) and anxiety (SMD -0.85, 95% CI -1.83, 0.14). Whereas, improvements occurred in disease self-management (SMD 1.49, 95% CI 0.66, 2.32) and self-efficacy in both short term (by 6-months, SMD 0.57, 95% CI 0.37, 0.77) and longer term (6-12 months, SMD 0.67, 95% CI 0.29, 1.05). Peer support interventions varied widely in format and several studies were of poor quality. Conclusion Peer support interventions benefit patient-reported outcomes of disease self-management and self-efficacy up to 6 and 12 months, but not for other patient reported outcomes after CHD diagnosis. More exploration is needed of content, delivery and focus of peer support interventions to maximise effectiveness for CHD secondary prevention.

Novel thick-target inverse kinematics method for the astrophysical 12C+12C fusion reaction

Novel thick-target inverse kinematics method for the astrophysical 12C+12C fusion reaction

Improving EEG Forward Modeling Using High-Resolution Five-Layer BEM-FMM Head Models: Effect on Source Reconstruction Accuracy

Electroencephalographic (EEG) source localization is a fundamental tool for clinical diagnoses and brain-computer interfaces. We investigate the impact of model complexity on reconstruction accuracy by comparing the widely used three-layer boundary element method (BEM) as an inverse method against a five-layer BEM accelerated by the fast multipole method (BEM-FMM) and coupled with adaptive mesh refinement (AMR) as forward solver. Modern BEM-FMM with AMR can solve high-resolution multi-tissue models efficiently and accurately. We generated noiseless 256-channel EEG data from 15 subjects in the Connectome Young Adult dataset, using four anatomically relevant dipole positions, three conductivity sets, and two head segmentations; we mapped localization errors across the entire grey matter from 4,000 dipole positions. The average location error among our four selected dipoles is ∼5mm (±2mm) with an orientation error of ∼12∘ (±7∘). The average source localization error across the entire grey matter is ∼9mm (±4mm), with a tendency for smaller errors on the occipital lobe. Our findings indicate that while three-layer models are robust under noiseless conditions, substantial localization errors (10–20mm) are common. Therefore, models of five or more layers may be needed for accurate source reconstruction in critical applications involving noisy EEG data.

Assessment of the Choked Flow Model of RELAP5 for Application of Inverse Quantification Methods

In the framework of deterministic safety analysis, best estimate plus uncertainty methodologies can provide a more informative detailed analysis of transients than conservative approaches. However, one important step in the application of such methodologies is derivation of uncertainty of the physical models. Probability density functions of the physical model parameters are obtained by applying inverse uncertainty quantification (IUQ) methods to experimental data. The present work deals with evaluation of the experimental database and assessment of the simulation model, which are required steps prior to the application of IUQ methods. The U.S. Nuclear Regulatory Commission system code RELAP5 has been applied for simulation of three experimental databases on choked/critical flow: Sozzi-Sutherland, Super MobyDick, and Marviken Critical Flow Tests. First, the adequacy of the experimental data to the specific target domain is carried out, to then proceed to calibration of the input model parameters by combining the use of Gaussian Process metamodels and optimization schemes. The assessment of the simulation models is performed by evaluating independently accuracy, precision, and consistency through four statistical indicators, including a novel indicator to evaluate the consistency of the results. The final results indicate that the model is capable of reproducing the selected experimental database and overall average accuracy, precision, and consistency below the 10% threshold and can be used for application of IUQ methods.

A data-based inverse problem-solving method for predicting structural orderings

A data-based inverse problem-solving method for predicting structural orderings

Upper limb exoskeleton rehabilitation robot inverse kinematics modeling and solution method based on multi-objective optimization

