Advanced Modeling Methods Research Articles

The Healthy Brain Initiative (HBI): A prospective cohort study protocol.

The Health Brain Initiative (HBI), established by University of Miami's Comprehensive Center for Brain Health (CCBH), follows racially/ethnically diverse older adults without dementia living in South Florida. With dementia prevention and brain health promotion as an overarching goal, HBI will advance scientific knowledge by developing novel assessments and non-invasive biomarkers of Alzheimer's disease and related dementias (ADRD), examining additive effects of sociodemographic, lifestyle, neurological and biobehavioral measures, and employing innovative, methodologically advanced modeling methods to characterize ADRD risk and resilience factors and transition of brain aging. HBI is a longitudinal, observational cohort study that will follow 500 deeply-phenotyped participants annually to collect, analyze, and store clinical, cognitive, behavioral, functional, genetic, and neuroimaging data and biospecimens. Participants are ≥50 years old; have no, subjective, or mild cognitive impairment; have a study partner; and are eligible to undergo magnetic resonance imaging (MRI). Recruitment is community-based including advertisements, word-of-mouth, community events, and physician referrals. At baseline, following informed consent, participants complete detailed web-based surveys (e.g., demographics, health history, risk and resilience factors), followed by two half-day visits which include neurological exams, cognitive and functional assessments, an overnight sleep study, and biospecimen collection. Structural and functional MRI is completed by all participants and a subset also consent to amyloid PET imaging. Annual follow-up visits repeat the same data and biospecimen collection as baseline, except that MRIs are conducted every other year after baseline. HBI has been approved by the University of Miami Miller School of Medicine Institutional Review Board. Participants provide informed consent at baseline and are re-consented as needed with protocol changes. Data collected by HBI will lead to breakthroughs in developing new diagnostics and therapeutics, creating comprehensive diagnostic evaluations, and providing the evidence base for precision medicine approaches to dementia prevention with individualized treatment plans.

Finite Element Analyses of Cast-in-situ RC Flat Slab with an Equivalent Frame for Horizontal Loads

In structural construction practice, the use of reinforced concrete slabs is extremely common due to their structural and economic advantages. An important issue is the study of structural behavior given to horizontal effects. The significance of this is centered not only on the scales caused by wind and construction loads, which are considered to be “normal” i.e., the quasi-static, which is essentially one-way monotonically increasing but also the most researched seismic, i.e., cyclically varying, direction and magnitude effects. The most widespread methods for sizing flat plate slabs to unidirectional, quasi-static horizontal loads are the so-called Equivalent Frame Method and the Effective Beam Width Method. In addition to analytical and basically linear numerical theoretical methods, several laboratory experiments have been performed and published. In this paper, we investigate the behavior of column-supported flat plate slabs against unidirectional, monotonically increasing horizontal loads using an advanced nonlinear numerical modeling method. Numerical models constructed with different geometric dimensions were created with ATENA 3D three-dimensional nonlinear finite element software. In the numerical studies, in addition to the vertical loads in the global sense, the horizontal loads were also taken into account. In our studies, we analyzed the global behavior of the structure, crack propagation, and internal stresses. The results were illustrated on force - displacement diagrams and compared with the results of the laboratory experiments used, thus showing the accuracy and limitations of the numerical modeling procedure. The numerical results were also compared with the results determined on the basis of the equivalent framework models.

MaskDUF: Data uncertainty learning in masked face recognition with mask uncertainty fluctuation

