Publications

Journal: Mechanical Systems and Signal Processing

Abstract: Ultrasonic wave propagation may be used in non-invasive sensing to identify the properties of a fluid in a container. An ultrasonic excitation can be coupled into the liquid or the gas through the walls of the container and detected by a receiver on the other side. However, in addition to this fluid path, another signal will also propagate through the structure and is referred as noise in this study. For most materials, this structural noise will be of higher amplitude compared to the fluid signal due to the large acoustic impedance difference between the solid container and the fluid. In this paper, an active noise cancellation (ANC) method is developed and demonstrated on an experimental setup. It was shown that this method cancels more than 90% of the noise signal in a given time window of interest. A sequential version of this cancellation method is also proposed, and it was shown that its performance improves as the number of canceling excitations is increased. The performance was found to be independent of the location of canceling transducers and position of the silence window but dependent on the width of the silence window. As an example, the ANC method was applied to noise cancellation of a signal travelling through water in a stainless-steel container. The signal-to-noise ratio (SNR) of the water signal in the window of interest was increased by 148% from 13.98 dB to 34.68 dB by using the proposed ANC method.

Journal: Chemical Engineering Journal

Abstract: Flexible capacitive pressure sensors have been widely used in applications ranging from healthcare monitoring to human − machine interaction. However, it remains a huge challenge to overcome the sensitivity attenuation with increasing pressure, as well as maintain sensing performance at high stretching. Herein, inspired by the Miura-ori structure, a Symmetrical Miura-ori Capacitive sensor (SMC sensor) is proposed to realize a positive correlation between sensitivity and pressure within a tunable pressure range, which can be adjusted by modifying the SMC sensor structures. The capacitance is determined through the synergistic effect of distance and opposite area between electrodes conformal to the Miura-ori structure. The SMC sensor exhibits a maximum sensitivity of 0.648 kPa−1 and high stability over 1000 cycles of compression and stretching. Furthermore, the special folding model of the Miura-ori structure avoids shape attenuation in sensitivity during stretching and detects both contractive and stretched strains. These unique characteristics make the proposed sensor promising for general physiological signal monitoring, even with high stretching and environmental adaptability.

Journal: Physics of Living Systems

Abstract: The freshwater polyp Hydra is a popular biological model system; however, we still do not understand one of its most salient behaviours, the generation of spontaneous body wall contractions. Here, by applying experimental fluid dynamics analysis and mathematical modelling, we provide functional evidence that spontaneous contractions of body walls enhance the transport of chemical compounds from and to the tissue surface where symbiotic bacteria reside. Experimentally, a reduction in the frequency of spontaneous body wall contractions is associated with a changed composition of the colonizing microbiota. Together, our findings suggest that spontaneous body wall contractions create an important fluid transport mechanism that (1) may shape and stabilize specific host-microbe associations and (2) create fluid microhabitats that may modulate the spatial distribution of the colonizing microbes. This mechanism may be more broadly applicable to animal-microbe interactions since research has shown that rhythmic spontaneous contractions in the gastrointestinal tracts are essential for maintaining normal microbiota.

Journal: Dynamic Games and Applications

Abstract: We use the Bernstein polynomials of degree d as the basis for constructing a uniform approximation to the rate of evolution (related to the fixation probability) of a species in a two component finite-population, well-mixed, frequency-dependent evolutionary game setting. The approximation is valid over the full range 0≤𝑤≤1, where w is the selection pressure parameter, and converges uniformly to the exact solution as 𝑑→∞. We compare it to a widely used non-uniform approximation formula in the weak-selection limit (𝑤∼0) as well as numerically computed values of the exact solution. Because of a boundary layer that occurs in the weak-selection limit, the Bernstein polynomial method is more efficient at approximating the rate of evolution in the strong selection region (𝑤∼1) (requiring the use of fewer modes to obtain the same level of accuracy) than in the weak selection regime.

Journal: Journal of Applied Mechanics

Abstract: Combined systems, which are flexible structures carrying moving subsystems, are seen in various applications. Due to structure–subsystem interactions, the structure in a combined system encounters jump discontinuities in its internal forces (such as the bending moment and shear force of a beam). Accurate estimation of such jump discontinuities is important to the performance, safety, and longevity of a combined system. Because of the time-varying nature and complexity of structure–subsystem interactions, conventional series solution methods experience slow convergence, and the Gibbs phenomenon in computation and the improved series expansion methods are limited to certain proportionally damped continua under moving forces and moving oscillators. In this paper, a novel modified series expansion method (MSEM) is proposed to resolve the aforementioned issues with the existing series solution methods. Through the introduction of a jump influence function, the proposed method produces fast-convergent series solutions and accurately predicts the jump discontinuities without the Gibbs phenomenon. The MSEM is applicable to structures with nonproportional damping and subject to arbitrary boundary conditions, and it can easily manage general M-DOF moving subsystems having multiple contact points with a supporting structure. As an important result of this investigation, a mathematical proof of the convergence of the MSEM-based solutions is given for the first time. Additionally, two numerical examples are presented to demonstrate the accuracy, efficiency, and versatility of the proposed MSEM in modeling and analysis of combined systems.

