Inverse Analysis Research Articles

Intelligent Monitoring System for Deep Foundation Pit Based on Digital Twin

Underground space development has significantly increased the depth, scale, and complexity of foundation pit engineering. However, monitoring systems lack mechanical analysis models and fail to predict and control construction risks. Additionally, the foundation pit model could not be updated based on on-site observed data, leading to inaccurate predictions. This study proposes a DT modeling framework for foundation pits, which is used to simulate, predict, and control the risks associated with the entire excavation process. Consequently, based on the DT modeling framework, a DT foundation pit model (DTFPM) was established using modeling and updating algorithms. This study summarizes and identifies the key modeling parameters of foundation pits. A parametric modeling algorithm based on ABAQUS (v2020) was developed to drive the excavation pit modeling process within seconds. Furthermore, an inverse analysis optimization algorithm based on genetic algorithms (GA) and real-time observed deformation was employed to update the elastic modulus of the soil. The algorithm supports parallel computing and can converge within 10 generations. The prediction error of the model after inverse analysis can be reduced to within 10%. Finally, the authors applied DTFPM to establish an intelligent monitoring system. The focus is on real-time and predictive warnings based on the monitoring deformation of the current construction step and the updated model. This study analyzes a Beijing project case to verify the effectiveness of the system, demonstrating the practical application of the proposed method. The results showed that the DTFPM could accurately simulate the deformation behavior of the foundation pit. The system could provide more timely and accurate safety warnings. The proposed method can potentially contribute to the intelligent construction of foundation pits in the future, both theoretically and practically.

Association of Admission Nutritional Status Evaluated by Prognostic Nutritional Index with One-Year Walking Independence after Primary Total Hip Arthroplasty for Osteonecrosis of the Femoral Head: A Retrospective Matched Cohort Study Based on 1,152 Patients.

Malnutrition is significantly associated with unfavorable outcomes, but the relevant prognostic assessment is not emphasized in patients who have osteonecrosis of the femoral head (ONFH). This study aimed to assess the association between preoperative nutritional status evaluated by the prognostic nutritional index (PNI) and walking independence at one year postoperatively in patients who have ONFH undergoing primary total hip arthroplasty (THA). This was a retrospective analysis of prospectively collected data from patients admitted to a tertiary referral hospital who had ONFH and underwent primary unilateral THA from October 30, 2014 to April 26, 2019. The restricted cubic spline was used to assess the dose-effect relationship between PNI and recovery of walking independence. Propensity score matching was performed to balance potential preoperative confounders, and multivariable conditional logistic regression analysis was applied to assess the association between malnutrition and losing walking independence (LWI) with perioperative and follow-up factors for further adjustment. Inverse probability treatment weighting and sensitivity analyses were performed to test the robustness of the results. Subgroup analyses were used to determine potential population heterogeneity. We found a negative relationship between admission PNI levels and recovery of walking independence at one year postoperatively, and that low PNI (< 42.2) was independently associated with a 2.35-fold (95% CI [confidence interval], 1.16 to 4.77; P = 0.018) increased risk of LWI. The results were overall robust. Significant heterogeneities were observed in the subgroups of age, labor intensity, American Society of Anesthesiologists class, Charlson's comorbidity index, and Association Research Circulation Osseous stage (P for interaction < 0.05). Preoperative low-level PNI is a significant risk factor for LWI after THA in patients who have ONFH. This finding supports nutritional screening and interventions for surgeons in decision-making for elective surgery and postoperative rehabilitation management.

Modulation of electron correlation dynamics in atomic non-sequential double ionization by counter-rotating two-color circularly polarized laser fields.