As an essential component of experts and intelligent systems, Masked Face Recognition (MFR) has been applied to various applications, but it is still a challenging task due to the aggravated uncertainty caused by mask occlusion. Most existing MFR methods are deterministic point embedding models that are limited in representing the uncertainty in masked face images. Data Uncertainty Learning (DUL) is an advanced uncertainty modeling method, but there are two problems when it is applied to MFR task: (1) the masked face tends to be regarded as noise due to the mask occlusion, which weakens its optimization; (2) the large representation difference between face and masked face results in a dispersed intra-class distribution. To solve the above problems, we propose a novel masked face data uncertainty learning method (MaskDUF) for MFR task, which can adaptively adjust the optimization weight by modeling the uncertainty and measuring the recognizability of samples, thus learning an ideal sample distribution with compact intra-class, discrepant inter-class and distant noise. Specifically, a Hard Kullback–Leibler Divergence (H-KLD) method is proposed to serve as an adaptive variance regularizer for masked faces, which contributes to learning more accurate uncertainty representations and avoiding overfitting noise. Moreover, by combining feature magnitude and variance uncertainty, Mask Uncertainty Fluctuation (MUF) is proposed to comprehensively measure sample recognizability, which contributes to enhancing the learning preference of masked faces and constructing a more compact cone-like intra-class distribution. Finally, compared with other advanced models, MaskDUF achieves an average accuracy improvement of 1.33% to 13.28%, and its effectiveness and strong robustness are also proved by ablation study, noise experiment and parametric analysis.

Forecasting algorithms in the ICU

Despite significant advances in modeling methods and access to large datasets, there are very few real-time forecasting systems deployed in highly monitored environment such as the intensive care unit.Forecasting models may be developed as classification, regression or time-to-event tasks; each could be using a variety of machine learning algorithms.An accurate and useful forecasting systems include several components beyond a forecasting model, and its performance is assessed using end-user-centered metrics.Several barriers to implementation and acceptance persist and clinicians will play an active role in the successful deployment of this promising technology.

Challenges of high-fidelity virtual reactor for exascale computing and research progress of China Virtual Reactor

The virtual reactor is a sophisticated software system deployed on supercomputers to simulate physics process or predict component behaviors inside real reactors. It highly relies on advanced modeling and coupling methods, high performance computing and advanced verification and validation (V&V) techniques, which is another important method for reactor engineering design and mechanism research besides experiment. With the advent of exascale computer, it becomes mainstream trend to develop full-core high-fidelity simulation research under the help of exascale computing technology. This paper first summarizes the challenges of developing high-fidelity virtual reactor simulation in exascale computing. The challenges of high-fidelity virtual reactor simulation are introduced from four aspects: science-based modeling methods, high-resolution mesh model, scientific numerical methods , and multiphysics and multiscale coupling. Based on the architecture of exascale computer, the challenges of parallel optimization of virtual reactor for exascale computing are introduced. Furthermore, this paper introduces the recent research progress of China Virtual Reactor (CVR), which is about the parallel optimization for Chinese exascale computers and the functional extensions for achieving full-core high-fidelity simulation. The recent research progress of CVR includes multiscale simulation software of materials, fuel performance simulation software, thermal-hydraulic simulation software, full-core high-resolution meshing tool, and optimization of basic sparse operators.

Understanding Li Nucleation and Growth

Lithium metal anode has been widely studied for next generation lithium metal batteries. Its chemical, electrochemical and mechanical properties are unique as lithium is the lightest metal in the planet earth. In this talk, I will use some of the advanced modeling and characterization methods to probe and understand the lithium metal anode in a rechargeable battery. The tools we developed here will be also useful for other reactive metal anodes such as sodium and zinc.

Discussion of the 2022 Hansen Lecture: “The Evolution of the Use of Models in Survey Sampling”

Abstract The 2022 Hansen Lecture gave a broad overview of the use of models in survey sampling, with emphasis on modeling approaches to incorporating auxiliary information in survey estimators. This discussion expands upon some issues in model-assisted estimation, exploring data needs and the availability of multipurpose weights for advanced modeling methods.