The inverse kinematics problem of exoskeleton rehabilitation robots is challenging due to the lack of a standard analytical model, resulting in a complex and varied solution process. This complexity is especially pronounced in redundant upper limb exoskeleton robots, where inefficient solutions hinder the robot’s ability to adapt to the kinematic shape of the upper limb. This paper proposes a modeling and solution method based on multi-objective optimization to address the inverse kinematics of upper limb exoskeleton robots. We analyzed and validated this method using a redundant upper limb exoskeleton rehabilitation robot system developed by ourselves. First, we established a multi-objective inverse kinematics solution model by defining the end-position function, joint motion comfort function, system energy consumption function, motion safety, and human-like constraints. Then, the solution was designed based on the Improved Equilibrium Optimization (IEO) algorithm and validated its computational performance in terms of optimization ability, accuracy, and robustness. Finally, we experimentally tested the inverse kinematics solution model with the IEO algorithm on discrete objectives and continuous training trajectories using a redundant upper limb exoskeleton rehabilitation robot system. The results show that by incorporating the joint comfort function, system energy consumption function, and human-like constraints into the inverse kinematics model, we can not only quickly solve the inverse kinematics of redundant upper limb exoskeleton robots but also significantly improve the robot’s motion shape. Furthermore, it has better solution accuracy and stronger robustness than other algorithms when solving this inverse kinematics model based on the IEO algorithm.

Thermal model to investigate temperature distribution with a hollow notched K-wire for bone drilling

K-wire drilling is commonly used in orthopedic surgeries, generating significant heat that can lead to bone thermal osteonecrosis. To understand the heat propagation and thermal damage region, a mathematical model of the negative rake angle on the hollow notched K-wire was established. Subsequently, a theoretical model of the shear angle for this negative rake angle was proposed. Based on these models, a comprehensive theoretical model of the heat flux was derived. The accurate heat flux was determined using the theoretical values, and experimental temperature data through inverse heat transfer method (IHTM). This heat flux was then applied in a finite element analysis to simulate the heat transfer process and identify thermal damage regions under different drilling conditions. Results showed that the thermal damage region of bone with a solid K-wire, hollow notched K-wire without cooling, with air cooling, and with water cooling were approximately 6.4 mm, 3.2 mm, 2.6 mm, and 4.8 mm in width, respectively, and 6.79 mm, 6.45 mm, 6.39 mm, and 6.58 mm in depth, respectively. This study demonstrates that the hollow notched K-wire with air cooling is a potential solution to reduce the thermal damage region, thereby accelerating patient recovery.

Average volume-assured pressure support versus fixed pressure support in chronic hypercapnic respiratory failure: a systematic review and meta-analysis

Rationale: Chronic hypercapnic respiratory failure occurs due to alveolar hypoventilation resulting in carbon dioxide retention. This is commonly managed with noninvasive ventilation (NIV) with modalities including fixed pressure support and average volume-assured pressure support (AVAPS). However, there is limited information comparing outcomes with these two modes of ventilator support in the management of chronic hypercapnic respiratory failure. Objective: This review and meta-analysis analyze the outcomes with fixed pressure NIV versus average volume-assured pressure support NIV in managing chronic obstructive pulmonary disease (COPD) with chronic hypercapnic respiratory failure, focusing on patients’ perception of symptom burden and gas exchange based on arterial blood gases. Search methods: PubMed, Embase, Cochrane Central Register of Controlled Trials, and Web of Science databases were searched; the latest search date was December 1, 2023. Inclusion criteria: randomized control trials and crossover studies in English in adults over the age of 19 with the diagnosis of COPD and chronic hypercapnic respiratory failure. Exclusion criteria: patients less than 19 years old, patients with acute exacerbations, and patients with central respiratory failure or neuromuscular disease. Outcomes included blood gas analysis after use of NIV measured in mmHg and patient perception of mental health, symptom burden, and comfort. Results for each outcome were analyzed in RevMan using an inverse variance statistical method with a fixed effect analysis. The final analysis included 7 studies with 252 participants. Results: The patients were 64 ± 9 years old. Baseline pulmonary function testing showed a forced expiratory volume in the first second (FEV1) of 34.6 ± 14.2 % predicted, consistent with severe COPD per GOLD criteria, and a baseline PaCO2 of 55.2 ± 9.2 mmHg. Primary outcomes for ventilation showed no statistical difference between AVAPS and fixed pressure support groups in PaCO2 (Odds Ratio [OR] -1.51; 95% Confidence Interval [CI]: -3.18, 0.16; p=0.08). Patient perceived outcomes were evaluated using several questionnaires, including St. George Respiratory Questionnaire (SGRQ), Short Form 36 Health Survey Questionnaire (SF-36), and Visual Analogue Scale (VAS). Comparable results were not available for all studies, but no statistically significant differences were found when comparing study results. Conclusions: There was little or no clinically significant difference between fixed pressure support and AVAPS in gas exchange. There are inadequate data to draw conclusions about the effect of fixed pressure support compared to AVAPS on patient perceived outcomes, such as comfort and symptom burden. No studies evaluated mortality benefit, cost effectiveness, or hospitalizations. Key words: COPD, chronic hypercapnic respiratory failure, non-invasive ventilation, average volume-assured pressure support ventilation