We investigate the ultrafast electron correlation effects during non-sequential double ionization (NSDI) of argon subjected to a combined femtosecond field composed of counter-rotating two-color circularly polarized (TCCP) pulse laser using a 3D classical ensemble model (CEM). Our simulation results reveal that manipulation of the carrier-envelope phase (CEP) of the external driving field modulates the dynamical behavior of the two electrons, resulting in a notable sensitivity of their momentum distribution to the relative phase of two components of the counter-rotating TCCP field. Through inversion analysis, we uncover the capability to direct electrons toward a single direction, thereby facilitating focused ion-electron collisions on the attosecond timescale. Furthermore, we observe that adjusting the CEP exerts a substantial influence on the NSDI ionization process, modifying the relative contributions of the recollision-induced ionization (RII) mechanism and recollision excitation with subsequent ionization (RESI) mechanism.

Monitoring phycocyanin in global inland waters by remote sensing: Progress and future developments.

Cyanobacterial blooms are increasingly becoming major threats to global inland aquatic ecosystems. Phycocyanin (PC), a pigment unique to cyanobacteria, can provide important reference for the study of cyanobacterial blooms warning. New satellite technology and cloud computing platforms have greatly improved research on PC, with the average number of studies examining it having increased from 5 per year before 2018 to 17 per year thereafter. Many empirical, semi-empirical, semi-analytical, quasi-analytical algorithm (QAA) and machine learning (ML) algorithms have been developed based on unique absorption characteristics of PC at approximately 620 nm. However, most models have been developed for individual lakes or clusters of them in specific regions, and their applicability at greater spatial scales requires evaluation. A review of optical mechanisms, principles and advantages and disadvantages of different model types, performance advantages and disadvantages of mainstream sensors in PC remote sensing inversion, and an evaluation of global lacustrine PC datasets is needed. We examine 230 articles from the Web of Science citation database between 1900 and 2024, summarize 57 of them that deal with construction of PC inversion models, and compile a list of 6526 PC sampling sites worldwide. This review proposed the key to achieving global lacustrine PC remote sensing inversion and spatiotemporal evolution analysis is to fully use existing multi-source remote sensing big data platforms, and a deep combination of ML and optical mechanisms, to classify the object lakes in advance based on lake optical characteristics, eutrophication level, water depth, climate type, altitude, population density within the watershed. Additionally, integrating data from multi-source satellite sensors, ground-based observations, and unmanned aerial vehicles, will enable future development of global lacustrine PC remote estimation, and contribute to achieving United Nations Sustainable Development Goals inland water goals.

On the quick–return motion capability of drag–link driven crank–slider mechanisms

Quick–return motion is used in machines where a tool needs to move slowly in one direction and return rapidly for efficiency. Typical applications include shaper machines, powered saws, mechanical presses, and others. It is characterized by a time ratio (TR), which compares the duration of the slower stroke to that of the faster stroke. While a higher TR indicates greater productivity, it usually corresponds with a lower motion transmission quality. Therefore, when designing a mechanism, it is essential to identify the feasible TR ranges the linkage can achieve while maintaining acceptable transmission quality. Another constraint that often limits the attainable TR is the space occupied by the mechanism. This paper explores the feasible TR ranges of the mechanism using transmission quality and space occupation as design requirements. A non-dimensional model is developed and an inverse analysis is performed to find the time ratio function. We explore the design space by varying the pressure angles in the mechanism from 0 to 90 degrees. For each pressure angle, the mechanism proportions are calculated while independently varying the slider’s offset and crank–slider’s rigid connection angle to the drag link. Sample computations are presented in charts showing the TR adjustability ranges. A design example demonstrates how the charts can help select a mechanism for a desired stroke and TR. The methodology is applied to a deep-drawing press redesign case study, resulting in a smaller mechanism. The case study demonstrates how altering the connection angle impacts the overall output velocity profile, not just the time ratio.

Refined Inversion Analysis of 3D Ground Stress Field Based on Random Field

Refined Inversion Analysis of 3D Ground Stress Field Based on Random Field

Towards constructing a generalized structural 3D breathing human lung model based on experimental volumes, pressures, and strains.

