Advanced Modeling Methods Research Articles

A framework for digital twin integration in biofabrication and a scaffold 3D bioplotting case study

Biofabrication, which integrates biological sciences with advanced manufacturing, is vital for innovations in tissue engineering and regenerative medicine. One primary consideration in this domain is ensuring consistent, scalable, and adaptable processes that are amenable to clinical translation. Toward this, this paper introduces a new framework for digital twin integration in biofabrication. Digital twins, which are real-time virtual replicas of physical systems, can facilitate comprehensive monitoring, accurate prediction, and effective optimization to enable robust biofabrication processes and systems. The proposed framework incorporates major building blocks for implementing digital twins for biofabrication, including comprehensive data acquisition and analysis using sophisticated sensors to integrate biological, material, and process data; advanced modeling methods utilizing traditional and machine learning algorithms to refine process parameters; and feedback loop for real-time process adjustments. The first phase of the framework is demonstrated through a case study highlighting process optimization of 3D bioplotting of polycaprolactone (PCL) scaffolds. The digital twin’s insights streamline decisions on bioplotting parameters and post-processing, ensuring that the printed scaffold meets the desired mechanical and biological properties, thereby significantly enhancing cell proliferation and printing resolution. Key benefits of this approach include optimized biofabrication outcomes by integrating physical and biological models, promoting scalability and reproducibility with an accelerated research and development phase. This work underlines the promise of digital convergence in biofabrication, which can accelerate advancements in regenerative medicine and tissue engineering.

UTILIZING GAUSSIAN PROCESS REGRESSION FOR NONLINEAR MAGNETIC SEPARATION PROCESS IDENTIFICATION

This paper presents a novel approach utilizing Gaussian Process Regression (GPR) to identify dynamic models with nonlinear parameters in magnetic separation processes. It aims to address the complex and dynamic nature of these processes by employing advanced modeling methods. The effectiveness of GPR is demonstrated through its application to simulated signals representing real iron ore separation processes, highlighting its potential to enhance existing models and optimize processes. Conducted within the MATLAB, this research lays the groundwork for further advancement and practical implementation. The utilization of GPR in magnetic separation offers innovative modeling of nonlinear dynamic processes, promising improved efficiency and precision in industrial applications.

RESEARCH ON THE DESTRUCTION PROCESSES OF WELDED JOINTS UNDER CONDITIONS OF DETACHMENT OF THE VERTICAL STEEL TANK BODY FROM THE BOTTOM DURING A FIRE

The study presents a comprehensive analysis of welded joints by applying advanced mathematical modelling and experimental research methods. The primary objective was to develop and validate a methodology for mathematical modelling of the behaviour of welded joints under mechanical loading, as well as to assess the effectiveness of the proposed approaches in predicting their strength and durability. Based on the research results, we can draw the following conclusions. The authors obtained the results of welded joint samples and analysed the corresponding curves of the dependence of force factors on their deformations. The study showed that the destruction of welded joints occurs mainly along the weld seam, where the strength is lower than in the base material of the rods. It is consistent with the practical data of the experiment, which confirms that the weld is the weakest link in the structure. The article substantiated a set of provisions and assumptions for implementing mathematical modelling of the behaviour of samples of welded joints imitating welded joints in a reservoir for petroleum products under mechanical tensile tests. The test results justified the mathematical models of the steel material of welded joints of petroleum product tanks and obtained numerical parameters of the mechanical properties of steel of welded joints. Yield strength is in the range of 240–300 MPa, the modulus of elasticity is 190–240 GPa, the hardening modulus is 18–20 GPa, and the ultimate plastic deformation is 0.00334. Based on a complex of well-founded mathematical models of welded joints, we carried out calculations regarding the numerical study of the behaviour of welded joints under the conditions of their mechanical tests. We compared the obtained results with the results of experimental tests and, based on this comparative analysis, established that they were adequate since the Fisher criterion did not exceed the tabular value and the relative error did not surpass 12%. The findings of this research offer valuable insights and practical recommendations for improving the design and evaluation of welded structures, enhancing their performance and safety in engineering applications.