The Influence of Material Properties and Wall Thickness on Predicted Wall Stress in Ascending Aortic Aneurysms: A Finite Element Study.

Finite element analysis (FEA) has been used to predict wall stress in ascending thoracic aortic aneurysm (ATAA) in order to evaluate risk of dissection or rupture. Patient-specific FEA requires detailed information on ATAA geometry, loading conditions, material properties, and wall thickness. Unfortunately, measuring aortic wall thickness and mechanical properties non-invasively poses a significant challenge, necessitating the use of non-patient-specific data in most FE simulations. This study aimed to assess the impact of employing non-patient-specific material properties and wall thickness on ATAA wall stress predictions. FE simulations were performed on 13 ATAA geometries reconstructed from computed tomography angiography (CTA) images. Patient-specific material properties and wall thicknesses were made available from a previous study where uniaxial tensile testing was performed on tissue samples obtained from the same patients. The ATAA wall models were discretised with hexahedral elements and prestressed. For each ATAA model, FE simulations were conducted using patient-specific material properties and wall thicknesses, and group-mean values derived from all tissue samples included in the same experimental study. Literature-based material property and wall thickness were also obtained from the literature and applied to 4 representative cases. Additional FE simulations were performed on these 4 cases by employing group-mean and literature-based wall thicknesses. FE simulations using the group-mean material property produced peak wall stresses comparable to those obtained using patient-specific material properties, with a mean deviation of 7.8%. Peak wall stresses differed by 20.8% and 18.7% in patients with exceptionally stiff or compliant walls, respectively. Comparison to results using literature-based material properties revealed larger discrepancies, ranging from 5.4% to 28.0% (mean 20.1%). Bland-Altman analysis showed significant discrepancies in areas of high wall stress, where wall stress obtained using patient-specific and literature-based properties differed by up to 674kPa, compared to 227kPa between patient-specific and group-mean properties. Regarding wall thickness, using the literature-based value resulted in even larger discrepancies in predicted peak stress, ranging from 24.2% to 30.0% (mean 27.3%). Again, using the group-mean wall thickness offered better predictions with a difference less than 5% in three out of four cases. While peak wall stresses were most affected by the choice of mechanical properties or wall thickness, the overall distribution of wall stress hardly changed. Our study demonstrated the importance of incorporating patient-specific material properties and wall thickness in FEA for risk prediction of aortic dissection or rupture. Our future efforts will focus on developing inverse methods for non-invasive determination of patient-specific wall material parameters and wall thickness.

Estimating Carbon Dioxide Emissions in Two California Cities Using Bayesian Inversion and Satellite Measurements

AbstractNASA's Orbiting Carbon Observatories (OCO‐2 and OCO‐3) provide measurements of column‐averaged carbon dioxide concentrations (XCO2) with sufficient spatial resolution and precision to constrain bottom‐up estimates of CO2 fluxes at regional scales. We use Bayesian inversion methods assimilating satellite retrievals to improve estimates of CO2 fluxes in the South Coast Air Basin (SoCAB) which surrounds Los Angeles, and in the San Francisco Bay Area Air Basin (SFBA). We study 2020 to understand the impact of the COVID‐19 lockdowns and an active wildfire season. Our results indicated that a 50% (30%) reduction in CO2 emissions relative to 2015 during the COVID‐19 lockdown period was consistent with OCO measurements for SFBA (SoCAB). We find that posterior wildfire emissions differed significantly from the prior at the scale of individual wildfires, though with large uncertainties, and that wildfire emissions in SFBA are significant, attributing 72% of the region's CO2 emissions during August 2020 to wildfires.