Sedimentary Processes and the Temporal Resolution of Sedimentary Strata

AbstractAn appealing strategy for reconstructing the timing and tempo of paleoenvironmental change from sedimentary strata is to linearly interpolate between marker beds of known age. This method requires significant assumptions, but more advanced age modeling methods are usually not feasible. We used experiments to explore how changes in sedimentary processes invalidate these assumptions and affect estimates of time from the strata. When sedimentary processes changed to favor widespread deposition, we found that measuring time linearly systematically overestimated time duration from the resulting strata (time dilation) and misestimated the beginning and end of geologic intervals (phase shifting). When simple age models must be used for sedimentary strata, geologic evidence for transient changes in spatial sediment dispersal may help identify sections of dilated and shifted time, and better resolve time in sedimentary strata.

Analysis and modelling of thrust force in drilling of basalt and carbon fibre-reinforced polymer (BFRP and CFRP) composites

Currently, the use of sustainable products and technologies is growing; consequently, mineral-origin basalt fibre-reinforced polymer (BFRP) composites are becoming more popular in industries. Although BFRP parts require mechanical drilling operations for manufacturing holes for assembly, many challenges make the drilling process difficult. Considering that the cutting force is one of the main parameters characterising the drilling process, this study aims to analyse the influence of feed (mm/rev) and cutting speed (m/min) on the thrust force and model the thrust force in the drilling of BFRP composites through response surface methodology (RSM) and advanced statistical modelling methods. In order to determine main and interaction effects and to calculate the regression coefficients and model parameters, mechanical drilling experiments were performed, and the thrust force was recorded. The raw force data were processed using fast Fourier transformation-based low-pass filtering, and then the calculated thrust force parameters were evaluated relative to various feeds and cutting speeds. In addition, results were compared with those of carbon fibre-reinforced polymer composites. The results of the validation experiments show that both RSM and advanced statistical models accurately predict the thrust force in BFRPs of 96.74% and 95.01%, respectively. However, the advanced statistical model can describe not only the maximum values of the force but also its characteristics at a coefficient of determination of 0.68.

Improving the hole picture: towards a consensus on the mechanism of nuclear transport.

Nuclear pore complexes (NPCs) mediate the exchange of materials between the nucleoplasm and cytoplasm, playing a key role in the separation of nucleic acids and proteins into their required compartments. The static structure of the NPC is relatively well defined by recent cryo-EM and other studies. The functional roles of dynamic components in the pore of the NPC, phenylalanyl-glycyl (FG) repeat rich nucleoporins, is less clear because of our limited understanding of highly dynamic protein systems. These proteins form a 'restrained concentrate' which interacts with and concentrates nuclear transport factors (NTRs) to provide facilitated nucleocytoplasmic transport of cargoes. Very rapid on- and off-rates among FG repeats and NTRs supports extremely fast facilitated transport, close to the rate of macromolecular diffusion in cytoplasm, while complexes without specific interactions are entropically excluded, though details on several aspects of the transport mechanism and FG repeat behaviors remain to be resolved. However, as discussed here, new technical approaches combined with more advanced modeling methods will likely provide an improved dynamic description of NPC transport, potentially at the atomic level in the near future. Such advances are likely to be of major benefit in comprehending the roles the malfunctioning NPC plays in cancer, ageing, viral diseases, and neurodegeneration.

A Critical Review on Channel Modeling: Implementations, Challenges and Applications

In recent years, the use of massive multiple-input multiple-output (MIMO) systems and higher frequency bands for next-generation urban rail transportation systems has emerged as an intriguing research topic due to its potential to significantly increase network capacity by utilizing available narrowband and broadband spectrums. In metro and mining applications, the high-reliability wireless sensor network (WSN) plays a vital role in providing personal safety, channel optimization, and improving operational performance. Through the duration of 1921–2023, this paper provides the survey on the progress of fifth-generation (5G) and beyond-fifth-generation (B5G) wireless communication systems in underground environments such as tunnels and mines, the evolution of the earliest technologies, development in channel modeling for vehicle-to-vehicle (V2V) communications, and realization of different wireless propagation channels in high-speed train (HST) environments. In addition, the most recent advanced channel modeling methods are examined, including the development of new algorithms and their use in intelligent transportation systems (ITS); mathematical, analytical, and experimental techniques for propagation design; and the significance of the radiation characteristics, antenna placing, and physical environment effect on wireless communications. Leaky coaxial cable (LCX) and distributed antenna system (DAS) designs are introduced in the demonstrated systems for improving the channel capacity of narrowband and wideband channels as well as the spatial characteristics of various MIMO systems. The review article concludes by figuring out open research directions for future technologies.