Modeling and validation of purification of pharmaceutical compounds via hybrid processing of vacuum membrane distillation

This study provides an in-depth examination of forecasting the concentration of pharmaceutical compounds utilizing the input features (coordinates) r and z through a range of machine learning models. Purification of pharmaceuticals via vacuum membrane distillation process was carried out and the model was developed for prediction of separation efficiency based on hybrid approach. Dataset was collected from mass transfer analysis of process to obtain concentration distribution in the feed side of membrane distillation and used it for machine learning models. The dataset has undergone preprocessing, which includes outlier detection using the Isolation Forest algorithm. Three regression models were used including polynomial regression (PR), k-nearest neighbors (KNN), and Tweedie regression (TWR). These models were further enhanced using the Bagging ensemble technique to improve prediction accuracy and reduce variance. Hyper-parameter optimization was conducted using the Multi-Verse Optimizer algorithm, which draws inspiration from cosmological concepts. The Bagging-KNN model had the highest predictive accuracy (R2 = 0.99923) on the test set, indicating exceptional precision. The Bagging-PR model displayed satisfactory performance, with a slightly reduced level of accuracy. In contrast, the Bagging-TWR model showcased the least accuracy among the three models. This research illustrates the effectiveness of incorporating bagging and advanced optimization methods for precise and dependable predictive modeling in complex datasets.

Development of an effective modeling method for the mechanical analysis of three-core submarine power cables under tension

The structural complexity of submarine power cables (SPCs) presents significant challenges in their local mechanical analysis. This paper introduces an advanced modeling method developed for analyzing the mechanical behavior of SPCs under tension, emphasizing both accuracy and efficiency. The method’s accuracy is validated through a comparison of simulation results with tension tests conducted on a three-core SPC sample. Efficiency is demonstrated by the superior calculation speed of our model relative to traditional full-scale models. This improved performance is achieved by adopting periodic boundary conditions derived from the homogenization method applied to slender beam-like structures, and by employing a specialized combination of elements to model the helical metal components within the SPCs. The resulting model provides robust capabilities for the mechanical analysis of SPCs under tension and demonstrates significant possibility in accommodating various other loadings.

POWER SYSTEM STABILITY CONSIDERING THE INFLUENCE OF DISTRIBUTED ENERGY RESOURCES ON DISTRIBUTION NETWORKS

The increasing penetration of distributed energy resources (DERs) into power systems has transformed the landscape of energy distribution, bringing both opportunities and significant challenges. This study examines the impact of DER integration on power system stability, focusing on voltage stability, frequency stability, and transient stability. A comprehensive review of 50 high-quality studies from peer-reviewed journals and reputable conference proceedings was conducted, utilizing advanced modeling, simulation, and empirical analysis methods. The findings reveal that the intermittent nature of renewable DERs, such as solar and wind, leads to voltage fluctuations, necessitating advanced control strategies to maintain stability. Additionally, the displacement of traditional synchronous generators by inverter-based DERs reduces system inertia, posing severe frequency stability challenges that require innovative solutions like synthetic inertia and fast frequency response mechanisms. Transient stability issues are also exacerbated by DER integration, highlighting the need for advanced inverter controls and enhanced fault ride-through capabilities. Energy storage systems (ESS) are identified as crucial for buffering the variability of renewable DERs, providing essential services such as frequency regulation and voltage support. However, high costs and scalability issues remain barriers to widespread ESS adoption. The study underscores the importance of supportive regulatory and policy frameworks in facilitating the seamless integration of DERs while maintaining grid stability. Effective policies that promote smart grid technologies and DER-friendly regulations are essential for ensuring a stable, resilient, and sustainable power grid. This research contributes to a deeper understanding of the complex dynamics introduced by DERs and offers insights into developing robust strategies to address stability challenges in modern power systems.