Journal: Journal of Biomechanical Engineering

Abstract: Type B aortic dissection is a life-threatening medical emergency that can result in rupture of the aorta. Due to the complexity of patient-specific characteristics, only limited information on flow patterns in dissected aortas has been reported in the literature. Leveraging the medical imaging data for patient-specific in vitro modeling can complement the hemodynamic understanding of aortic dissections. We propose a new approach toward fully automated patient-specific type B aortic dissection model fabrication. Our framework uses a novel deep-learning-based segmentation for negative mold manufacturing. Deep-learning architectures were trained on a dataset of 15 unique computed tomography scans of dissection subjects and were blind-tested on 4 sets of scans, which were targeted for fabrication. Following segmentation, the three-dimensional models were created and printed using polyvinyl alcohol. These models were then coated with latex to create compliant patient-specific phantom models. The magnetic resonance imaging (MRI) structural images demonstrate the ability of the introduced manufacturing technique for creating intimal septum walls and tears based on patient-specific anatomy. The in vitro experiments show the fabricated phantoms generate physiologically-accurate pressure results. The deep-learning models also show high similarity metrics between manual segmentation and autosegmentation where Dice metric is as high as 0.86. The proposed deep-learning-based negative mold manufacturing method facilitates an inexpensive, reproducible, and physiologically-accurate patient-specific phantom model fabrication suitable for aortic dissection flow modeling.

Journal: arXiv preprint arXiv:2306.04895

Abstract: The solution of probabilistic inverse problems for which the corresponding forward problem is constrained by physical principles is challenging. This is especially true if the dimension of the inferred vector is large and the prior information about it is in the form of a collection of samples. In this work, a novel deep learning based approach is developed and applied to solving these types of problems. The approach utilizes samples of the inferred vector drawn from the prior distribution and a physics-based forward model to generate training data for a conditional Wasserstein generative adversarial network (cWGAN). The cWGAN learns the probability distribution for the inferred vector conditioned on the measurement and produces samples from this distribution. The cWGAN developed in this work differs from earlier versions in that its critic is required to be 1-Lipschitz with respect to both the inferred and the measurement vectors and not just the former. This leads to a loss term with the full (and not partial) gradient penalty. It is shown that this rather simple change leads to a stronger notion of convergence for the conditional density learned by the cWGAN and a more robust and accurate sampling strategy. Through numerical examples it is shown that this change also translates to better accuracy when solving inverse problems. The numerical examples considered include illustrative problems where the true distribution and/or statistics are known, and a more complex inverse problem motivated by applications in biomechanics.

Journal: Energy Storage Materials

Abstract: The rapid development of portable and scalable electronics requires the production of high-performing, miniaturized energy storage devices with great flexibility and dimensional liberty. In recent years, printed capacitors have emerged as a promising means of meeting these demands. Printed flexible solid-state capacitors are being considered as next-generation energy storage systems because of their flexibility, portability, low cost, scalability, long cycle stability and the option of charging or discharging securely. Here we use sustainable and toxin-free photosynthetic protein complexes to fabricate solid-state flexible Photo-electro micro-capacitors as flexible power packs that are operable under indoor illumination. Electrohydrodynamic (EHD) printing was used to print biohybrid Photo-electro protein micro-capacitors that exhibited high performance uniformity and operational stability. Devices could be connected in either series or parallel configurations to modulate the operating voltage window and charge-discharge time. A specific capacitance of 110 mF g−1 was obtained at a scan rate of 10 mV s−1 and was retained at 91% of the initial value after 10,000 charge/discharge cycles at a current density of 0.063 mA g−1. Devices also displayed mechanical stability and robustness, retaining 93% of initial capacitance after 1000 cycles of bending. The data demonstrate that these micro-capacitors can deliver an economical and practical option as flexible energy storage and delivery devices for applications where exposure is primarily to indoor light.

Journal: Journal of Computing and Information Science in Engineering

Abstract: Deep learning-based image segmentation methods have showcased tremendous potential in defect detection applications for several manufacturing processes. Currently, majority of deep learning research for defect detection focuses on manufacturing processes where the defects have well-defined features and there is tremendous amount of image data available to learn such a data-dense model. This makes deep learning unsuitable for defect detection in high-mix low volume manufacturing applications where data are scarce and the features of defects are not well defined due to the nature of the process. Recently, there has been an increased impetus towards automation of high-performance manufacturing processes such as composite prepreg layup. Composite prepreg layup is high-mix low volume in nature and involves manipulation of a sheet-like material. In this work, we propose a deep learning framework to detect wrinkle-like defects during the composite prepreg layup process. Our work focuses on three main technological contributions: (1) generation of physics aware photo-realistic synthetic images with the combination of a thin-shell finite element-based sheet simulation and advanced graphics techniques for texture generation, (2) an open-source annotated dataset of 10,000 synthetic images and 1000 real process images of carbon fiber sheets with wrinkle-like defects, and (3) an efficient two-stage methodology for training the deep learning network on this hybrid dataset. Our method can achieve a mean average precision (mAP) of 0.98 on actual production data for detecting defects.

