Heterogeneous Parameters Research Articles

Data Freshness Optimization Under CAA in the UAV-Aided MECN: A Potential Game Perspective

As a promising enabler for edge intelligence, Unmanned Aerial Vehicles (UAV) have become more and more important in Mobile Edge Computing Networks (MECN), such as communication, computation, collection and control service supply. Although Age of Information (AoI) minimization is indispensable for fresh information collection and computation in the UAV-aided MECN, some attackers can launch attacks to deteriorate the availability of precious channel resources, such as revealed channel access attacks (CAAs). Moreover, recent research has not considered the system's active probability and security issues concurrently, e.g., CAA, in the average AoI minimization process. In this paper, to deal with this problem, we consider an AoI-oriented channel access problem under CAA with a game theory viewpoint. Firstly, to obtain a MECN-based AoI indicator under CAA, the system model with active probability consideration is established. Next, the channel access-based AoI minimization problem is formulated from the viewpoint of the Ordinary Potential Game (OPG). Furthermore, two algorithms called AACSD and DCASD are proposed to determine channel access strategies, by which the Nash Equilibrium (NE) solution of the OPG could be reached. Finally, experiments are conducted under homogeneous and heterogeneous parameter settings, and the simulation results evaluate the correctness and effectiveness of our proposals.

Optimal mix among PAYGO, EET and individual savings

In order to deal with the aging problem, the pension system is actively transformed into the funded scheme. However, the funded scheme does not completely replace PAYGO (Pay as You Go) scheme and there exist heterogeneous mixes among PAYGO, EET (Exempt, Exempt, Taxed) and individual savings in different countries. In this paper, we establish the optimal mix by solving a Nash equilibrium between the pension participants and the government. Given the obligatory PAYGO and EET contribution rates, the participants choose the optimal asset allocation of the individual savings and the consumption policies to achieve the objective. The results extend the ‘Samuelson-Aaron’ criterion to age-dependent preference orderings. Under the baseline model, we identify three critical ages to distinguish the multiple outcomes of preference orderings based on heterogeneous characteristic parameters. The government is fully aware of the optimal feedback of the participants. It chooses the optimal PAYGO and EET contribution rates to maximize the overall utility of the participants weighted by each cohort's population. As such, the negative population growth rate leads to the decline of the PAYGO attractiveness as well as the increase of the older cohorts' weight in the government's decision-making. The optimal mix is the comprehensive result of the two effects.

Development of an integrated model on the basis of GCMs-RF-FA for predicting wind energy resources under climate change impact: A case study of Jing-Jin-Ji region in China

Climate change mitigation and renewable resources utilization are becoming particularly urgent for socioeconomic sustainability and human survival. This study develops an integrated model (named as GCMs-RF-FA) for long-term wind energy resources prediction through incorporating multiple global climate models (GCMs), random forest (RF) and factorial analysis (FA) techniques within a general framework. GCMs-RF-FA is capable of (i) dealing with the heterogeneity of structures and parameters in multiple GCMs, (ii) addressing the nonlinear/discrete relationships and uncertainties between the variables, and (iii) identifying the individual and interactive effects of main factors on wind resources prediction. GCMs-RF-FA is then applied to Jing-Jin-Ji region for wind energy prediction under two representative emission scenarios (RCP4.5 & RCP8.5) for 80 years. Multiple validation coefficients prove that the proposed model is effective and feasible. Results reveal that the main factors affecting wind power density (WPD) prediction and their effects at different sites. Results also show that (i) the predicted wind energy value under RCP4.5 is lower than that under RCP8.5; (ii) the seasonal difference of wind energy is obvious, high in winter and spring, low in autumn and summer; (iii) in 2060, the shares of wind energy resources in Beijing, Tianjin and Hebei account for 17.8%, 11.6% and 46.1% of total energy supply, respectively.

Left ventricular entropy is a novel predictor of major adverse cardiac events (MACE) in patients with coronary atherosclerosis: a multi-center study.

