Bogdan I. Epureanu

Understanding and explaining the dynamics of complex systems is a critical yet challenging task. Such a challenge often arises from high dimensionality, nonlinearity, and randomness, which together make it difficult to study the key drivers influencing system behavior. One illustrative example is infectious disease dynamics. Outbreaks like COVID-19 have major public-health impacts, and timely vaccines and non-pharmaceutical interventions can lessen them. However, COVID-19, influenza, and other pathogens continually generate new strains, so standard low-dimensional compartmental models can be uninformative or misleading (a complexity comparable to many biological models). Policymakers therefore need tools to understand and identify key drivers of the system in such high-dimensional settings with random events. In this study, we address interpretability of such complex models through a machine learning framework. Specifically, we introduce a novel, multi-strain, multi-vaccine compartmental model and analyze it using AI methods and conditional SHapley Additive exPlanations importance measures. By training a surrogate neural network, we efficiently approximate the dynamics and perform feature importance analysis. Unlike traditional approaches, our conditional importance analysis reveals how a feature’s influence varies with other features, capturing key interactions among features in disease dynamics. While our case study focuses on epidemiological systems, the proposed framework offers a general methodology for understanding the drivers of complex nonlinear dynamical systems.

A novel local trajectory planner, capable of controlling an autonomous off-road vehicle on rugged terrain at high-speed is presented. Autonomous vehicles are currently unable to safely operate off-road at high-speed, as current approaches either fail to predict and mitigate rollovers induced by rough terrain or are not real-time feasible. To address this challenge, a novel model predictive control (MPC) formulation is developed for local trajectory planning. A new dynamics model for off-road vehicles on rough, non-planar terrain is derived and used for prediction. Extreme mobility, including tire liftoff without rollover, is safely enabled through a new energy-based constraint. The formulation is analytically shown to mitigate rollover types ignored by many state-of-the-art methods, and real-time feasibility is achieved through parallelized GPGPU computation. The planner's ability to provide safe, extreme trajectories is studied through both simulated trials and full-scale physical experiments. The results demonstrate fewer rollovers and more successes compared to a state-of-the-art baseline across several challenging scenarios that push the vehicle to its mobility limits.

Abstract Comprehensively predicting the structural dynamics of turbomachinery blisks is of critical importance to the gas turbine industry. Located in the compressor and turbine stages, vibrations of these structures are amplified by their inherent small or sometimes large mistuning. Hence, it is paramount for the safe operation of gas turbines to predict mistuned blisk vibration responses. However, this requires significantly higher computational effort compared to computing cyclic system (i.e., tuned) responses. To address this issue, physics-based and data-driven reduced-order models (ROMs) have been developed. While many physics-based ROMs have been developed for predicting blisk responses with small and large mistuning, they require large finite element (FE) models to characterize the system dynamics, and they cannot be enhanced using experimental data. Thus, this paper proposes a novel physics-informed data-driven approach to compute blisk responses with both large and small mistuning. Similar to classical physics-based approaches (i.e., PRIME), this paper utilizes two physics-informed neural networks to compute the transfer function matrices of two systems: (1) a cyclic pristine blisk with small mistuning, and (2) a cyclic rogue blisk with small mistuning. These transfer functions are then introduced in a linear system of equations to compute the blade root responses of a blisk with small mistuning and rogue blades. Blade tip responses can then be computed using root responses through a third neural network. This proposed method has been tested using a blisk lumped mass model with 18 blades. Results show highly accurate predictions, with absolute errors below 10% for all mistuned blisk configurations explored. Future work focuses on enhancing accuracy by optimizing neural network architecture and hyperparameters.