Respiratory diseases represent a significant healthcare burden, as evidenced by the devastating impact of COVID-19. Biophysical models offer the possibility to anticipate system behavior and provide insights into physiological functions, advancements which are comparatively and notably nascent when it comes to pulmonary mechanics research. In this context, an Inverse Finite Element Analysis (IFEA) pipeline is developed to construct the first continuously ventilated three-dimensional structurally representative pulmonary model informed by both organ- and tissue-level breathing experiments from a cadaveric human lung. Here we construct a generalizable computational framework directly validated by pressure, volume, and strain measurements using a novel inflating apparatus interfaced with adapted, lung-specific, digital image correlation techniques. The parenchyma, pleura, and airways are represented with a poroelastic formulation to simulate pressure flows within the lung lobes, calibrating the model's material properties with the global pressure-volume response and local tissue deformations strains. The optimization yielded the following shear moduli: parenchyma (2.8 kPa), airways (0.2 kPa), and pleura (1.7 Pa). The proposed complex multi-material model with multi-experimental inputs was successfully developed using human lung data, and reproduced the shape of the inflating pressure-volume curve and strain distribution values associated with pulmonary deformation. This advancement marks a significant step towards creating a generalizable human lung model for broad applications across animal models, such as porcine, mouse, and rat lungs to reproduce pathological states and improve performance investigations regarding medical therapeutics and intervention.

Self-Potential Inversion for Regularly Polarized Ore Deposits Anomalies in Different Models Based on Quantum Genetic Algorithm

The self-potential (SP) method is widely used in mineral exploration for its simplicity and cost-effectiveness. Traditional inversion approaches often face challenges with local optima and computational inefficiency in complex models. A quantum genetic algorithm (QGA) for SP data inversion is introduced, combining quantum computation principles with genetic algorithms. Modal analysis and parameters analysis of inversion in different physical models are conducted to determine parameter sensitivity and the optimal parameter range. The proposed optimizer is applied on synthetic data generated from diverse geological models. Gaussian noise is added to test robustness. Indoor experiments, using a controlled environment with a 2D electrode grid, validate QGA's effectiveness in layered models. The impact of data density on the algorithm's performance is evaluated by reducing the number of measurement points. Different field cases from Turkey, Germany and USA further confirmed QGA's practical applicability, with inversion results closely matching drilling data or research published before. Uncertainty appraisal analyses show the validity of the model parameter estimations. In summary, QGA significantly improves SP data inversion, offering better accuracy, efficiency, and robustness, making it a promising tool for complex geological conditions.

Association Between Serum Levels of Perfluoroalkyl andPolyfluoroalkyl Substances and Dental Floss Use:TheDouble-Edged Sword of Dental Floss Use-A Cross-Sectional Study.

Although evidence suggests that dental floss contains perfluoroalkyl and polyfluoroalkyl substances (PFASs), it is still uncertain whether the use of dental floss contributes to an increased risk of PFAS exposure. We analysed data on serum PFAS concentrations and dental floss usage in a cohort of 6750 adults who participated in the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2020. In our study, we used logistic regression, a survey-weighted linear model, item response theory (IRT) scores, inverse probability weights (IPWs) and sensitivity analysis to assess the potential impact of dental floss usage on human serum PFAS levels. The analysis of six PFASs revealed that dental floss users had higher serum concentrations of perfluorooctanoic acid (PFOA) compared with non-users, while serum concentrations of other PFASs were lower. Dental floss users recorded a lower level of overall PFAS burden score compared with non-users. Sensitivity analysis showed a statistically significant increase in serum PFOA concentration among dental floss users. Our findings suggest that the use of dental floss may be associated differently with serum concentrations of specific PFASs. Among a large representative sample of U.S. adults, individuals reporting the use of dental floss had lower levels of serum PFASs overall, with the exception of PFOA, which was slightly elevated. Dental floss is an important oral hygiene tool, and further research is needed to clarify its role in PFAS exposure.

Loading rate, geometry, and damage state influence vertical extraction biomechanics in an ex vivo swine dental model.