To investigate the incremental prognostic value of left ventricular (LV) entropy in a large multi-center population with coronary atherosclerotic heart disease (CAD). Current risk stratification of patients with CAD is imprecise and not accurate enough. A total of 314 CAD patients who underwent cardiovascular magnetic resonance (CMR) late gadolinium enhancement (LGE) at two medical centers in China between October 2015 and July 2022 were included in this study. Additionally, the 193 patients under 3.0-T field also underwent CMR T1 mapping. LV entropy and extracellular volume (ECV) were calculated from the LGE image of LV myocardium, and major adverse cardiac events (MACEs) were analyzed. Among 314 patients, 110 experienced MACE during a median follow-up of 13months. The risk of MACE was significantly increased in the high entropy group (log-rank p < 0.001). Entropy maintained an independent association with MACE in a multivariable model including left ventricular ejection fraction (LVEF) and LGE (HR = 1.78; p = 0.001). In addition, the primary endpoint events prognostic value was significantly improved by adding LV entropy to the baseline multivariable model (C-statistic improvement: 0.785-0.818, Delong test: p = 0.001). Similarly, among 193 3.0-T field patients, adding LV entropy to the multivariable baseline model significantly improved the prognostic value of the model for MACE (C-statistic improvement: 0.820-0.898, Delong test: p = 0.004). CMR-assessed LV entropy is a powerful independent predictor of MACE in patients with CAD, incremental to common clinical and CMR risk factors, including LVEF, LGE, Native T1, and ECV. Left ventricular entropy is a powerful independent predictor of major adverse cardiac events in patients with coronary atherosclerotic heart disease, incremental to common clinical and cardiac magnetic resonance risk factors. • Left ventricular entropy, a novel cardiac magnetic resonance parameter of myocardial heterogeneity, demonstrated a robust prognostic association with major adverse cardiac events beyond guideline-based, clinical risk markers. • Entropy can have an important role in the primary prevention of major adverse cardiac events in patients with coronary atherosclerotic heart disease. • Compared with late gadolinium enhancement, extracellular volume, and native T1, entropy could be used to more comprehensively characterize the heterogeneity of left ventricular myocardium.

Comparative Assessment of the Parameters of Spatial Heterogeneity of the Social Sphere of the Far Eastern Federal District Regions

Spatial heterogeneity is an objective reality and a condition for the development of individual territories located in space. The total set of competitive advantages of territories determines the level of development of their economy and social sphere and the level of differences between individual territories. In its turn, from the standpoint of the federal district, the differences between the territories of the constituent entities determine the different positions and roles of the territories within the federal district. Federal executive authorities make decisions on the need to smooth out differences and apply smoothing mechanisms if the differences between the constituent entities of the Russian Federation are significant. In connection with the need to use smoothing mechanisms, the question arises about the measure of calculation and the magnitude of the differences, in which there is a need to apply existing or develop new smoothing mechanisms. The element of scientific novelty presented in the article is an approach to assessing the spatial heterogeneity of the parameters of the social sphere. The possibilities of using the proposed approach are considered on the example of the Far Eastern Federal District and its «new» subjects: the Republic of Buryatia and the Trans-Baikal Territory, which have been part of the Far Eastern Federal District since the end of 2018. The result allowed us to conclude that certain territories, including the «new» ones, despite comparable development conditions, demonstrate a significant lag in the parameters of social development and they require the application of additional measures of state support.

Evaluation of the inertia distribution performance for multi-machine power system

Given large-scale modern power systems with inverter-based resources, the frequency stability faces great challenges in the characteristics of the decreasing inertia for the system and the uneven distribution of the inertia. Although several metrics for system inertia performance have been developed, a more generalized inertia evaluation method is still required. This paper proposed an inertia evaluation method for a multi-machine power system considering both the inertia and disturbance distribution. To reflect the dynamic of a more general system, an equivalent homogeneous transformation (EHT) is used in the system frequency response model with heterogeneous parameters. To reveal the inertia distribution under different disturbances scenario, a general inertia (GI) is proposed to evaluate the nodal frequency stability. Finally, the effectiveness of the proposed equivalent homogeneous system and the metric is validated using two test systems: a modified 4-machine 2-area power system and the 5-area 68-bus system. Numerical simulation study reveals that the proposed method can effectively reflect the inertia spatial distribution characteristics.

On the link between shadow economy and carbon dioxide emissions: an analysis of homogeneous groups of countries

In the framework of an environmental Kuznets curve, the linkage between shadow economy and carbon dioxide (CO2) emissions was evaluated for 145 countries from 1991 to 2017. In assessing the effect of the shadow economy on CO2 emissions, we used panel quantile regression, panel fixed effects, and panel smooth transition regression as estimation methods. In addition, to deal with parameter heterogeneity, we resorted to the procedure of Lin and Ng (2012). We found two country groups that share homogeneous parameters. No environmental Kuznets curve was found for the set of all countries. Nevertheless, one was found for each of the homogeneous parameter country groups. This result supports different turning points for different groups of countries. Shadow economy contributed to reducing CO2 emissions in group 1 and aggravated it in group 2. Manufacturing was revealed to be statistically significant for the countries of group 1. Fossil fuel rents increased the CO2 emissions, mainly in group 2. Urbanization contributed to the hike of CO2 emissions in both country groups but much more intensely for group 1. Evidence of a tendency for decreasing CO2 emissions was also found, reflecting the efficiency gains over time.