3D pole-pole responses of buried anisotropic targets and various anisotropic backgrounds in direct-current resistivity survey

Abstract Many rocks often have the electrical anisotropy property that produces quite different responses from the electrical isotropic rocks. We applied an advanced modeling method to calculate the apparent resistivity responses over an anisotropic target in a homogenous isotropic backgound and an isotropic target embedded in different anisotropic host rocks. We demonstrated comprehensive modeling experiments to characterize the responses of surface pole-pole surveys to the buried targets in different geological backgrounds, which vary in the principal resistivities and Euler angles of the targets or the host rocks. Our modeling results show that: (i) the location of current injection controls the apparent resistivity images in value and shape; (ii) almost circle-contours or more or less elongated contours appear in the apparent resistivity images for the anisotropic targets; (iii) the isotropic target embedded in the anisotropic host rock generates some radial patterns from the current injection point; (iv) the anisotropy strike angles can be obtained from the symmetry axis of the apparent resistivity contours; and (v) multiple locations of current injections throughout the detection region are necessary for the surface pole-pole survey to avoid misleading data interpretation.

Noise-Aware Sparse Gaussian Processes and Application to Reliable Industrial Machinery Health Monitoring

Maintenance of machinery equipment in smart manufacturing requires real-time health monitoring, strongly supported by the rapid evolution of Artificial Intelligence (AI) technologies. Most AI-based health monitoring systems are powered by advanced modeling methods and intensive high-quality monitoring data. Such monitoring systems center on high-accuracy predictive performance but cannot necessarily convey reliability, such as satisfactory resistance to strong noises, credible uncertainty analysis, and model interpretability. This article novelly proposes noise-aware sparse Gaussian processes (NASGP) within the Bayesian inference framework. NASGP are capable of consistent high-performance and credible uncertainty assessment under strong noises. Based on NASGP, we then develop an explainable generalized additive model to bridge the gap between latent inference mechanism and domain expert knowledge. The efficacy of the proposed approach is corroborated through two case studies including remaining useful life prognosis and fault diagnosis for rolling bearings.

Fluid-structure interaction simulation for performance prediction and design optimization of parafoils

Parachute design is challenging to achieve innovative progress if the dominant role of testing continues, as it will be an increasingly expensive and time-consuming work. The aim of this study is to establish a reliable and efficient design tool using existing advanced numerical modeling methods. This paper presents a numerical method based on two-way coupled fluid-structure interaction (FSI) strategies for predicting aerodynamic and flight performance for parafoil design optimization. The nonlinear finite element method was used for the canopy fabric model and flow field, and the fluid dynamics were solved by Reynolds-averaged Navier-Stokes with the Spalart-Allmaras turbulence model. The FSI simulations are performed to assess the aerodynamic performance and structural deformations of full-scale parafoil canopies. The equilibrium shape of the parafoil canopy under steady gliding states and the relevant flow field were analyzed to enhance confidence and understanding in the performance prediction of new parachutes. Three-dimensional FSI simulation results of parafoils show that the inflation caused flexible bulges of canopy cells, and the maximum lift coefficient increased more than 16% with a higher stall angle of attack than that of the rigid body model. A parafoil with a smaller leading edge inlet or a scaling down area can improve the aerodynamic performance, mainly manifested in a higher lift-to-drag ratio and better anti-stall performance. Finally, the prediction results of parafoil glide performance were verified by flight test data, and the prediction accuracy of the flexible model is more than 10% higher than that of the rigid model. This work makes the simulation tools a step closer to practical application.