This study proposes a novel autonomous inspection framework for insulator strings in transmission facilities with an unmanned aerial vehicle (UAV). The proposed framework aims to overcome practical limitations of current inspection using the UAV for transmission facilities. Current inspection relies on manual piloting, which makes it difficult in consistent acquisition of inspection data under structural and environmental variability in transmission facilities. Reliable anomaly detection also remains challenging in real deployments because of scarce abnormal samples and diverse site conditions. To address these challenges, the proposed framework hybridizes an adaptive flight strategy with a novel deep neural network to autonomously inspect insulator strings without manual intervention. The proposed framework features three key characteristics. First, the adaptive flight strategy determines the position and orientation of the UAV to acquire high-quality images of insulator strings by analyzing the structural features of transmission facilities with multimodal information. Second, a novel deep neural network, titled hybrid anomaly detection via multi-scale variational autoencoder and classifier (HAD-VAEC), is proposed to detect abnormal insulator strings by effectively learning the distinct features between normal and abnormal patterns in a latent hyperspace. Third, synthetic insulator images are co-trained with real images using a novel co-training strategy, which are generated through three-dimensional computer-aided design modeling and generative adversarial networks. This feature aims to address data imbalance and improve the robustness of the HAD-VAEC. Extensive experiments on both virtual and real-world environments clearly demonstrate that the proposed framework enables efficient, safe, and autonomous inspection of insulator strings by addressing core technologies of the 4th industrial resolution.

A novel topology-aware model predictive control (MPC) framework is presented for navigating off-road wheeled vehicles with extreme mobility on 3-D terrains. Prior studies have oversimplified the problem by either treating it solely as a path-planning problem or disregarding the terrain topology. To bridge this gap, this article presents a one-layer model predictive planning and control framework that considers terrain topology for off-road vehicles at operationally relevant speeds. The algorithm is tested in various simulated scenarios with comparisons to benchmarks. The proposed algorithm demonstrates superior performance by capturing the dynamic constraints, while the benchmarks either fail to complete tasks or exhibit inferior performance due to inaccurate representation of the dynamic limitations of the vehicle. Furthermore, the algorithm is tested for robustness to demonstrate its ability to handle uncertainties in terrain topology.

Suppressing dendrite growth on metal electrodes is a key challenge for enabling high-performance, long-lasting rechargeable batteries. This study demonstrates an innovative dendrite-suppression mechanism based on oscillatory electrolyte flow generated by applying small mechanical vibrations to one of the electrodes in a symmetric zinc–zinc cell. The oscillatory flow prevents ion concentration polarization, resulting in stable voltage profiles and uniform electrodeposition, preventing dendrite growth at currents 20 times greater than the limiting current. The underlying mechanism is confirmed by coupled electrochemical-fluid flow simulations. The simulation results demonstrate the experimentally observed prevention of ion depletion and can be clearly correlated by Butler-Volmer kinetics. Notably, all experiments were conducted without external electrolyte reservoirs, utilizing only the electrolyte contained between the electrodes, a critical step towards practical use of internal electrolyte flow as a mechanism to enhance battery performance.

The COVID-19 pandemic exposed significant gaps in the coordination and integration of efforts required to effectively manage large-scale infectious disease outbreaks. A successful response to such crises demands the swift and ongoing synthesis of information and activities across multiple sectors, including government, healthcare, and private industry. However, these systems are often managed in isolation, leading to misaligned policies, fragmented communications, and inefficiencies that hinder pandemic response efforts. To address these challenges, we propose adopting a systems-of-systems (SOS) paradigm to enhance epidemic intelligence and improve preparedness and response during infectious disease emergencies. The SOS approach, widely used in engineering, offers a framework for integrating diverse fields such as virology, ecology, psychology, and policy. We illustrate the potential of this approach using highly pathogenic avian influenza (HPAI) as a case study and discuss key considerations for implementing SOS thinking in the context of global epidemic intelligence systems.

Abstract The study of blisks is essential to the development of safe and efficient turbomachines. Blisks are geometrically complex structures exposed to high temperatures and pressures that operate at high rotational speeds, and thus are susceptible to fatigue. Therefore, reducing vibration amplitudes in blisks is paramount to avoid failure of these systems. This paper extends previous studies of blisk dampers based on tuned vibration absorbers by analyzing the effects of including energy dissipation through impacts. In this work, the absorber has a ring architecture that also allows impacts. A finite element model of an as-manufactured blisk with 22 blades is used throughout the analysis, and the damper is tuned for experimental operating conditions. Impacts are modeled microscopically, using a novel impact-contact characterization. Results show that impacts significantly enhance the blade response reduction through energy dissipation. Furthermore, impacts slightly increase shift resonant frequencies and are effective for specific combinations of contact surface geometry and material properties.