Stochastic comparisons of the largest and smallest claim amounts with heterogeneous survival exponentiated location-scale distributed claim severities

Suppose X 1 , . , X n are independent survival exponentiated location-scale random variables, and I p 1 , … , I p n are independent Bernoulli random variables, independently of X i ’s, i = 1 , … , n . Let Y i = I p i X i , for i = 1 , … , n . Then, in actuarial context, Y i corresponds to the claim amount in a portfolio of heterogeneous risks. In this work, we compare the largest and smallest order statistics arising from two heterogeneous portfolios in the sense of usual stochastic order. The results obtained here are based on multivariate chain majorization with heterogeneity in different parameters, and generalize some of the results known in the literature. Some examples and counterexamples are also presented for illustrating the results established here.

Governing equation discovery based on causal graph for nonlinear dynamic systems

The governing equations of nonlinear dynamic systems is of great significance for understanding the internal physical characteristics. In order to learn the governing equations of nonlinear systems from noisy observed data, we propose a novel method named governing equation discovery based on causal graph that combines spatio-temporal graph convolution network with governing equation modeling. The essence of our method is to first devise the causal graph encoding based on transfer entropy to obtain the adjacency matrix with causal significance between variables. Then, the spatio-temporal graph convolutional network is used to obtain approximate solutions for the system variables. On this basis, automatic differentiation is applied to obtain basic derivatives and form a dictionary of candidate algebraic terms. Finally, sparse regression is used to obtain the coefficient matrix and determine the explicit formulation of the governing equations. We also design a novel cross-combinatorial optimization strategy to learn the heterogeneous parameters that include neural network parameters and control equation coefficients. We conduct extensive experiments on seven datasets from different physical fields. The experimental results demonstrate the proposed method can automatically discover the underlying governing equation of the systems, and has great robustness.

Conditional Karhunen–Loève regression model with Basis Adaptation for high-dimensional problems: Uncertainty quantification and inverse modeling

We propose a methodology for improving the accuracy of surrogate models of the observable response of physical systems as a function of the systems’ spatially heterogeneous parameter fields, with applications to uncertainty quantification and parameter estimation in high-dimensional problems. Practitioners often formulate finite-dimensional representations of spatially heterogeneous parameter fields using truncated unconditional Karhunen–Loève expansions (KLEs) for a certain choice of unconditional covariance kernel and construct surrogate models of the observable response with respect to the KLE coefficients. When direct measurements of the parameter fields are available, we propose improving the accuracy of these surrogate models by representing the parameter fields via conditional Karhunen-Loève expansions (CKLEs). CKLEs are constructed by conditioning the covariance kernel of the unconditional expansion on the direct measurements of the parameter field via Gaussian process regression, and then truncating the corresponding KLE.We apply the proposed methodology to constructing surrogate models via the Basis Adaptation (BA) method of the stationary hydraulic head response, measured at spatially discrete observation locations, of a groundwater flow model of the Hanford Site, as a function of the 1000-dimensional representation of the model’s log-transmissivity field. We find that BA surrogate models of the hydraulic head based on CKLEs are more accurate than BA surrogate models based on unconditional expansions for forward uncertainty quantification tasks. Furthermore, we find that inverse estimates of the hydraulic transmissivity field computed using CKLE-based BA surrogate models are more accurate than those computed using unconditional BA surrogate models.

Investigating the impact of environmental governance, green innovation, and renewable energy on trade-adjusted material footprint in G20 countries

The primary objective of the recent “Conference of the Parties,” such as decarbonization, cannot be attained without considering sustainable resource management. The high consumption of natural resources in exportable goods amplifies natural resource conservation challenges and accelerates negative environmental externalities. The existing literature barely addressed the factors affecting natural resource management. This study has taken the initiative to evaluate the impact of environmental governance, green innovation, and renewable energy transition on the trade-adjusted resources management (through material footprint) in G-20 countries from 1990 to 2020. The study applied the method of the moments quantile regression to address non-linearity and heterogeneous parameters. The outcomes explored that environmental governance significantly reduces material footprint across all quantiles, while reduction effects are heterogeneous from lower to higher quantiles. Besides, green innovation and renewable energy transition profoundly mitigate the resource footprint at all innovative quantiles (0.1-0.9) with different magnitude and significance levels. The results also affirm the resource-based Environmental Kuznets Curve. Similar findings are also endorsed using alternative panel estimators. Overall the study offers valuable policy implications to improve resource management by enhancing ecological governance, green technologies, and energy transition in G-20 economies.

Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization.

The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.

Diffusion heterogeneity and vascular perfusion in tumor and peritumoral areas for prediction of overall survival in patients with high-grade glioma

ObjectiveTo evaluation of diffusion heterogeneity and vascular perfusion in tumor and peritumoral areas for prognostic prediction in high-grade glioma (HGG, WHO III/IV grade). MethodsForty patients with HGG underwent diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and arterial spin labeling (ASL) MRI before operation. After normalization, the parameters were divided into diffusion heterogeneity parameters (rD, rMD, rMK, rKr, rKa) and vascular perfusion parameters (rD*, rF, rCBF). Univariate and multivariate Cox regression analyses were used to evaluate associations between overall survival (OS) and the above parameters, clinical factors, and IDH1 status. The Mann-Whitney test was used to evaluate differences in the parameters between different IDH1 states. ResultsIn the univariate Cox regression analysis, OS was significantly associated with tumor resection range, IDH1 status, tumor heterogeneity parameters (rD, rMD, rMK, rKr, rKa), and rCBF in tumor area(all p < 0.05). In addition, rD and rCBF measured in the peritumoral region were also predictors of poor OS (both p < 0.01). Multivariate Cox regression analysis indicated that rMK in the tumor area and rCBF in the peritumoral area (hazard ratio = 7.900 and 5.466, respectively, for each 0.1 increase in the normalized value) were independent predictors of OS. ConclusionThe rMK of tumor area and rCBF of peritumoral area had independent predictive value for OS in patients with HGG. Advances in knowledgeThis study explored useful imaging biomarkers from the diffusion heterogeneity and vascular perfusion of tumor and peritumoral areas in HGG, which is useful to help clinician to make precise therapeutic plans, and predict the prognostic for glioma patients.

Synchronization in networked systems with large parameter heterogeneity

Systems that synchronize in nature are intrinsically different from one another, with possibly large differences from system to system. While a vast part of the literature has investigated the emergence of network synchronization for the case of small parametric mismatches, we consider the general case that parameter mismatches may be large. We present a unified stability analysis that predicts why the range of stability of the synchronous solution either increases or decreases with parameter heterogeneity for a given network. We introduce a parametric approach, based on the definition of a curvature contribution function, which allows us to estimate the effect of mismatches on the stability of the synchronous solution in terms of contributions of pairs of eigenvalues of the Laplacian. For cases in which synchronization occurs in a bounded interval of a parameter, we study the effects of parameter heterogeneity on both transitions (asynchronous to synchronous and synchronous to asynchronous.).

Dosimetric Validation for Prospective Clinical Trial of GRID Collimator-Based Spatially Fractionated Radiation Therapy: Dose Metrics Consistency and Heterogeneous Pattern Reproducibility

Dose heterogeneity within a tumor target is likely responsible for the biologic effects and local tumor control from spatially fractionated radiation therapy (SFRT). This study used a commercially available GRID-pattern dose mudulated nonuniform radiation therapy (GRID) collimator to assess the interplan variability of heterogeneity dose metrics in patients with various bulky tumor sizes and depths. The 3-dimensional heterogeneity metrics of 14 bulky tumors, ranging from 155 to 2161 cm3 in volume, 6 to 23 cm in equivalent diameter, and 3 to 13 cm in depth, and treated with GRID collimator-based SFRT were studied. A prescription dose of 15 Gy was given at the tumor center with 6 MV photons. The dose-volume histogram indices, dose heterogeneity parameters, and peak/valley dose ratios were derived; the equivalent uniform doses of cancer cells with various radiosensitivities in each plan were estimated. To account for the spatial fractionation, high dose core number density of the tumor target was defined and calculated. Among 14 plans, the dose-volume histogram indices D5, D10, D50, D90, and D95 (doses covering 5%, 10%, 50%, 90%, and 95% of the target volume) were found within 10% variation. The dose ratio of D10/D90 also showed a moderate consistency (range, 3.9-5.0; mean, 4.4). The equivalent uniform doses were consistent, ranging from 4.3 to 5.5 Gy, mean 4.6 Gy, for radiosensitive cancer cells and from 5.8 to 6.9 Gy, mean 6.2 Gy, for radioresistant cancer cells. The high dose core number density was within 20% among all plans. GRID collimator-based SFRT delivers a consistent heterogeneity dose distribution and high dose core density across bulky tumor plans. The interplan reproducibility and simplicity of GRID therapy may be useful for certain clinical indications and interinstitutional clinical trial design, and its heterogeneity metrics may help guide multileaf-collimator-based SFRT planning to achieve similar or further optimized dose distributions.