Numerical modeling of open TBM tunneling in stratified rock masses using a coupled FDM-DEM method

The complex engineering characteristics of stratified rock masses make the comprehensive analysis of TBM tunneling more complex and difficult. In this study, an advanced modeling method based on the coupled finite difference method (FDM) and discrete element method (DEM) was proposed for the simulation of open TBM tunneling in stratified rock masses. The modeling and analysis process of the FDM-DEM numerical model was presented with the DLS Tunnel as the research background. Numerical results show that the displacement values and characteristics of the rock mass are consistent with the deformation of the surrounding rocks obtained from in situ monitoring. Severe shear and tensile fractures occur progressively in both rock blocks and bedding planes during TBM tunneling. The extent of the loosening area in the rock mass also continues to grow. The numerical model reproduces the progressive failure process of the stratified rock mass through the analysis of contact fracture events and the evolution of contact forces. The simulation of the interaction between the rock mass and the shield is accomplished by FDM-DEM coupling methods under the large-strain mode. This study presents an effective tool for the comprehensive analysis of open TBM tunneling in stratified rock masses.

Advanced Modeling and Signal Processing Methods in Brain-Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes.

In this study is substantiated the new mathematical model of vector of electroencephalographic signals, registered under the conditions of multiple repetitions of the mental control influences of brain-computer interface operator, in the form of a vector of cyclic rhythmically connected random processes, which, due to taking into account the stochasticity and cyclicity, the variability and commonality of the rhythm of the investigated signals have a number of advantages over the known models. This new model opens the way for the study of multidimensional distribution functions; initial, central, and mixed moment functions of higher order such as for each electroencephalographic signal separately; as well as for their respective compatible probabilistic characteristics, among which the most informative characteristics can be selected. This provides an increase in accuracy in the detection (classification) of mental control influences of the brain-computer interface operators. Based on the developed mathematical model, the statistical processing methods of vector of electroencephalographic signals are substantiated, which consist of statistical evaluation of its probabilistic characteristics and make it possible to conduct an effective joint statistical estimation of the probability characteristics of electroencephalographic signals. This provides the basis for coordinated integration of information from different sensors. The use of moment functions of higher order and their spectral images in the frequency domain, as informative characteristics in brain-computer interface systems, are substantiated. Their significant sensitivity to the mental controlling influence of the brain-computer interface operator is experimentally established. The application of Bessel's inequality to the problems of reducing the dimensions (from 500 to 20 numbers) of the vectors of informative features makes it possible to significantly reduce the computational complexity of the algorithms for the functioning of brain-computer interface systems. Namely, we experimentally established that only the first 20 values of the Fourier transform of the estimation of moment functions of higher-order electroencephalographic signals are sufficient to form the vector of informative features in brain-computer interface systems, because these spectral components make up at least 95% of the total energy of the corresponding statistical estimate of the moment functions of higher-order electroencephalographic signals.

Deep Neural Networks With Koopman Operators for Modeling and Control of Autonomous Vehicles

Autonomous driving technologies have received notable attention in the past decades. In autonomous driving systems, identifying a precise dynamical model for motion control is nontrivial due to the strong nonlinearity and uncertainty in vehicle dynamics. Recent efforts have resorted to machine learning techniques for building vehicle dynamical models, but the generalization ability and interpretability of existing methods still need to be improved. In this paper, we propose a pure data-driven vehicle modeling approach based on deep neural networks with an interpretable Koopman operator. The main advantage of using the Koopman operator is to represent the nonlinear dynamics in a linear lifted feature space. In the proposed approach, a deep learning-based extended dynamic mode decomposition algorithm is presented to learn a finite-dimensional approximation of the Koopman operator. A multi-step prediction loss function is used in the training process, enabling a long-term prediction capability. Furthermore, a data-driven model predictive controller with the learned Koopman model is designed for velocity profile tracking control of autonomous vehicles. Simulation results in a high-fidelity CarSim environment show that our approach outperforms previously developed traditional and advanced modeling methods. Velocity profile tracking tests of the autonomous vehicle are also performed in the CarSim environment. The results show that our approach has better tracking accuracy and higher computational efficiency than the model predictive control algorithms using a nonlinear model and a linear time-varying model.