Journal: 2023 American Control Conference (ACC)

Abstract: This paper presents a novel method to control humanoid robot dynamic loco-manipulation with multiple contact modes via multi-contact Model Predictive Control (MPC) framework. The proposed framework includes a multi-contact dynamics model capable of capturing various contact modes in loco-manipulation, such as hand-object contact and foot-ground contacts. Our proposed dynamics model represents the object dynamics as an external force acting on the system, which simplifies the model and makes it feasible for solving the MPC problem. In numerical validations, our multi-contact MPC framework only needs contact timings of each task and desired states to give MPC the knowledge of changes in contact modes in the prediction horizons in loco-manipulation. The proposed framework can control the humanoid robot to complete multitasks dynamic loco-manipulation applications such as efficiently picking up and dropping off objects while turning and walking.

Journal: 2023 IEEE International Conference on Robotics and Automation (ICRA)

Abstract: We present a new framework for implementing real-time embedded safety-critical controllers which utilizes hybrid computing to address the issue of limited computational resources, a problem that is particularly prevalent in microrobotics. In our approach, the nominal stabilizing control algorithm is implemented digitally while the safety-critical quadratic program is solved via a dedicated analog resistor array. We apply this hybrid computing architecture to a simulated collision avoidance task for a micro-aerial vehicle and show the benefit relative to a purely-digital implementation. By leveraging analog quadratic programming on the Crazyflie 2.1 micro quadrotor, a reduction in overall processing time from 8.9 ms to 0.6 ms is estimated for this computationally-limited system. We further display the viability of our proposed safety-critical control framework through real-time flight demonstrations, utilizing a novel prototype analog circuit tethered to the Crazyflie. The flight results confirm the functionality of the control structure and prototype circuit while highlighting the overall capabilities of hybrid computing.

Journal: 2025 IEEE International Conference on Robotics and Automation (ICRA)

Abstract: In this work, we present an inverse reinforcement learning approach for solving the problem of task sequencing for robots in complex manufacturing processes. Our proposed framework is adaptable to variations in process and can perform sequencing for entirely new parts. We prescribe an approach to capture feature interactions in a demonstration dataset based on a metric that computes feature interaction coverage. We then actively learn the expert's policy by keeping the expert in the loop. Our training and testing results reveal that our model can successfully learn the expert's policy. We demonstrate the performance of our method on a real-world manufacturing application where we transfer the policy for task sequencing to a manipulator. Our experiments show that the robot can perform these tasks to produce human-competitive performance. Code and video can be found at: https://sites.google.com/usc.edu/irlfortasksequencing

Journal: 2024 IEEE International Conference on Robotics and Automation (ICRA)

Abstract: We consider the problem of task assignment and scheduling for human-robot teams to enable the efficient completion of complex problems, such as satellite assembly. In high-mix, low volume settings, we must enable the human-robot team to handle uncertainty due to changing task requirements, potential failures, and delays to maintain task completion efficiency. We make two contributions: (1) we account for the complex interaction of uncertainty that stems from the tasks and the agents using a multi-agent concurrent MDP framework, and (2) we use Mixed Integer Linear Programs and contingency sampling to approximate action values for task assignment. Our results show that our online algorithm is computationally efficient while making optimal task assignments compared to a value iteration baseline. We evaluate our method on a 24-task representative assembly and a real-world 60-task satellite assembly, and we show that we can find an assignment that results in a near-optimal makespan.

Journal: 2023 IEEE International Conference on Robotics and Automation (ICRA)

Abstract: Recent studies on quadruped robots have focused on either locomotion or mobile manipulation using a robotic arm. However, legged robots can manipulate large objects using non-prehensile manipulation primitives, such as planar pushing, to drive the object to the desired location. This paper presents a novel hierarchical model predictive control (MPC) for contact optimization of the manipulation task. Using two cascading MPCs, we split the loco-manipulation problem into two parts: the first to optimize both contact force and contact location between the robot and the object, and the second to regulate the desired interaction force through the robot locomotion. Our method is successfully validated in both simulation and hardware experiments. While the baseline locomotion MPC fails to follow the desired trajectory of the object, our proposed approach can effectively control both object's position and orientation with minimal tracking error. This capability also allows us to perform obstacle avoidance for both the robot and the object during the loco-manipulation task.

Journal: ACS omega

Abstract: Soluble signaling molecules and extracellular matrix (ECM) regulate cell dynamics in various biological processes. Wound healing assays are widely used to study cell dynamics in response to physiological stimuli. However, traditional scratch-based assays can damage the underlying ECM-coated substrates. Here, we use a rapid, non-destructive, label-free magnetic exclusion technique to form annular aggregates of bronchial epithelial cells on tissue-culture treated (TCT) and ECM-coated surfaces within 3 h. The cell-free areas enclosed by the annular aggregates are measured at different times to assess cell dynamics. The effects of various signaling molecules, including epidermal growth factor (EGF), oncostatin M, and interleukin 6, on cell-free area closures are investigated for each surface condition. Surface characterization techniques are used to measure the topography and wettability of the surfaces. Further, we demonstrate the formation of annular aggregates on human lung fibroblast-laden collagen hydrogel surfaces, which mimic the native tissue architecture. The cell-free area closures on hydrogels indicate that the substrate properties modulate EGF-mediated cell dynamics. The magnetic exclusion-based assay is a rapid and versatile alternative to traditional wound healing assays.