Background: Many chronic kidney disease (CKD) patients have not been properly diagnosed, and their characteristics remain unclear. It has been difficult to identify their clinical courses by conventional analysis. Therefore, our study aimed to identify overlooked CKD patient groups at high risk of dialysis or death, addressing unmet clinical needs. Methods: We employed data mining techniques using a newly developed artificial intelligence (AI) algorithm—time-series K-means analysis—to classify patients on the basis of 34 time-series variables, including background, laboratory test results, and medication from a CKD cohort study (n=3,129). This method utilizes dynamic time warping to assess the similarity between clinical data trajectories. Subsequently, we conducted a barycenter transition analysis of all time-series data to visually examine the trajectories. Results: Their mean age and eGFR were 62.0 years and 50.6 mL/min, respectively. They were distinctly classified into seven groups on the basis of human-understandable backgrounds (Figure 1). Notably, one group exhibited low urinary protein levels, no hypertension, and no identifiable cause of CKD. They were assumed to have a favorable prognosis and were not specifically treated. However, their outcomes were worse than those of appropriately treated hypertensive patients, who had the best prognosis (hazard ratio, 2.82; 95% confidence interval, 1.58–5.09; p<0.0001). Although each group initially displayed different clinical courses, their progression patterns gradually aligned over time, ultimately converging into the final common pathway (Figure 2). Conclusion: The AI-based trajectory clustering analysis successfully uncovered the distinctive clinical course of previously overlooked CKD patients with unknown etiologies. The AI algorithm is valuable in identifying unmet clinical needs within large-scale patient datasets.

Abstract Comprehensively understanding the structural dynamics of turbomachinery blisks is of critical importance to the gas turbine industry. Located in the compressor and turbine stages, vibrations of these structures are amplified by their inherent mistuning present in blisks. Mistuning can be small or large. Small mistuning is defined as inherent deviations from nominal sector properties and geometry that does not significantly change blade-alone mode shapes or frequencies. Large mistuning is described as significant blade variations like those resulting from damage during operation and/or after repairs, such as geometric changes due to foreign object ingestion or blends. Large mistuning significantly changes the blade-alone mode shapes and frequencies, resulting in rogue blades. Mistuning breaks the nominal cyclicity of the system, resulting in shifts in frequency responses and large vibration amplifications. Hence, it is paramount for the safe operation of gas turbines to predict vibration responses of blisks with both small mistuning and rogue blades. Predicting mistuned blisk responses requires a significantly higher computational effort compared to cyclic systems. To address this issue, physics-based and data-driven reduced-order models (ROMs) have been developed. While many physics-based ROMs have been developed for predicting blisk responses with small mistuning, a much smaller number of methods have focused on blisks with both small and large mistuning. The pristine-rogue-interface modal expansion (PRIME) ROM is one of these methods. PRIME is a projection-based ROM where the basis vectors are composed of tuned modes from two cyclic systems: (1) modes of the blisk with all blades pristine (used only for sectors that only have small mistuning), and (2) modes of the blisk with all blades rogue (used only for rogue sectors that have large and possibly small mistuning). While purely physics-based ROMs are accurate, they require large finite element (FE) models to characterize the system dynamics, and cannot be enhanced using experimental data. Thus, this paper proposes a novel physics-informed data-driven approach to compute blisk responses with both large and small mistuning. Similar to a classical physics-based approach (i.e., PRIME), this paper utilizes two physics-informed neural networks to compute the transfer function matrices of two systems: (1) a cyclic pristine blisk with small mistuning, and (2) a cyclic rogue blisk with small mistuning. These transfer functions are then introduced in a linear system of equations to compute the blade root responses of a blisk with small mistuning and rogue blades. Blade tip responses can then be computed using root responses through a third neural network. This proposed method has been tested using a blisk lumped mass model with 18 blades. Results show highly accurate predictions, with absolute errors below 10% for all presented mistuned blisk configurations. Future work focuses on further enhancing the accuracy of this method by enhancing the neural network hyperparameters.