A progress in the inverse matrix method in QCD sum rules

In traditional QCD sum rules, the simple hadron spectral density model of the “delta-function-type ground state + theta-function-type continuous spectrum” determines that there is no perfect parameter selection. In recent years, inverse problem methods, particularly the inverse matrix method, have shown better handling of QCD sum rules. This work continues to develop the inverse matrix method. Considering that the narrow-width approximation may still be a good approximation, we separate the ground state from the spectral density. We then follow the general steps of the inverse matrix method to extract physical quantities such as decay constants that we may be more interested in.

Inversion method and evolutionary behaviors of matric suction for diesel-contaminated soil

Inversion method and evolutionary behaviors of matric suction for diesel-contaminated soil

Bayesian finite element model inversion of offshore wind turbine structures for joint parameter-load estimation

Operating in harsh and unsteady marine environment, offshore wind turbine (OWT) structures are exposed to unpredictable wind and wave loads. Identifying the structural loads and their effects on the OWTs allow for predicting the remaining fatigue life of these structures and improving the structural design procedure. In this paper, a finite element (FE) model inversion method is presented to estimate the unknown loads and model parameters of OWTs using sparse measurement data. A realistic FE model of an OWT structure with jacket substructure is created in the open-source simulation platform, OpenSees. A Bayesian inference framework is presented to integrate the measured data with the FE model to estimate unknown wind loads and mass of rotor-nacelle assembly. To evaluate the performance of this data assimilation framework, the effect of sensor type, number of sensors, and modeling errors on the estimation accuracy of wind loads and model parameters are investigated through different case studies where synthetic data are used as measurements. The results of this study are important to guide instrumentation of new OWT structures, and to understand the potential limitations and sources of error in the real-world application of this data assimilation framework for joint model parameter and input load estimation.

Bayesian model averaging and Bayesian inference-based probabilistic inversion method for arch dam zonal material parameters

Inversion analysis based on structural monitoring data is an important part of evaluating the working behaviour of structures. In this paper, a probabilistic inversion method for the elastic modulus of concrete in arch dams based on Bayesian model averaging (BMA) and Bayesian inference is proposed. According to the measured displacement data of an arch dam project, the influence of the separation accuracy of water pressure component, the inversion method and the selection of displacement measuring points on the inversion results of the elastic modulus of the dam body is studied. Example analysis results show that the accuracy of the multi-measurement point displacement and hydraulic pressure component separation model based on BMA-HST (Hydrostatic-seasonal-time) is at least 18.97 % higher than that of the single measurement point, and the relative error of the elastic modulus value of the multi-measurement point zonal inversion based on the Polynomial chaos-Kriging (PCK) proxy model and the Bayesian inference is only 6 % at the maximum, which indicates that the inversion model described in this paper is able to realize the high-precision inversion analysis of the material parameters of the zonal analysis of concrete dams at a relatively low computational cost.

Compact Metasurface Terahertz Spectrometer

AbstractThe terahertz frequency region of the electromagnetic spectrum is crucial for understanding the formation and evolution of galaxies and stars throughout the universe's history, as well as the process of planet formation. Detecting the unique spectral signatures of molecules and atoms requires terahertz spectrometers, which must be operated in space observatories due to water vapor absorption in the Earth's atmosphere. However, current terahertz spectrometers face challenges such as low resolution, limited bandwidth, large volume, and complexity. In this paper, the issues of size and weight are addressed by demonstrating a concept for a centimeter‐sized, low‐weight terahertz spectrometer using a metasurface. The design of the metasurface spectrometer is first discussed for the 1.85 to 2.4 THz range, followed by its fabrication. Next, an array of quantum cascade lasers operating at slightly different frequencies around 2.1 THz is utilized to characterize the spectrometer. Finally, a spectrum inversion method is applied to analyze the measured data, confirming a resolution R (λ/Δλ) of at least 273. This concept can be extended to other application areas, such as planetary observations and various wavelengths in the far‐infrared (FIR) and near‐infrared (NIR) ranges.