Journal: Small

Abstract: Optical lenses require feature resolution and surface roughness that are beyond most (3D) printing methods. A new continuous projection-based vat photopolymerization process is reported that can directly shape polymer materials into optical lenses with microscale dimensional accuracy (< 14.7 µm) and nanoscale surface roughness (< 20 nm) without post-processing. The main idea is to utilize frustum layer stacking, instead of the conventional 2.5D layer stacking, to eliminate staircase aliasing. A continuous change of mask images is achieved using a zooming-focused projection system to generate the desired frustum layer stacking with controlled slant angles. The dynamic control of image size, objective and imaging distances, and light intensity involved in the zooming-focused continuous vat photopolymerization are systematically investigated. The experimental results reveal the effectiveness of the proposed process. The 3D-printed optical lenses with various designs, including parabolic lenses, fisheye lenses, and a laser beam expander, are fabricated with a surface roughness of 3.4 nm without post-processing. The dimensional accuracy and optical performance of the 3D-printed compound parabolic concentrators and fisheye lenses within a few millimeters are investiagted. These results highlight the rapid and precise nature of this novel manufacturing process, demonstrating a promising avenue for future optical component and device fabrication.

Journal: Journal of Sound and Vibration

Abstract: An approach for vibration analysis of three-dimensional motion beam systems is formulated. The proposed method employs analytical solutions that are used to formulate a unified, three-dimensional dynamic representation the elements of beam systems, including beams, rigid bodies, and point masses. Interconnections between system’s components and their connections to the inertial frame are formulated as motion constraints. The interconnections can be rigid or flexible and can be located at arbitrary locations on the system components. The unified representations of different types of system elements and of different interconnection allows vibration analysis of complex beam systems. A procedure is developed for computation of natural frequencies and mode shapes by employing a sequence of orthogonal transformations. A three-dimensional structure that includes flexibly and rigidly connected beams and rigid bodies with different types of connections, is used to demonstrate the capabilities of the proposed method. The accuracy of the approach is verified by restricting the structure to planar motion with rigid connections and comparing the results to results of obtained in previous studies. The example structure is used to demonstrate the ability of the proposed approach to conduct vibrational analysis study of three-dimensional structures with different boundary conditions. Moreover, it also demonstrates suitability of the approach for studying sensitivity of vibrations characteristics, such as modal frequencies, with respect to system parameters, e.g. interconnections stiffness.

Journal: Advanced Functional Materials

Abstract: Wide bandgap (WBG) semiconductors have attracted significant research interest for the development of a broad range of flexible electronic applications, including wearable sensors, soft logical circuits, and long-term implanted neuromodulators. Conventionally, these materials are grown on standard silicon substrates, and then transferred onto soft polymers using mechanical stamping processes. This technique can retain the excellent electrical properties of wide bandgap materials after transfer and enables flexibility; however, most devices are constrained by 2D configurations that exhibit limited mechanical stretchability and morphologies compared with 3D biological systems. Herein, a stamping-free micromachining process is presented to realize, for the first time, 3D flexible and stretchable wide bandgap electronics. The approach applies photolithography on both sides of free-standing nanomembranes, which enables the formation of flexible architectures directly on standard silicon wafers to tailor the optical transparency and mechanical properties of the material. Subsequent detachment of the flexible devices from the support substrate and controlled mechanical buckling transforms the 2D precursors of wide band gap semiconductors into complex 3D mesoscale structures. The ability to fabricate wide band gap materials with 3D architectures that offer device-level stretchability combined with their multi-modal sensing capability will greatly facilitate the establishment of advanced 3D bio-electronics interfaces.

Journal: Nature Biomedical Engineering

Abstract: Serial assessment of the biomechanical properties of tissues can be used to aid the early detection and management of pathophysiological conditions, to track the evolution of lesions and to evaluate the progress of rehabilitation. However, current methods are invasive, can be used only for short-term measurements, or have insufficient penetration depth or spatial resolution. Here we describe a stretchable ultrasonic array for performing serial non-invasive elastographic measurements of tissues up to 4 cm beneath the skin at a spatial resolution of 0.5 mm. The array conforms to human skin and acoustically couples with it, allowing for accurate elastographic imaging, which we validated via magnetic resonance elastography. We used the device to map three-dimensional distributions of the Young’s modulus of tissues ex vivo, to detect microstructural damage in the muscles of volunteers before the onset of soreness and to monitor the dynamic recovery process of muscle injuries during physiotherapies. The technology may facilitate the diagnosis and treatment of diseases affecting tissue biomechanics.