Drawing inspiration from the biological phenomenon of echolocation , ultrasound perception holds immense potential across various engineering domains, spanning from advanced imaging to precise navigation. Despite advances in sensor development and signal processing, current methodologies struggle to match the remarkable perceptual acuity of echolocating animals when deciphering real-world ultrasound echoes. In this study, we bridge this disparity by harnessing Convolutional Neural Networks (CNNs) to discern ultrasound scattering from objects of different shapes. Our novel approach entails training CNNs using exclusively synthetic data, derived from numerical simulations, to process real echoes. We achieve this through (1) sophisticated data augmentation and processing of synthetic echoes that accommodate physical variations and uncertainties inherent in practical scenarios and (2) specialized CNNs (SCNNs) targeted at each shape to compel models to learn features unique to that shape. Rigorous experimentation demonstrates the ability of these synthetically-trained models to accurately classify fundamental geometric shapes of objects based solely on experimentally measured echoes. Furthermore, the intentional selection of the size and shapes of the objects to produce perceptually similar echoes elucidates the efficacy of our approach in handling intricate perception scenarios. By alleviating laborious and costly data acquisition procedures in favor of synthetic data-driven training for real-world perception, our method opens avenues for advancements in diverse fields reliant on ultrasound-based technologies. These advancements bear implications spanning from diagnostics to the realm of autonomous systems and beyond.

Nonlinear flutter analysis is essential for ensuring the safety and performance of modern aeroelastic systems. Performing nonlinear stability analysis, however, is a challenging task for aeroelastic systems when relying on traditional approaches. This paper introduces a data-driven approach for nonintrusive nonlinear reduced order modeling and flutter analysis in aeroelastic systems. The proposed approach integrates nonlinear stability analysis for dynamical systems theory with machine learning techniques, enabling nonlinear flutter analysis with a limited number of simulated time-domain trajectories. This data-driven method determines reduced order models of systems exhibiting flutter instabilities and the transformation to and from the state space and the reduced order coordinates. Numerical results are provided to demonstrate the performance of the proposed method for a typical nonlinear airfoil section exhibiting supercritical and subcritical flutter.

Using convolutional neural networks (CNNs) to process ultrasound signals is a widely studied research field with applications in nondestructive evaluation, medical diagnostics, and remote sensing. While some studies have demonstrated the ability of CNNs to classify target objects by analyzing the ultrasound echoes they produced, past approaches rely on fixed, monolithic network architectures that cannot be easily expanded to learn new objects after the initial training. In this presentation, we introduce a modular CNN approach for adaptable ultrasound sensing. Our approach uses a set of small CNNs, each specialized in distinguishing a particular target object by analyzing the echoes the object produced. The received echoes are inputs to specialized CNNs, where each CNN outputs a classification probability, i.e., the likelihood of the shape of the object that produced the input echoes. The object with the highest probability is used as the final prediction. We also demonstrate the adaptability of this modular architecture, which allows expanding the pool of known objects by simply adding CNNs specialized in identifying additional objects. This adaptability shows remarkable perception accuracy despite its simplicity. Additionally, specialized CNNs are further analyzed to understand distinct echo features that determine the object identification.

Echolocating animals can rapidly learn echoic signatures of newly encountered objects from relatively few probing ultrasound pulses. Significant research has recently focused on replicating this ability in engineered systems, but the most promising methods rely on a single deep neural network classifier requiring very large training sets and posing significant challenges to learning additional objects. This work analyzes a perception framework in which multiple (but shallow) specialized convolutional neural networks acting in parallel identify and locate newly encountered objects using small training sets. In this approach, each recognized object has two associated networks, one specialized to identify the object and the other to locate it. Thus, learning an additional object only requires training two new networks without changing the previously trained ones. This paper shows that this architecture performs well even in cluttered environments containing closely spaced objects. More importantly, analysis of the trained neural networks provides insights into the echolocation process, such as salient echo features differentiating the echoes produced by various objects. The specialized neural network framework promises to bring the performance of artificial ultrasound perception closer to that of its biological counterparts.