A descriptive and prescriptive analysis of rail service subsidies in the China–Europe freight transportation market

This study delivers a descriptive and prescriptive analysis of rail service subsidies for China Railway Express (CRE) in the China-Europe freight transportation market. The analysis is conducted by advanced mathematical modeling and programming methods. Specifically, we implemented a multicommodity multimodal freight transportation network equilibrium model that can be used for predicting the commodity-specific mode-route cargo flow pattern and hence for assessing the effectiveness and limitations of the current CRE subsidy scheme. To properly quantify the impact of subsidies on individual shippers’ decision making, the model explicitly characterizes individual shippers’ mode-route choice behavior and takes into account shipping cost, transit time, capacity-induced congestion surcharge, and unobserved transportation impedances as shippers’ disutility. The solution of the network equilibrium model resorts to a disaggregate simplicial decomposition algorithm within the well-known Lagrangian relaxation framework. A bi-level network-based subsidy optimization model is constructed, in which the upper level aims at minimizing the sum of revenue loss and congestion charge, and the lower level is the aforementioned freight transportation network equilibrium model. A tabu search procedure is proposed and implemented to derive the solution of the bi-level model. The above models and algorithms are then applied to the China-Europe containerized freight transportation network, which comprises all China-Europe liner shipping lines, all CRE service lines, and the highway networks in China and Europe. The evaluation and optimization results show that the current subsidy scheme creates an imbalanced capacity utilization pattern across CRE service lines while an optimized line-specific subsidy solution can yield noteworthy improvements in the service utilization and economic efficiency of CRE.

Nonlinear dynamical modeling and response analysis of complex structures based on assumed mode weighting

The assumed mode method (AMM) is widely used for dynamical modeling but faces increasing challenges in developing low-dimensional and high-precision models of complex structures for investigations of nonlinear dynamical responses and active vibration control. This is mainly because the current complex structure has an increasing number of components, each of which needs to be discretized by multiple assumed modes in dynamical modeling, leading to increasing degrees of freedom for the system. To address this issue, this paper proposes a dimension-reduced method based on the assumed mode weighting method, referred to as Mode Weighting Method (MWM). The modeling process of a multi-beam structure is fully presented as an example to show the strategy of the MWM. During this process, the assumed mode (AM) model (the dynamic model derived in terms of a set of assumed modes) is developed first where the joints of the structure between its components are considered as artificial springs and each component is discretized by its vibration modes without constraints. The components of the eigenvector of the characteristic equation derived from the AM model are taken as the weighting coefficients to get the approximate analytical global modes of the structure by multiplying the corresponding assumed modes. Consequently, a nonlinear dynamic model with a few degree of freedom is obtained by discretizing the structure with the weighted modes. Moreover, a technique for simply obtaining the weighted mode (WM) models by directly processing the AM model through a transformation matrix is presented. To validate the proposed method, the simulations on the natural characteristics and the dynamical responses for both linear and nonlinear models, are performed respectively based on the WM and the finite element models, where both results are well matched each other. This work provides a new way of developing advanced dynamical modeling methods for structural dynamics design, optimization, and active vibration control.

Optimizing multi-objective design, planning, and operation for sustainable energy sharing districts considering electrochemical battery longevity

The urgent need to address the global warming crisis has led to a paradigm shift in energy production from traditional fossil fuels to renewable sources such as geothermal, solar, and wind. This transition necessitates efficient energy management strategies to enhance renewable energy utilization, reduce microgrid dependency, and establish a more stable power grid. This study explores the novel integration of interactive energy sharing networks utilizing electrochemical battery storage, emphasizing detailed modeling of battery degradation, smart energy management, and multi-criteria decision-making. This research involves an innovative method combining Monte Carlo Tree Search technique and a multi-criteria approach for energy management in district communities. The research delves into the complexities of integrated multi-energy systems, considering structural designs, advanced modeling methods, and multi-criteria predictions. It also employs powerful machine learning methods to predict battery depreciation, demonstrating their superiority over standard semi-empirical models. The study also proposes deterministic and stochastic methods for intelligent energy management, considering unpredictable factors like weather and energy demand profiles. The research incorporates a comprehensive examination of interactive energy sharing networks, encompassing renewable-to-vehicle and vehicle-to-building interactions. Through an in-depth analysis this research highlights the advantages of multi-agent systems, time-of-use energy shifting, demand profile flattening, and micro-grid resilience.