Journal: Journal of Nonlinear Science

Abstract: This study seeks to provide physical insight into the friction-driven crawling locomotion of systems with radially symmetric bodies. Laboratory experiments with a tripedal robot show that both translation and rotation can be achieved with just three independently actuated rigid limbs, i.e., with 3 degrees-of-freedom. These observations are rationalized using a simple mathematical model, which assumes that the friction at each limb is linearly proportional to the normal force at the contact point, and opposes the direction of motion. This dynamic model reproduces experimental observations across an extensive parametric sweep involving sinusoidal rotation of the limbs with varying amplitudes and phase shifts. Model predictions highlight the role played by time-varying normal forces at the contact points. These predictions are confirmed using embedded force transducers in the limbs. We present a further simplified analysis explaining that a geometric nonlinearity is induced in the dynamics from the radial symmetry and that this nonlinearity is essential to the generation of pure translation. We also show that this nonlinearity can be amplified by a cyclic time-varying limb length variation. These results provide a framework for further study of radially symmetric movers.

Journal: Proceedings of a Royal Society A

Abstract: Kirigami metamaterials dramatically change their shape through a coordinated motion of nearly rigid panels and flexible slits. Here, we study a model system for mechanism-based planar kirigami featuring periodic patterns of quadrilateral panels and rhombi slits, with the goal of predicting their engineering scale response to a broad range of loads. We develop a generalized continuum model based on the kirigami’s effective (cell-averaged) nonlinear deformation, along with its slit actuation and gradients thereof. The model accounts for three sources of elasticity: a strong preference for the effective fields to match those of a local mechanism, inter-panel stresses arising from gradients in slit actuation, and distributed hinge bending. We provide a finite-element formulation of this model and implement it using the commercial software Abaqus. Simulations of the model agree quantitatively with experiments across designs and loading conditions.

Journal: Journal of Physics D: Applied Physics

Abstract: The next generation of advanced combustion devices is being developed to operate under ultra-high-pressure conditions. However, under such extreme conditions, flame tends to become unstable and measurement of fundamental properties such as the laminar flame speed becomes challenging. One potential method to resolve this issue is measuring the ignition-affected region during spherically expanding flame experiments. The flame in this region is more resistant to perturbations and remains smooth due to the high stretch rates (i.e. small radii). Stable flame propagation allows for improved flame measurement, however, the experimentally observed kernel propagation is a function of both inflammation and ignition plasma. Therefore, the goal of the present study is to better understand the plasma formation and propagation during the ignition process, which would allow for reliable laminar flame speed measurements. To accomplish this goal, thermal plasma operating at high pressures is studied with emphasis on the spark energy effects on the formation of the ignition kernel. The thermal effect of the plasma is experimentally observed using a high-speed Schlieren imaging system. The energy dissipated within the plasma is measured with the use of voltage and current probes with a measurement of plasma sheath voltage drop as an input to numerical modeling. The measured kernel propagation rate is used to assess the accuracy of the model. The experiments and modeling are conducted in dry air at 1, 3, and 5 atm as well as in CH4-N2 mixtures at 1 atm, and kernel radius, temperature, and mass are reported. The voltage-drop (as a non-thermal loss) is measured to be approximately 330 $ \pm $ 5 V (dry air at 1 atm) for glow plasma with a large dependency on pressure, gas composition, electrode surface quality, electrode geometry, electrode shape, and current density. The same loss within the arc plasma is measured to be 15 $ \pm $ 5 V, however the arc phase loss which agrees with arc propagation is significantly higher (∼45 V) which suggest additional unaccounted for phenomena occurring during the arc phase. With these losses, the modeling results are shown to predict the final kernel radius within 10%–20% of the observed kernel size. The difference found between the modeling and experimental results is determined to be a result of assuming that the primary loss mechanism (voltage drop across sheath formation) remains constant for the duration of glow discharge. The discrepancy for arc discharge is discussed with several potential sources, however, additional studies are required to better understand how the arc formation affects the kernel propagation.

Journal: The Journal of Urology

Abstract: INTRODUCTION AND OBJECTIVE: To assess the potential value of using machine learning (ML) approaches to derive risk prediction models for urothelial bladder cancer (BCa) recurrence at 1, 3, and 5 years after radical cystectomy (RC).

Journal: Water Resources Research

Abstract: Many rivers and streams are ungauged or poorly gauged and predicting streamflow in such watersheds is challenging. Although streamflow signals result from processes with different frequencies, they can be “sparse” or have a “lower-dimensional” representation in a transformed feature space. In such cases, if this appropriate feature space can be identified from streamflow data in gauged watersheds by dimensionality reduction, streamflow in poorly gauged watersheds can be predicted with a few measurements taken. This study utilized this framework, named data-driven sparse sensing (DSS), to predict daily-scale streamflow in 543 watersheds across the contiguous United States. A tailored library of features was extracted from streamflow training data in watersheds within the same climatic region, and this feature space was used to reconstruct streamflow in poorly gauged watersheds and identify the optimal timings for measurement. Among different regions, streamflow in snowmelt-dominated and baseflow-dominated watersheds (e.g., Rocky Mountains) was more effectively predicted with fewer streamflow measurements taken. The prediction efficiency in some rainfall-dominated regions, for example, New England and the Pacific coast, increased significantly with an increasing number of measurements. The spatial variability of prediction efficiency can be attributed to the process-driven mechanisms and the dimensionality of watershed dynamics. Storage-dominated systems are lower-dimensional and more predictable than rainfall-dominated systems. Measurements taken during periods with large streamflow magnitudes and/or variances are more informative and lead to better predictions. This study demonstrates that DSS can be an especially useful technique to integrate ground-based measurements with remotely sensed data for streamflow prediction, sensor placement, and watershed classification.