Detection and identification of nonlinearity is a task of high importance for structural dynamics.On the one hand, identifying nonlinearity in a structure would allow one to build more accurate models of the structure.On the other hand, detecting nonlinearity in a structure, which has been designed to operate in its linear region, might indicate the existence of damage within the structure.Common damage cases which cause nonlinear behaviour are breathing cracks and points where some material may have reached its plastic region.Therefore, it is important, even for safety reasons, to detect when a structure exhibits nonlinear behaviour.In the current work, a method to detect nonlinearity is proposed, based on the distribution of the gradients of a data-driven model, which is fitted on data acquired from the structure of interest.The data-driven model selected for the current application is a neural network.The selection of such a type of model was done in order to not allow the user to decide how linear or nonlinear the model shall be, but to let the training algorithm of the neural network shape the level of nonlinearity according to the training data.The neural network is trained to predict the accelerations of the structure for a time-instant using as input accelerations of previous time-instants, i.e. one-step-ahead predictions.Afterwards, the gradients of the output of the neural network with respect to its inputs are calculated.Given that the structure is linear, the distribution of the aforementioned gradients should be unimodal and quite peaked, while in the case of a structure with nonlinearities, the distribution of the gradients shall be more spread and, potentially, multimodal.To test the above assumption, data from an experimental structure are considered.The structure is tested under different scenarios, some of which are linear and some of which are nonlinear.More specifically, the nonlinearity is introduced as a column-bumper nonlinearity, aimed at simulating the effects of a breathing crack and at different levels, i.e. different values of the initial gap between the bumper and the column.Following the proposed method, the statistics of the distributions of the gradients for the different scenarios can indeed be used to identify cases where nonlinearity is present.Moreover, via the proposed method one is able to quantify the nonlinearity by observing higher values of standard deviation of the distribution of the gradients for lower values of the initial column-bumper gap, i.e. for "more nonlinear" scenarios.

This paper presents a novel terrain-adaptive local trajectory planner designed for the autonomous operation of off-road vehicles on deformable terrains. State-of-the-art solutions either do not account for deformable terrains, or do not offer sufficient robustness or computational speed. To bridge this research gap, the paper introduces a novel model predictive control (MPC) formulation. In contrast to the prevailing state-of-the-art approaches that rely exclusively on hard or soft constraints for obstacle avoidance, the present formulation enhances robustness by incorporating both types of constraints. The effectiveness and robustness of the formulation are evaluated through extensive simulations, encompassing a wide range of randomized scenarios, and compared against state-of-the-art methods. Subsequently, the formulation is augmented with an optimal-control-oriented terramechanics model from the literature, explicitly addressing terrain deformation. Additionally, a terrain estimator employing the unscented Kalman filter is utilized to dynamically adjust the sinkage exponent online, resulting in a terrain-adaptive formulation. This formulation is tested on a physical vehicle in real world experiments against a rigid-terrain formulation as the benchmark. The results showcase the superior safety and performance achieved by the proposed formulation, underscoring the critical significance of integrating terramechanics knowledge into the planning process. Specifically, the proposed terrain-adaptive formulation achieves reduced mean absolute sideslip angle, decreased mean absolute yaw rate, shorter time to goal, and a higher success rate, primarily attributed to its enhanced understanding of terramechanics within the planner.

Qualitative changes can occur in the dynamics of nonlinear systems even for small parameter variations. Such changes are manifestations of bifurcation in dynamical systems. In the context of differential game theory, bifurcations offer insights into the underlying mechanisms driving strategic interactions and identify transitions between different types of behavior. Such critical transitions are tipping points that can dramatically change the outcomes of the game. This work explores the possibility of predicting such qualitative shifts, including supercritical Hopf bifurcations, before they occur using a data-driven forecasting technique. This concept is demonstrated for an attacker-defender game in a limited resource scenario and for an active cybersecurity defense game. The time histories of the system dynamics as it approaches a bifurcation allow one player to detect the existence of bifurcations. This capability provides that player insights into the dynamics of the game and potential defense mechanisms in resource-constrained scenarios.