Validated models describing the biomechanics of tooth extraction are scarce. This study seeks to perform experimental and numerical characterization of vertical tooth extraction biomechanics in swine incisors with imposed vertical extraction loads. Imaging analysis related mechanical outcomes to tooth geometry and applied loading rate. Then, the predictive capabilities of the developed finite element analysis (FEA) models were demonstrated by testing different loading scenarios and validating the results against experimental equivalents. Simulated vertical extractions were performed on partial swine central incisors (n = 49) and studied for peak extraction force and dental complex stiffness. Post-extraction µCT images were obtained to measure root surface attachment area (RSAA) and observe patterns of periodontal ligament (PDL) rupture. Crosshead force-displacement data was used in an inverse finite element analysis (IFEA) to verify parameters for the PDL in an axisymmetric model of tooth extraction. New force-hold loading protocols were devised in silico and validated in a series of tests on swine incisors to demonstrate the predictive efficacy of the finite element model. Force-hold loading on an initially-damaged PDL was also simulated. Reductions in loading rate and RSAA were found to significantly reduce peak extraction forces by 98N-120N. Increases in instantaneous stiffness during loading were associated with increases in loading rate. Inverse finite element solutions demonstrated consistent PDL parameters across loading cases. Force-hold loading predicted extraction behaviour with large variance in extraction time. Damage imposed in the FEA model was able to predict experimental results from experiments on similarly-damaged dental complexes. This study presents a comprehensive experimental and numerical characterization of vertical tooth extraction biomechanics employing an ex vivo swine model. The results of these experiments suggest that the axisymmetric FEA model is a powerful tool for predicting a range of conditions and dental complex geometries. The predictive power of the FEA model demonstrated in this study encourages its use in pre-clinical testing and development of new vertical extraction loading schemes for improving clinical outcomes.

Genetic association between human skin microbiota with vaginitis: atwo-sample Mendelian randomization study.

The human skin microbiome is closely associated with various diseases. We aimed to find the causal association of the human skin microbiome with vaginitis. A two-way two-sample Mendelian randomization study used summary statistics of the human skin microbiota from the largest genome-wide association study meta-analysis. Pooled statistics for vaginitis disease were obtained from the FinnGen consortium R10 release. Inverse variance weighted, MR-Egger, weighted median, weighted model, MR-PRESSO, and STEIGER methods were used to test for causal associations between different parts of the human skin microbiota and vaginitis disease in either dry or moist environments. IVW estimates suggest that phylococcus hominischaracterizes a protective effect against vaginitis lesions at the Antecubitalfossa wet site. Staphylococcus in the dry state at the Dorsalforearm site also demonstrated a protective effect against vaginitis lesions. Micrococcus showed a trend of up-regulation of vaginitis risk under IVW method estimation. In contrast, Anaerococcus and Veillonella were associated with a low risk of vaginitis as estimated by the IVW method: ASV061 and ASV065. In the Class, IVW estimation showed that Bacilli at Antecubitalfossa site had an increased risk effect on vaginitis in the moist skin condition. Similarly in Genus, Staphylococcus at Antecubitalfossa sites had an increased risk effect against vaginitis in the moist skin condition. Based on the results of the inverse MR analysis, no significant causal effect of vaginitis on the human skin microbiota was found. This two-sample bidirectional Mendelian randomization study found a causal relationship between seven human skin microbiota and vaginitis, further enriching our understanding of the value of skin flora in reproductive diseases such as vaginitis.

Construction of Machine Learning Models to Predict the Maximum Absorption Wavelength Considering the Solute and Solvent and Inverse Analysis of the Models.

Fluorescent organic molecules are used in a wide range of fields, such as organic light-emitting diodes, paints, and fluorescent imaging, and they are indispensable in our daily lives. However, the development of fluorescent organic molecules is difficult because fluorescence is only exhibited by specific molecules. In addition, the optical properties of the same fluorescent organic molecule can change by changing the solvent. Therefore, to design new fluorescent organic molecules, it is necessary to predict their optical properties by considering the interaction between the solute and solvent. In this study, mathematical models of the maximum absorption wavelength obtained from the interaction between the solute and solvent were constructed by machine learning, and the solute-solvent combination with a specific maximum absorption wavelength can be predicted. In addition to the solute-solvent combinations in the data set used to construct the model, we also predicted the maximum absorption wavelengths of new solute-solvent combinations and new solute data sets.