Journal: arXiv preprint arXiv:2304.04862

Abstract: Low-fidelity data is typically inexpensive to generate but inaccurate. On the other hand, high-fidelity data is accurate but expensive to obtain. Multi-fidelity methods use a small set of high-fidelity data to enhance the accuracy of a large set of low-fidelity data. In the approach described in this paper, this is accomplished by constructing a graph Laplacian using the low-fidelity data and computing its low-lying spectrum. This spectrum is then used to cluster the data and identify points that are closest to the centroids of the clusters. High-fidelity data is then acquired for these key points. Thereafter, a transformation that maps every low-fidelity data point to its bi-fidelity counterpart is determined by minimizing the discrepancy between the bi- and high-fidelity data at the key points, and to preserve the underlying structure of the low-fidelity data distribution. The latter objective is achieved by relying, once again, on the spectral properties of the graph Laplacian. This method is applied to a problem in solid mechanics and another in aerodynamics. In both cases, this methods uses a small fraction of high-fidelity data to significantly improve the accuracy of a large set of low-fidelity data.

Journal: ACS Applied Nano Materials

Abstract: To achieve high-performance printed electronic devices, scalable and cost-effective printing of high-quality metallic electrodes with narrow gaps, such as for transistors with short channel lengths, is desirable. Here, we demonstrate short channel (<10 μm) transistors, using thin (<100–200 nm) electrodes fabricated by flexographic printing with nanoporous stamps, with single-wall carbon nanotubes (SWCNTs) as the network semiconductor. The nanoporous stamps comprise polymer-coated vertically aligned carbon nanotubes and facilitate control of the printed ink thickness in the 50–200 nm range. The measured on–off ratio and mobility meet or exceed those of previously reported SWCNT network transistors fabricated by alternative printing methods.

Journal: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction

Abstract: We focus on enabling robots to proactively assist humans in assembly tasks by adapting to their preferred sequence of actions. Much work on robot adaptation requires human demonstrations of the task. However, human demonstrations of real-world assemblies can be tedious and time-consuming. Thus, we propose learning human preferences from demonstrations in a shorter, canonical task to predict user actions in the actual assembly task. The proposed system uses the preference model learned from the canonical task as a prior and updates the model through interaction when predictions are inaccurate. We evaluate the proposed system in simulated assembly tasks and in a real-world human-robot assembly study and we show that both transferring the preference model from the canonical task, as well as updating the model online, contribute to improved accuracy in human action prediction. This enables the robot to proactively assist users, significantly reduce their idle time, and improve their experience working with the robot, compared to a reactive robot.

Journal: arXiv preprint arXiv:2303.06741

Abstract: Despite the potential benefits of collaborative robots, effective manipulation tasks with quadruped robots remain difficult to realize. In this paper, we propose a hierarchical control system that can handle real-world collaborative manipulation tasks, including uncertainties arising from object properties, shape, and terrain. Our approach consists of three levels of controllers. Firstly, an adaptive controller computes the required force and moment for object manipulation without prior knowledge of the object's properties and terrain. The computed force and moment are then optimally distributed between the team of quadruped robots using a Quadratic Programming (QP)-based controller. This QP-based controller optimizes each robot's contact point location with the object while satisfying constraints associated with robot-object contact. Finally, a decentralized loco-manipulation controller is designed for each robot to apply manipulation force while maintaining the robot's stability. We successfully validated our approach in a high-fidelity simulation environment where a team of quadruped robots manipulated an unknown object weighing up to 18 kg on different terrains while following the desired trajectory.

Journal: arXiv preprint arXiv:2303.05711

Abstract: In this paper, we propose a novel framework for synthesizing a single multimodal control policy capable of generating diverse behaviors (or modes) and emergent inherent transition maneuvers for bipedal locomotion. In our method, we first learn efficient latent encodings for each behavior by training an autoencoder from a dataset of rough reference motions. These latent encodings are used as commands to train a multimodal policy through an adaptive sampling of modes and transitions to ensure consistent performance across different behaviors. We validate the policy performance in simulation for various distinct locomotion modes such as walking, leaping, jumping on a block, standing idle, and all possible combinations of inter-mode transitions. Finally, we integrate a task-based planner to rapidly generate open-loop mode plans for the trained multimodal policy to solve high-level tasks like reaching a goal position on a challenging terrain. Complex parkour-like motions by smoothly combining the discrete locomotion modes were generated in 3 min. to traverse tracks with a gap of width 0.45 m, a plateau of height 0.2 m, and a block of height 0.4 m, which are all significant compared to the dimensions of our mini-biped platform.