Background: Chronic kidney disease (CKD) leads to end-stage renal disease (ESRD) or death. A new surrogate marker reflecting its pathophysiology has been needed for CKD therapy. Methods: In this study, we developed a virtual space where data in medical words and those of actual CKD patients were unified by natural language processing and category theory. Results: A virtual space of medical words was constructed from the CKD-related literature (n=165,271) using Word2Vec, in which 106,612 words composed a network. The network satisfied the definition of vector calculations, and retained the meanings of medical words. The data of CKD patients of a cohort study for 3 years (n=26,433) were transformed into the network as medical-word vectors. We let the relationship between vectors of patient data and the outcome (dialysis or death) be a marker (inner product). Then, the inner product accurately predicted the outcomes: C-statistics of 0.911 (95% CI 0.897, 0.924). Cox proportional hazards models showed that the risk of the outcomes in the high-inner-product group was 21.92 (95% CI 14.77, 32.51) times higher than that in the low-inner-product group. Conclusion: This study showed that CKD patients can be treated as a network of medical words that reflect the pathophysiological condition of CKD and the risks of CKD progression and mortality.

Background: Chronic kidney disease (CKD) is the cause of end-stage kidney disease (ESKD), cardiovascular disease, and death, and is categorized into 18 stages on the basis of the estimated glomerular filtration rate (eGFR) and proteinuria. It is difficult to accurately predict CKD progression, because CKD stage cannot be mathematically analyzed in terms of scale and cut-off values. In this study, we determined whether CKD stage transformed into a vector field accurately predicts ESKD risk (CKD vector field model). Methods: The distance from stage G1 A1 to a patient’s current stage in terms of on eGFR and proteinuria was defined, r. The model was constructed to reflect ESKD risk on the basis of systematic review of large cohort studies: ESKD risk=exp(r). Then, the model was validated using data from a cohort study of CKD patients in Japan followed up for three years (n=1,564). Moreover, the directional derivative of the model was developed as an index of CKD progression velocity. Results: Cox proportional hazards models showed the exponential association between r and ESKD risk (p<0.0001). The CKD potential model more accurately predicted ESKD with the areas under the receiver operating characteristic curves adjusted for baseline characteristics 0.81 (95% CI 0.76, 0.87) than CKD stage 0.59 (95% CI 0.54, 0.63) (p<0.0001). Moreover, the directional derivative of the model better predicted the ESKD risk 0.77 (95% CI 0.71, 0.83) than eGFR slope 0.53 (95% CI 0.47, 0.60) (p<0.0001). Conclusion: Those results indicated that the vector field model mathematically unifies CKD stage and eGFR slope and enables the accurate estimation of CKD progression.

Abstract This paper introduces an ultrasound-assisted multistage constant current (UA-MSCC) charging protocol to enhance the charging performance of lithium-ion batteries. In this approach, ultrasound is applied during the final portion of each MSCC charging phase. Experimental results demonstrate that ultrasound decreases the internal resistance of pouch cells by up to 7%, leading to significant increase in charging capacity during each MSCC stage. The overall charging time is reduced by 26% compared to the conventional constant current–constant voltage (CCCV) protocol. The performance improvement delivered by this ultrasound-assisted charging approach is especially large when the battery is charged at low temperatures and to a partial capacity. Notably, the application of ultrasound improves the coulombic efficiency to levels comparable to that at the room temperature when charging in cold environments (0 °C). This approach can be applied to commercial batteries to immediately improve their charging performance, and can be seamlessly integrated into battery management systems. Unlike approaches that necessitate electrode material modifications or electrolyte additives, which require a long development time, this UA-MSCC charging protocol offers a practical and easily applicable solution for improving the battery charging performance.