Investigation of hygrothermal behavior of a novel bio-based panel: Experiment and numerical simulation

Straw composites, owing to their low carbon footprint and favorable hygrothermal properties, are becoming a promising alternative insulation material for buildings in order to promote energy saving and occupants’ comfort. However, the heat and moisture characteristics of straw composites at the material scale and under steady-state condition are insufficient for a thorough assessment of their performance as a building component in actual service conditions. This study focused on the hygrothermal performance of a novel bio-based wall made with a rice straw–alginate composite material. The temperature and relative humidity profiles within the wall were monitored under various boundary conditions. The inverse analysis method was proposed to determine liquid water permeability. In in a dynamic test, compared with the model of coupled heat-and-moisture transfer (CHM), the transient heat transfer model predicted temperature profiles with higher errors and underestimated total heat flux by up to 30.6%. Also, under the dynamic condition, the CHM model with liquid water transport showed decreased mean absolute errors by 61%, 57% and 8% at depths of 28 mm, 36 mm and 64 mm, respectively, compared with those predicted by the CHM model without liquid water transport. Both vapor transport and liquid transport seemed to be essential when modeling thermal transfer and moisture transfer through the wall.

Coupled differential-algebraic equations framework for modeling six-degree-of-freedom flight dynamics of asymmetric fixed-wing aircraft

This study presents a comprehensive mathematical framework for modeling the flight dynamics of a six-degree-of-freedom fixed-wing aircraft as a rigid body with three control surfaces: rudder, elevators, and ailerons. The framework consists of 35 differential-algebraic equations (DAEs) and requires 30 constants to be specified. It supports both direct and inverse flight dynamics analyses. In direct dynamics, the historical profiles of control inputs (deflection angles and engine thrust) are specified, and the resulting flight trajectory is predicted. In inverse dynamics, the desired flight trajectory and an additional constraint are specified to determine the required control inputs. The framework employs wind axes for linear-momentum equations and body axes for angular-momentum equations, incorporates two flight path angles, and provides formulas for aerodynamic force and moment coefficients. Key advantages include improved computational efficiency, elimination of Euler angle singularities, and independence from symmetry assumptions with regard to the aircraft’s moments of inertia. The model also accounts for nonlinear air density variations with altitude, up to 20 km above mean sea level, making it suitable for accurate and efficient flight dynamics simulations.

The causal role of lipids in dementia: A Mendelian randomization study

Background Increasing evidence suggests that abnormal lipid metabolism is one of the pathogeneses of dementia. It is necessary to reveal the relationship between lipids and dementia. Objective This study used bidirectional two-sample Mendelian randomization to explore the causal relationship between 179 lipid species and the risk of dementia. Methods We assessed the causal effects of 179 lipid species and four subtypes of dementia including Alzheimer's disease (AD), vascular dementia (VaD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB). Inverse variance weighting, MR-Egger method, weighted median, simple mode, and weighted mode were used to analyze the relationship between lipids and dementia. Cochran's Q, MR-Egger intercept test, and MR-PRESSO test were used to test the heterogeneity and pleiotropy of the results. In addition, we performed an inverse MR analysis testing the causal effects of dementia on lipids. Results Our study revealed causal effects of glycerophospholipid, glycerolipid, and sterol on the risk of dementia. Phosphatidylcholine, phosphatidylinositol, and triglycerides play significant roles in AD. Notably, phosphatidylcholine played a protective role in both FTD and DLB. However, this study did not observe a significant effect of phosphatidylinositol on FTD. In the case of VaD, not only glycerophospholipid, but also glycerolipid, exerted an influence, but sterol was also a risk factor. Conclusions Our study provided new evidence supporting the causal role of genetically predicted lipid species in dementia. Future clinical trials are necessary to evaluate the potential role of lipid levels in dementia prevention.