Journal: Journal of the Acoustical Society of America

Abstract: This study uses the singular perturbation method to analyze the streaming flow around a pulsating bubble at the velocity antinode of a standing wave. The bubble radially and laterally oscillates with small nondimensional amplitudes of 𝜀̀ and 𝜀, respectively. The momentum equation is expanded using 𝜀 . The frequency parameter ∣∣𝑀∣∣ , which is the ratio of the bubble radius to the viscous length, is included in the expanded equations as 𝑂(∣∣𝑀∣∣−1) . Four boundary conditions are solved: non-pulsating and pulsating assuming no-slip and shear-free boundaries. For the non-pulsating bubble, the streaming is on the order of 𝑂(∣∣𝑀∣∣−1) for the shear-free boundary. The flow has a quadrupole pattern, with direction from the equator to the poles. However, for the non-pulsating bubble with the no-slip boundary, the flow pattern is from the poles to the equator and the direction reverses after a critical value of ∣∣𝑀∣∣=13.3 . When bubble pulsation is introduced, the intensity of the streaming increases and is proportional to ∣∣𝑀∣∣ . The flow pattern is dipole with a direction from the south to the north pole for the shear-free boundary. For the non-slip boundary, the flow is quadrupole for small values of ∣∣𝑀∣∣ and varies with the phase shift 𝜙 . As ∣∣𝑀∣∣ increases, the flow intensifies and becomes dipole. For both cases, the maximum velocity is at the phase shift angle 𝜙=135° and ∣∣𝑀∣∣=10.

Journal: arXiv preprint arXiv:2303.04985

Abstract: This paper presents a novel approach for controlling humanoid robots pushing heavy objects using kinodynamics-based pose optimization and loco-manipulation MPC. The proposed pose optimization plans the optimal pushing pose for the robot while accounting for the unified object-robot dynamics model in steady state, robot kinematic constraints, and object parameters. The approach is combined with loco-manipulation MPC to track the optimal pose. Coordinating pushing reaction forces and ground reaction forces, the MPC allows accurate tracking in manipulation while maintaining stable locomotion. In numerical validation, the framework enables the humanoid robot to effectively push objects with a variety of parameter setups. The pose optimization generates different pushing poses for each setup and can be efficiently solved as a nonlinear programming (NLP) problem, averaging 250 ms. The proposed control scheme enables the humanoid robot to push object with a mass of up to 20 kg (118% of the robot's mass). Additionally, the MPC can recover the system when a 120 N force disturbance is applied to the object.

Journal: Journal of the American College of Cardiology

Abstract: Background: The intrinsic frequency (IF) method is a recently developed systems-based approach for evaluating cardiovascular dynamics using arterial pressure waveforms. In this study, we aim to detect myocardial ischemia from a randomly selected carotid pressure waveform by applying hybrid IF-machine learning (ML) classifiers.

Journal: ChemRxiv

Abstract: Fibre reinforced thermoplastic polymer (FRTP) composites have been used for a wide range of engineering applications (e.g. in transport, construction, energy, etc) due to their excellent mechanical properties and ease of repair and recycling. In recent years, FRTP is increasingly deployed as an alternative to conventional thermoset carbon fibre reinforced epoxy (CF/epoxy) composites, for the purpose of reducing the carbon footprint and contributing to a sustainable manufacturing agenda. Machining of FRTP remains an indispensable process to achieve rapid parts assembly whilst meeting stringent geometric tolerances. However, due to the heterogeneous structure and high thermal sensitivity of FRTP, a range of machining-induced damages (such as matrix smearing, thermal degradation, delamination, burr and surface cavity) often occur, leading to concerns on machined parts quality and reliability. To date, composite machining studies have been mostly focused on conventional thermoset CF/epoxy and there is a lack of an up-to-date, in-depth review of the latest advancement concerning the machining of FRTP. This paper provides a state-of-the-art overview on the recent developments in FRTP machining over the past decade, with a particular emphasis on machining characteristics, damage mechanisms as well as the challenges facing such manufacturing process. The purpose is to present the composite manufacturing community with a timely update, which may guide and inspire further research and development for future FRTP manufacturing.

Journal: Journal of Applied Mechanics

Abstract: This paper develops an alternative description of the general equation of motion for constrained mechanical systems with singular mass matrices. The formulation gives a new explicit equation of motion for such systems and provides a simple and elegant way to interpret the manner in which Nature orchestrates constrained motion, something that was not possible for such systems in the past.