Abstract The study of blisks is essential to the development of safe and efficient turbomachines. Blisks are geometrically complex structures exposed to high temperatures and pressures that operate at high rotational speeds, and thus are susceptible to fatigue. Therefore, reducing vibration amplitudes in blisks is paramount to avoid failure of these systems. This paper extends previous studies of blisk dampers based on tuned vibration absorbers by analyzing the effects of including energy dissipation through impacts. In this work, the absorber has a ring architecture that also allows impacts. A finite element model of an as-manufactured blisk with 22 blades is used throughout the analysis, and the damper is tuned for experimental operating conditions. Impacts are modeled microscopically, using a novel impact-contact characterization. Results show that impacts significantly enhance the blade response reduction through energy dissipation. Furthermore, impacts slightly increase shift resonant frequencies and are effective for specific combinations of contact surface geometry and material properties.

Developing autonomous vehicles (AVs) that operate in diverse and demanding environments is a difficult challenge. Two fundamental tools that can accelerate this process are testing an AV in diverse simulated environments and identifying core system weaknesses. While most efforts focus on improving these tools for on-road AVs, this paper focuses on an analogous set of tools for off-road AVs. A method called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Black-Box Adversarially Compounding Regret Through Evolution</i> (BACRE) is proposed for identifying adversarial scenarios using an evolutionary algorithm guided by a novel regret-based metric for general navigation tasks. A black-box approach is often preferable when system complexity can be diverse, like with off-road AVs, and when whole-system testing is required. A custom simulation platform is also provided to assist with the automated testing of AVs in diverse, unstructured environments. Numerical experiments demonstrate that BACRE's evolutionary process gradually increases scenario complexity to degrade vehicle performance (an effective and explainable process that comparable methods cannot achieve). Consequently, BACRE can streamline AV development by finding weaknesses at any development stage.

There are cases in which CKD progression is difficult to evaluate, because the changes in estimated glomerular filtration rate (eGFR) and proteinuria sometimes show opposite directions as CKD progresses. Indices and models that enable the easy and accurate risk prediction of end-stage-kidney disease (ESKD) are indispensable to CKD therapy. In this study, we investigated whether a CKD stage coordinate transformed into a vector field (CKD potential model) accurately predicts ESKD risk. Meta-analysis of large-scale cohort studies of CKD patients in PubMed was conducted to develop the model. The distance from CKD stage G2 A1 to a patient's data on eGFR and proteinuria was defined as r. We developed the CKD potential model on the basis of the data from the meta-analysis of three previous cohort studies: ESKD risk = exp(r). Then, the model was validated using data from a cohort study of CKD patients in Japan followed up for three years (n = 1,564). Moreover, the directional derivative of the model was developed as an index of CKD progression velocity. For ESKD prediction in three years, areas under the receiver operating characteristic curves (AUCs) were adjusted for baseline characteristics. Cox proportional hazards models with spline terms showed the exponential association between r and ESKD risk (p<0.0001). The CKD potential model more accurately predicted ESKD with an adjusted AUC of 0.81 (95% CI 0.76, 0.87) than eGFR (p<0.0001). Moreover, the directional derivative of the model showed a larger adjusted AUC for the prediction of ESKD than the percent eGFR change and eGFR slope (p<0.0001). Then, a chart of the transformed CKD stage was developed for implementation in clinical settings. This study indicated that the transformed CKD stage as a vector field enables the easy and accurate estimation of ESKD risk and CKD progression and suggested that vector analysis is a useful tool for clinical studies of CKD and its related diseases.

Identification of flutter margins in modern aeroelastic systems is a challenging task due to increased nonlinearities in novel designs, which can result in instabilities occurring below the linear flutter speed. These instabilities pose a significant risk as they may involve multiple stable solutions, such as large-amplitude self-sustained oscillations. The lack of efficient nonlinear bifurcation analysis methods for experimental systems exacerbates the challenges associated with postflutter analysis. This paper presents a data-driven method for predicting flutter instabilities and bifurcation diagrams of an experimental nonlinear 2-degree-of-freedom (2-DOF) airfoil. The approach uses measurement data from the preflutter regime to forecast the postflutter dynamics, eliminating the need for computationally expensive models. This study is the first application of the recently introduced data-driven bifurcation forecasting method to experimental aeroelastic systems. The results show that the proposed method is accurate, with predictions matching the measured behavior of the system. The presented study provides valuable insights into the nonlinear stability and dynamics of experimental airfoils and demonstrates the potential for applicability of this approach in the analysis of experimental systems. The findings have significant implications for online monitoring and evaluation of the nonlinear dynamics of aeroelastic systems in the aerospace industry, where safety is of crucial importance.