A State-of-the-Art Review on Direct Welding of Polymer to Metal for Structural Applications: Part 1 — Promising Processes

Structural lightweighting through the effective use of multiple materials has received increasing attention for fulfilling today’s demands for environmental sustainability in transportation systems. Direct dissimilar material joining methods (versus, e.g., traditional adhesive bonding or mechanical fastening) have become increasingly desirable since they offer process simplicity, production efficiency, and hermetic sealing, among others. In this two-part article, we provide a critical assessment of the state-of-the-art research and promising direct dissimilar material joining techniques reported over the last decades, with a particular emphasis on their potential for structural applications in Part I. As such, recent advances in advanced joint design and modeling methods for enabling optimum joint design for jointability and joint performance are presented along with some detailed examples for demonstrating their potential impacts on industrial applications in Part II. Finally, recommendations on future research and development directions are outlined for supporting the industry’s drive towards multi-material lightweighting.

Post-damage recovery of substandard RC columns by CFRPs

Cyclic response of substandard reinforced concrete (RC) columns and structural repair of pre-damaged columns by carbon-fiber-reinforced polymers (CFRPs) were investigated through combination of experimental and advanced numerical modeling methods. The specimens, representing typical RC columns in substandard buildings, were constructed from low-strength concrete, plain round bars, and nonconforming reinforcement details. The key parameters under investigation included the lap-splice, hook detail in the longitudinal reinforcement, and axial load ratio. First, the RC columns were subjected to cyclic loading until the attainment of their heavy damage state. The failure mode was dominated by the flexural response alongside significant reinforcement slip and axial damage, involving concrete crushing and reinforcement buckling. Subsequently, the severe cracks and concrete damage were repaired by the externally wrapped CFRP sheets. The former load-carrying capacities of the repaired RC columns were recovered. The damage formation in the concrete and reinforcement was transferred to CFRP sheets. The final failure mode was characterized by local deformations in the CFRP, with minimal damage observed in the concrete and reinforcement bars. Furthermore, the numerical solution effectively reproduced the nonlinear behavior of the as-built specimens. The crack patterns and capacities observed in the numerical solutions align with the responses observed in the experimental tests.

A Vision for the Future of Multiscale Modeling.

The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.

Dynamic Modeling Framework Based on Automatic Identification of Operating Conditions for Sintering Carbon Consumption Prediction

In iron and steel industry, sintering process requires huge carbon consumption. Achieving accurate and dynamic prediction of carbon consumption in this process is of evident significance to protect the environment and raise the economic efficiency of iron and steel industry. This paper develops an original dynamic modeling framework including automatic identification of operating conditions, modeling in different conditions, and dynamic prediction model of sintering carbon consumption. Firstly, an automatic kernel-based fuzzy C-means algorithm is presented for automatic identification of operating conditions. Dynamic relationships between the inputs and output of the model are analyzed by Copula Entropy, and then the relevant production data in each operating condition are determined by just-in-time learning method. Next, broad learning models are established under different operating conditions. Further, dynamic prediction model of sintering carbon consumption is designed, and the prediction error is adopted to quantify performance of the model and as one criteria to determine the update of production database. Finally, results of experiments using actual production data demonstrate the advantage and validity of the developed model compared with some advanced modeling methods. The developed model considers complex characteristics of the sintering process and presents a better dynamic performance and information mining performance.