Preoperative vascular heterogeneity based on dynamic susceptibility contrast MRI in predicting spatial pattern of locally recurrent high-grade gliomas

ObjectivesTo investigate if spatial recurrence pattern is associated with patient prognosis, and whether MRI vascular habitats can predict spatial pattern.MethodsIn this retrospective study, 69 patients with locally recurrent high-grade gliomas (HGGs) were included. The cohort was divided into intra-resection cavity recurrence (ICR) and extra-resection cavity recurrence (ECR) patterns, according to the distance between the location of the recurrent tumor and the resection cavity or surgical region. Four vascular habitats, high angiogenic tumor, low angiogenic tumor, infiltrated peripheral edema, and vasogenic peripheral edema, were segmented and vascular heterogeneity parameters were analyzed. The survival and diagnostic performance under different spatial recurrence patterns were analyzed by Kaplan–Meier and ROC. A nomogram model was constructed by regression analysis and validated by bootstrapping technique.ResultsProgression-free survival (PFS) and overall survival (OS) were longer for ICR (n = 32) than those for ECR (n = 37) (median PFS: 8 vs. 5 months, median OS: 17 vs. 13 months, p < 0.05). MRI vascular habitat analyses showed ECR had higher median relative cerebral blood volume (rCBVmedian) at each habitat than ICR (all p < 0.01). The rCBVmedian at IPE had good diagnostic performance (AUC: 0.727, 95%CI: 0.607, 0.828). The AUC of the nomogram based on MRI vascular habitats and clinical factors was 0.834 (95%CI: 0.726, 0.913) and was confirmed as 0.833 (95%CI: 0.830, 0.836) by bootstrapping validation.ConclusionsThe spatial pattern of locally recurrent HGGs is associated with prognosis. MRI vascular heterogeneity parameter could be used as a non-invasive imaging marker to predict spatial recurrence pattern.Clinical relevance statementVascular heterogeneity parameters based on MRI vascular habitat analyses can non-invasively predict the spatial patterns of locally recurrent high-grade gliomas, providing a new diagnostic basis for clinicians to develop the extent of surgical resection and postoperative radiotherapy planning.Key Points• Intra-resection cavity pattern was associated with longer progression-free survival and overall survival in locally recurrent high-grade gliomas.• Higher vascular heterogeneities in extra-resection cavity recurrence than in intra-resection cavity recurrence and the vascular heterogeneity parameters had good diagnostic performance in discriminating spatial recurrence pattern.• A nomogram model based on MRI vascular habitats and clinical factors had good performance in predicting spatial recurrence pattern.

Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review.

Sepsis, a life-threatening infection-induced inflammatory condition, has significant global health impacts. Timely detection is crucial for improving patient outcomes as sepsis can rapidly progress to severe forms. The application of machine learning (ML) and deep learning (DL) to predict sepsis using electronic health records (EHRs) has gained considerable attention for timely intervention. PubMed, IEEE Xplore, Google Scholar, and Scopus were searched for relevant studies. All studies that used ML/DL to detect or early-predict the onset of sepsis in the adult population using EHRs were considered. Data were extracted and analyzed from all studies that met the criteria and were also evaluated for their quality. This systematic review examined 1942 articles, selecting 42 studies while adhering to strict criteria. The chosen studies were predominantly retrospective (n = 38) and spanned diverse geographic settings, with a focus on the United States. Different datasets, sepsis definitions, and prevalence rates were employed, necessitating data augmentation. Heterogeneous parameter utilization, diverse model distribution, and varying quality assessments were observed. Longitudinal data enabled early sepsis prediction, and quality criteria fulfillment varied, with inconsistent funding-article quality correlation. This systematic review underscores the significance of ML/DL methods for sepsis detection and early prediction through EHR data.