A new marker reflecting the pathophysiology of chronic kidney disease (CKD) has been desired for its therapy. In this study, we developed a virtual space where data in medical words and those of actual CKD patients were unified by natural language processing and category theory. A virtual space of medical words was constructed from the CKD-related literature (n = 165,271) using Word2Vec, in which 106,612 words composed a network. The network satisfied vector calculations, and retained the meanings of medical words. The data of CKD patients of a cohort study for 3 years (n = 26,433) were transformed into the network as medical-word vectors. We let the relationship between vectors of patient data and the outcome (dialysis or death) be a marker (inner product). Then, the inner product accurately predicted the outcomes: C-statistics of 0.911 (95% CI 0.897, 0.924). Cox proportional hazards models showed that the risk of the outcomes in the high-inner-product group was 21.92 (95% CI 14.77, 32.51) times higher than that in the low-inner-product group. This study showed that CKD patients can be treated as a network of medical words that reflect the pathophysiological condition of CKD and the risks of CKD progression and mortality.

A novel hierarchical global trajectory planner is presented to allow highly dynamical nonholonomic off-road autonomous vehicles to achieve high mobility on 3D terrains. On complex terrains with uneven topology, designing safe and feasible vehicle trajectories often demands an understanding of the vehicle's dynamical and nonholonomic constraints. Prior research, however, treats the global planning problem as a path planning problem without effectively accounting for topology or dynamical constraints. To address this gap, this paper presents a three-phase trajectory planning algorithm composed of an A*, a rapidly exploring random tree (RRT), and a local trajectory refining (LTR) phase to incorporate dynamical and nonholonomic constraints on uneven terrain. The algorithm is tested in scenarios with randomized terrain fields and obstacles to demonstrate the necessity for all three phases. The algorithm is shown to have lower cost, higher success rate, and higher computational efficiency compared to state-of-the-art methods. The algorithm is then tested by controlling a simulated MRZR vehicle on a 3D terrain along with a local controller, with comparisons to state-of-the-art algorithms. It is demonstrated that the new algorithm is capable of planning dynamically feasible trajectories with lower cost where the state-of-the-art algorithms fail to perform due to neglecting dynamical vehicle limitations.

We report a discovery that the internal impedance of pouch-type lithium ion batteries with polymer electrolytes can be significantly reduced by ultrasound waves applied at constant temperature. By precluding any temperature effect from ultrasound heating, the observation reveals an innovative mechanism to dynamically improve battery performance in operando. The reduction is 16.9% at room temperature, highlighting the great potential for extending lifespan and enhancing energy efficiency. The reduced impedance also increases the usable capacity by 16.3% at room temperature and 53.4% at low temperature, enabling accelerated charging without overheating. The increased effectiveness of ultrasound at low temperatures improves the performance of batteries that degrade under such conditions. This impedance reduction is reversible and can be tuned by the ultrasound power. A potential mechanism is proposed to understand the process, which is supported by molecular dynamics simulations.

Abstract Prediction and control of emerging pathogens is a fundamental challenge for public health. To meet this challenge, new analytic tools are needed to characterize the underlying dynamics of the geographical spread of pathogens, identify predictable changes in their dynamics, and support strategic planning for disease elimination and control. Nonparametric and model-independent tools are particularly needed. Here, we propose a multivariate method that uses similarity in cross-spectral density between measured spatial time series of disease prevalence as a feature measuring the proximity of a tipping point, i.e., emergence or elimination. In particular, we show that the increase in the average value of spectral similarity in measured epidemiological time series contains crucial information about the underlying dynamics and proximity to critical points in infectious disease systems. Theoretical analysis of a standard metapopulation SIR model and empirical analysis of case reports of pertussis in the continental USA demonstrate that this increase is observed when the disease approaches elimination. Therefore, this nonparametric indicator provides insight into the fundamental underlying state of the epidemiological system, which is key in developing appropriate strategies to more quickly achieve elimination goals.