A State-of-the-Art Review on Direct Welding of Polymer to Metal for Structural Applications: Part 2 — Joint Design and Property Characterization

Structural lightweighting through the effective use of multiple materials has received increasing attention for fulfilling today’s demands for environmental sustainability in transportation systems. Direct dissimilar material joining methods (versus, e.g., traditional adhesive bonding or mechanical fastening) have become increasingly desirable since they offer process simplicity, production efficiency, and hermetic sealing, among others. In Part I of this two-part article, we provided a critical assessment of the state-of-the-art research and promising direct dissimilar material joining techniques reported over the last decades, with a particular emphasis on their potential for structural applications. As such, in Part 2, recent advances in advanced joint design and modeling methods for enabling optimum joint design for joint ability and joint performance are presented along with some detailed examples for demonstrating their potential impacts on industrial applications. Finally, recommendations on future research and development directions are outlined for supporting the industry’s drive towards multi-material lightweighting.

The effect of external radiation exposure per person depending on his position and anthropometric indicators

This study reviews existing methods for modelling the impact of ionising radiation sources on the human body. Using advanced modelling methods, the main modelling parameters are determined and it is revealed whether anthropometry affects the increase or decrease of the total radiation dose in the conditions of work in radiation-contaminated areas of the nuclear fuel cycle. The most rational and safe approach today is to conduct experimental studies on anthropomorphic mannequins to simulate human exposure using the RANDO phantom model with different geometries of exposure to ionising radiation sources. The considered modelling methods are taken from medicine, nuclear power, economics and labour protection, where they have found their application and continue to be improved. For further research, it is necessary to use other types of models, such as the Event Tree model, SWOT analysis model, Zero Risk model, Correlation and Regression System model, Matrix model, and other computer models and simulations using artificial intelligence. The factors proposed in this article provide a “complete picture” of the factors that occur in real conditions of research, measurements, service, security and physical protection of radiation-hazardous facilities such as tailing ponds of former uranium production.

The Role of Modeling in the Analysis and Design of Sustainable Systems: A Panel Report

Sustainability should become a key concern in the next generation of engineered systems. While this expectation is relatively straightforward, the question of how to get there is less obvious. The multi-dimensional and intricate nature of sustainability poses challenges in designing sustainable systems and analyzing sustainability properties. Finding trade-offs between economic, environmental, societal, and technological aspects of sustainability is a wicked problem and calls for advanced modeling and simulation methods. In this paper, we report on a panel discussion held at the 28th Working Conference on Exploring Modeling Methods for Systems Analysis and Development (EMMSAD) with four esteemed experts representing four complementary and often conflicting perspectives on the role of modeling for sustainability – stakeholders, digitalization, degrowth and IT, and ethics. We report the key arguments of the panelists, discuss the roles of modeling in the analysis and design of sustainable systems, and, finally, elaborate on the conflicts among the perspectives, their effects, and potential resolutions.

An analysis of trends in UAV swarm implementations in current research: simulation versus hardware

In the fast-evolving field of uncrewed aerial vehicle (UAV) swarm research, there is a growing emphasis on validating results through simulation rather than hands-on hardware experiments. This article delves into this shift, focusing on fundamental research questions on whether simulation tests verify results with hardware experiments, if they mention reasons for not using hardware, and if they provide plans for future implementation using hardware. By examining relevant trends, this study aims to be among the first to address the question of whether the advancements in simulation platforms and disruption modeling have reduced the perceived need for real-world hardware-based tests to verify performance metrics. Supported by data from articles spanning a decade, this report examines global trends in UAV swarm research and experimentation. Variables such as the country, swarm size, and implementation method are reviewed to reveal current trends in how UAV swarm research is conducted and validated. It is concluded that the increase in the simulation-only deployments used by UAV swarm researchers is being readily accepted by the academic community, viewing it as a viable solution to avoid regulations on the UAV industry as well as a reflection on the advanced simulation and modeling methods being developed to support them.

Can fusion of vis-NIR and MIR spectra at three levels improve the prediction accuracy of soil nutrients?