An Exact Solution to the Linearized Richards Equation for Layered Media With Flexible Initial Condition

AbstractSrivastava and Yeh (1991, https://doi.org/10.1029/90WR02772) derived an exact solution to the linearized Richards equation (LRE) for two‐layer medium infiltration using the Laplace transform (LT) method with a particular initial condition assumed, making the most pioneering contribution to the derivation of exact solutions to the layered‐medium LRE (i.e., ES‐LMLREs). However, the LT method is unsuitable for deriving an ES‐LMLRE that considers either an arbitrary initial condition or an arbitrary number of layers, or both, preventing further progress in developing ES‐LMLREs. Adopting a new solution strategy, namely a conjunctive use of the variable separation method and the transfer matrix method, we develop a novel exact layered‐medium‐LRE infiltration solution, overcoming the above difficulties. First, the proposed solution is successfully validated against the Srivastava‐Yeh solution. As a feature‐demonstration example, a layered‐medium water absorption process is simulated, and our solution well captures how the heterogeneity of hydraulic parameters affects the dynamics of this process. Moreover, the proposed solution is a valuable benchmark for related numerical models.

Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics

Patient-specific finite element analysis (FEA) holds great promise in advancing the prognosis of cardiovascular diseases by providing detailed biomechanical insights such as high-fidelity stress and deformation on a patient-specific basis. Albeit feasible, FEA that incorporates three-dimensional, complex patient-specific geometry can be time-consuming and unsuitable for time-sensitive clinical applications. To mitigate this challenge, machine learning (ML) models, e.g., deep neural networks (DNNs), have been increasingly utilized as potential alternatives to finite element method (FEM) for biomechanical analysis. So far, efforts have been made in two main directions: (1) learning the input-to-output mapping of traditional FEM solvers and replacing FEM with data-driven ML surrogate models; (2) solving equilibrium equations using physics-informed loss functions of neural networks. While these two existing strategies have shown improved performance in terms of speed or scalability, ML models have not yet provided practical advantages over traditional FEM due to generalization issues. This has led us to the question: instead of abandoning or replacing the traditional FEM framework that can reliably solve biomechanical problems, can we integrate FEM and DNNs to enhance performance?In this study, we propose a synergistic integration of DNNs and FEM to overcome their individual limitations. Using biomechanical analysis of the human aorta as the test bed, we demonstrated two novel integrative strategies in forward and inverse problems. For the forward problem, we developed DNNs with state-of-the-art architectures to predict a nodal displacement field, and this initial DNN solution was then updated by a FEM-based refinement process, yielding a fast and accurate computing framework. For the inverse problem of heterogeneous material parameter identification, our method employs DNN as a regularizer of the spatial distribution of material parameters, aiding the optimizer in locating the optimal solution. In our demonstrative examples, despite that the DNN-only forward models yielded small displacement errors in most test cases; stress errors were considerably large, and for some test cases, the peak stress errors were greater than 50%. Our DNN-FEM integration eliminated these non-negligible errors in DNN-only models and was magnitudes faster than the FEM-only approach. Additionally, compared to FEM-only inverse method with errors greater than 50%, our DNN-FEM inverse approach significantly improved the parameter identification accuracy and reduced the errors to less than 1%.

Analysis of the Effect of Technological Parameters on the Properties of Transparent Conductive Oxides

It is shown that a promising method for obtaining transparent conductive oxides is spray pyrolysis, which allows to obtain films of proper quality without the use of vacuum. For the effective use of the spray pyrolysis method in order to obtain reproducible films with specified properties, it is necessary to take into account a variety of heterogeneous technological parameters that affect the quality of transparent conductive coatings. To this end, the authors have developed a model in the form of an oriented graph that displays the relationship of technological modes and quality parameters of transparent conductive coatings. The model displays parameters in the form of graph vertices, which are divided into technological and parameters characterizing the quality of transparent conductive coatings. The arcs connecting the vertices represent the properties of the coatings, and the arrows of the arcs indicate the direction from the action property to the reaction property. This model made it possible to systematize the influencing factors and offer an analytical description of the influencing parameters, reactions to them and properties of the synthesized coatings in the form of a system of differential equations. Based on the specified mathematical model of the relationship between technological modes and coating properties, a method for selecting optimal technological parameters has been developed in order to obtain transparent conductive coatings with specified properties. The technique has been tested on the example of the synthesis of a transparent conductive film of tin dioxide doped with antimony. Analysis of the factors affecting the surface resistance of coatings showed that the concentration of charge carriers significantly depends on the volume and concentration of impurities. The results of the analysis of experimental studies of the dependences of the concentration of charge carriers on the concentration of the impurity, the volume of the solution, on the thickness, the dependence of the surface resistance of the film on the thickness are presented