ПРИМЕНЕНИЕ ФУНКЦИОНАЛЬНО-СТОИМОСТНОГО АНАЛИЗА (ФСА) ДЛЯ ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ УПРАВЛЕНЧЕСКИХ РЕШЕНИЙ В ДЕЯТЕЛЬНОСТИ ЭНЕРГОСНАБЖАЮЩИХ ОРГАНИЗАЦИЙ

The article discusses the features of managerial decision-making in the activities of energy supply organizations. The objective of the article is to determine the functions of the decision-making process in developing a program for the innovative development of energy supply organizations. The article defines the functions of the stages of the managerial decision-making process, defines the content of subfunctions for the implementation of the functions of the stages of the managerial decision-making process, substantiates the importance of functions at different levels of management, which allows to specify the methods of implementing functions at different stages of managerial decision-making. To identify the characteristic signs of problems, it is proposed to use methods of inversion and diversionary analysis aimed at solving inverse problems and identifying hidden potential problems in the operation of an energy supply company. The article solves the fundamental problem of substantiating managerial decisions based on the allocation of process functions, which makes it possible to develop an algorithm for choosing alternatives in conditions of uncertainty and risk, as well as to determine the essence and expediency of making a managerial decision.

Inverse analysis-guided development of acid-tolerant nanoporous high-entropy alloy catalysts for enhanced water-splitting performance

Through material-driven inverse analysis, the acid-tolerant high-entropy catalyst, composed of Al, Au, Ir, Nb, Pt, Rh, Ru, and Ta, exhibited superior catalytic activity and stability in acidic environments, outperforming Pt and IrO2 catalysts.

High-Frequency Active Interrogation for Structural Damage Identification Enabled by Intelligent Hierarchical Search

A common issue in structural damage identification is the measurement information being limited while the baseline structural model is complex, which renders the inverse analysis underdetermined. Leveraging that the damage in its early stage usually affects only small area(s), a multiobjective optimization can be formulated by minimizing the difference between model prediction and physical measurement and concurrently enforcing the sparsity in damage locations. The challenges, however, lie in handling the high-dimensional parametric space with multimodal objective functions as well as the computational cost brought by incorporating the l0 norm minimization. In this research, we synthesize a hierarchical framework that searches potential damage locations first and then delves into accurate characterization of damage. In particular, to address the high-dimensional and multimodal challenges intensified by high-frequency measurement toward high-precision identification, we adopt the Group Lasso technique for sparsity induction and use the technique for order preference by similarity to ideal solution to realize optimization phase transition, aiming at improving the computational efficiency as well as the accuracy. The validation of our approach through a piezoelectric admittance sensing testbed underscores its potential for structural health monitoring practices by providing a robust, accurate, and computationally efficient approach for early-stage damage identification and assessment.

Improving Molecular Design with Direct Inverse Analysis of QSAR/QSPR Model.

Recent advances in machine learning have significantly impacted molecular design, notably the molecular generation method combining the chemical variational autoencoder (VAE) with Gaussian mixture regression (GMR). In this method, a mathematical model is constructed with X as the latent variable of the molecule and Y as the target properties and activities. Through direct inverse analysis of this model, it is possible to generate molecules with the desired target properties. However, this approach outputs many strings that do not follow the simplified molecular input line entry system grammar and generates unrealistic chemical structures in which the properties and activity do not satisfy the target values. In this study, we focus on hierarchical VAE using molecular graphs to address these issues. We confirm that the combination of hierarchical VAE and GMR does not generate invalid outputs and returns molecules that simultaneously satisfy multiple target values. Moreover, we use this method to identify several molecules that are predicted to exhibit activity against drug targets.

Rapid and simultaneous measurement of ice and unfrozen water content based on an inverse analysis surrogate model

Rapid and simultaneous measurement of ice and unfrozen water content based on an inverse analysis surrogate model