Soil nutrients are an important component of soil fertility, and having accurate and timely soil nutrients information is conducive to scientifically guided agricultural fertilization and improved crop yields. Traditional soil nutrient measurement methods are accurate but time-consuming and costly. Visible near-infrared (vis-NIR) and mid-infrared (MIR) as techniques of spectroscopy measurements are cheap, fast, and non-destructive, and, after training, have been used separately to predict soil nutrients. They can give complementary information, so using them separately may not be optimal. This study investigated whether the fusion of vis-NIR and MIR spectra would improve the prediction of six soil nutrients: total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (AN), available phosphorus (AP) and available potassium (AK). The sample set was 501 tillage (0 ∼ 20 cm) soil samples from Guizhou Province (China). Three different sensor fusion techniques were compared: (1) direct fusion of spectra (low-level fusion; SF1); (2) fusion of spectral features selected by the least absolute shrinkage and selection operator (LASSO) algorithm (middle-level fusion; SF2); and (3) fusion using the Granger-Ramanathan averaging (GRA) method of the separate vis-NIR and MIR results (high-level fusion; SF3). Prediction models were built using partial least squares regression (PLSR) and support vector machine (SVM), and evaluated using leave-one-out cross-validation (LOOCV), coefficient of determination (R2), and ratio of performance to interquartile (RPIQ), defined as Rcv2 and RPIQCV. All three sensor fusion methods were more accurate than single-sensor methods in predicting TN, TK and AN (0.60 ≤ Rcv2 ≤ 0.92, 1.86 ≤ RPIQCV ≤ 5.08), but were less accurate for TP, AP and AK (Rcv2 ≤ 0.34, RPIQCV ≤ 1.55). Compared to the best prediction using each single sensor model, low-level fusion improved the prediction of TN and AN; middle-level fusion improved the prediction of TN, TP, AN, AP and AK; and high-level fusion improved the prediction of all soil nutrients. Moreover, the SF2-PLSR model provided the best prediction for TN (Rcv2 = 0.83, RPIQCV = 3.18), the SF2-SVM model for AN (Rcv2 = 0.67, RPIQCV = 2.06), and the SF3-PLSR model for TK (Rcv2 = 0.92, RPIQCV = 5.08). The SF2 and SF3 methods are recommended to predict TN and AN, and MIR spectra are recommended to predict TK (Rcv2 = 0.92, RPIQCV = 5.02) since using only a single sensor is cost-effective. The SF2 and SF3 methods improved the prediction accuracy of AP and AK, but the prediction accuracy was still low. This study only covered one area with its set of soil landscapes, and only used well-established modelling methods. The results can motivate research on new spectra techniques and advanced modelling methods, as well as transferability to other soil landscapes.

Approximate global mode method for flutter analysis of folding wings

The aircraft with folding wings benefits from the ability to transform its folding wings to adapt to various flight missions but suffers from the change of the wings’ aeroelastic characteristics as a result. To clarify the change, it is necessary to study the dynamical modeling of the folding wing and also its aeroelastic response. This paper proposes a novel approximate global mode method (AGMM) for dealing with complex boundary conditions and models a folding wing consisting of separate rectangular plates for nonlinear flutter analysis. A high-precision low-dimensional dynamical model of the folding wing is developed, which is first validated by comparing the modal results obtained from the AGMM model with those obtained from an equivalent finite element model and is also experimentally validated by a comparison of forced vibration responses. On this basis, an AGMM model of the folding wing in supersonic aerodynamic loads based on the piston theory is further developed. The critical flutter velocity at different folding angles and the aeroelastic response are comprehensively studied by simulations based on the model. The result shows that the first 7th-order modes are sufficient for obtaining a converged critical flutter velocity, which varies with geometric parameters and potentially increases by 714 %. In addition, the low-order torsional modes of the wing contribute more to the vibration than the other modes during flutter. This work provides a new way of developing advanced dynamical modeling methods of folding and combinational structures for their dynamics design, optimization, and system control.

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.