Advanced Modeling and Simulation Methods for Evaluation of Thermal Neutron Scattering Materials

With the rise of interest in thermal neutron scattering data for advanced reactor, criticality safety, and shielding applications, new experimental data are required for evaluation of new materials or for re-evaluation (or validations) of previously evaluated materials. New experimental data are evaluated in a three-step process: (1) computing the phonon characteristics, (2) computing the dynamic structure factor (DSF) from the data, and (3) using the experimental setup to simulate the experimental data. All three steps have challenges, ranging from the need for a suffciently general material simulation code—a processing code that can compute the corresponding DSF—to having a detailed layout of the instrument/beamline/facility where the data were measured. Whereas phonon characteristics of materials can be computed using various methods (molecular dynamics, density functional theory, etc.), a high-fidelity computation of the DSF and the simulation of the experiment based on the DSF is vital to the accuracy of the evaluation. The latter two steps can be achieved by using the two corresponding code systems developed by instrument scientists at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory: (1) OCLIMAX, a program that calculates the dynamic structure factor from DFT and MD simulation results, and (2) MCViNE, a Monte Carlo neutron ray-tracing program designed to simulate neutron scattering experiments. Recently, polyethylene and yttrium hydride were measured at the Wide Angular-Range Chopper (ARCS) and SEQUOIA instrument stations of the SNS. These experiments are simulated using the density functional theory code, the Cambridge Serial Total Energy Package (CASTEP), to compute its phonon characteristics (eigenvalues/vectors and PDOS), which is then processed using OCLIMAX to yield the DSF, and finally the data at each instrument station are simulated by the MCViNE for comparison to the measured data for evaluation. For comparison to conventional evaluation methods, the scattering data processed from OCLIMAX are compared against those processed from the LEAPR module of NJOY, and the results from MCViNE simulations are compared against previously used simplified beamline models implemented in the Monte Carlo N-Particle (MCNP) code.

Advances in understanding and predicting the spread of invading insect populations.

Understanding and predicting the spread of invading insects is a critical challenge in management programs that aim to minimize ecological and economic harm to native ecosystems. Although efforts to quantify spread rates have been well studied over the past several decades, opportunities to improve our ability to estimate rates of spread, and identify the factors, such as habitat suitability and climate, that influence spread, remain. We review emerging sources of data that can be used to delineate distributional boundaries through time and thus serve as a basis for quantifying spread rates. We then address advances in modeling methods that facilitate our understanding of factors that drive invasive insect spread. We conclude by highlighting some remaining challenges in understanding and predicting invasive insect spread, such as the role of climate change and biotic similarity between the native and introduced ranges, particularly as it applies to decision-making in management programs.

Advances in analysis, quantification and modelling of N2O emission in SBRs under various DO set points

Nitrous oxide (N2O), considered a major greenhouse gas (GHG) in wastewater treatment plants (WWTPs), is produced during both nitrification and denitrification processes; hence, it needs to be controlled by internal and external strategies. Various factors, such as DO, temperature, and pH, could be incorporated into the mitigation of emissions in WWTPs. In this research, potential operational strategies were investigated in order to find the optimal range for DO and temperature for controlling the N2O production during the nitrification process. In parallel, the activity of nitrite oxidizing bacteria (NOB) could also be limited under optimal conditions to make the process more cost-effective and energy-saving. In this regard, under a lab-scale environment, DO = 0.7 mg/l was detected as the optimal range for inhibiting NOB activity and maintaining AOB activity. Moreover, the importance of developing mathematical modelling methods has gained significant attention in order to better understand the possibility of minimizing GHG in WWTPs. In this study, advanced mathematical modelling methods were used for simulating the kinetics of the nitrification process to determine the interaction among different operating factors compared to nitrification rates.