Journal: Journal of the European Ceramic Society

Abstract: It is still a challenge to prepare high-performance ceramics due to the inherent problems of poor compatibility between ceramic powder and photosensitive resin in Vat photopolymerization. Herein, we used two types of coupling agents, aluminic acid ester and silane coupling agent, to modify Al2O3 ceramic powder based on transesterification reactions. In addition, after pressureless sintering, hot isostatic pressing sintering was followed to further improve the mechanical performance of the Al2O3 ceramic. Furthermore, Al2O3 ceramic cutting tools with chip breaker grooves were prepared based on vat photopolymerization and adopted for cutting for the first time. Simulation and experimental results showed that the as-prepared ceramic cutting tool with grooves has the best surface processing quality and the medium flank wear amount compared with that without grooves and even Kyocera commercial ceramic cutting tool. The results open a new avenue for the preparation of high-performance and complex-shaped ceramic cutting tools in the future.

Journal: Physiological Measurement

Abstract: Objective. Children with heart failure have higher rates of emergency department utilization, health care expenditure, and hospitalization. Therefore, a need exists for a simple, non-invasive, and inexpensive method of screening for left ventricular (LV) dysfunction. We recently demonstrated the practicality and reliability of a wireless smartphone-based handheld device in capturing carotid pressure waveforms and deriving cardiovascular intrinsic frequencies (IFs) in children with normal LV function. Our goal in this study was to demonstrate that an IF-based machine learning method (IF-ML) applied to noninvasive carotid pressure waveforms can distinguish between normal and abnormal LV ejection fraction (LVEF) in pediatric patients. Approach. Fifty patients ages 0 to 21 years underwent LVEF measurement by echocardiogram or cardiac magnetic resonance imaging. On the same day, patients had carotid waveforms recorded using Vivio. The exclusion criterion was known vascular disease that would interfere with obtaining a carotid artery pulse. We adopted a hybrid IF- Machine Learning (IF-ML) method by applying physiologically relevant IF parameters as inputs to Decision Tree classifiers. The threshold for low LVEF was chosen as <50%. Main results. The proposed IF-ML method was able to detect an abnormal LVEF with an accuracy of 92% (sensitivity = 100%, specificity = 89%, area under the curve (AUC) = 0.95). Consistent with previous clinical studies, the IF parameter ${\omega }_{1}$ was elevated among patients with reduced LVEF. Significance. A hybrid IF-ML method applied on a carotid waveform recorded by a hand-held smartphone-based device can differentiate between normal and abnormal LV systolic function in children with normal cardiac anatomy.

Journal: Physica D: Nonlinear Phenomena

Abstract: We model Covid-19 vaccine uptake as a reinforcement learning dynamic between two populations: the vaccine adopters, and the vaccine hesitant. Using data available from the Center for Disease Control (CDC), we estimate the payoff matrix governing the interaction between these two groups over time and show they are playing a Hawk–Dove evolutionary game with an internal evolutionarily stable Nash equilibrium (the asymptotic percentage of vaccinated in the population). We then ask whether vaccine adoption can be improved by implementing dynamic incentive schedules that reward/punish the vaccine hesitant, and if so, what schedules are optimal and how effective are they likely to be? When is the optimal time to start an incentive program, how large should the incentives be, and is there a point of diminishing returns? By using a tailored replicator dynamic reinforcement learning model together with optimal control theory, we show that well designed and timed incentive programs can improve vaccine uptake by shifting the Nash equilibrium upward in large populations, but only so much, and incentive sizes above a certain threshold show diminishing returns.

Journal: Journal of Manufacturing Processes

Abstract: Although new generation carbon fibre reinforced thermoplastic (CFRTP) such as carbon fibre reinforced polyetherketoneketone (CF/PEKK) is a promising sustainable alternative to the conventional thermoset carbon fibre reinforced plastic (CFRP), there is a lack of literature regarding its machining performance. This is the first study unveiling the machining temperature evolution during drilling of CF/PEKK and its potential impact on the associated material damages. Through a comparative study with the thermoset CF/epoxy, the disparate drilling performance of the two composites has been uncovered, and the results were found to be closely related to the materials' thermal/mechanical properties. Specifically, CF/PEKK produces continuous chips due to its excellent ductility and thermal sensitivity, whereas CF/epoxy produces segmented chips due to its brittle nature. CF/PEKK generates up to 40 N (50.5 %) higher thrust force, 87.6 °C (98.9 %) higher hole wall temperature and 61.1 °C (48.8 %) higher chip temperature than that of CF/epoxy. This has been correlated to the longer tool-chip contact length of CF/PEKK and its unique chip morphology. Despite the greater thrust force/temperature generation, CF/PEKK shows 55.7 % lower delamination damage as compared to CF/epoxy, and this is owning to its excellent interlaminar toughness. This study establishes a more in-depth understanding into the drilling performance of thermoplastic CF/PEKK and thermoset CF/epoxy and also provides guidance on the high performance manufacturing of next generation composites.

Journal: arXiv preprint arXiv:2302.12152

Abstract: Many elastic structures exhibit rapid shape transitions between two possible equilibrium states: umbrellas become inverted in strong wind and hopper popper toys jump when turned inside-out. This snap-through is a general motif for the storage and rapid release of elastic energy, and it is exploited by many biological and engineered systems from the Venus flytrap to mechanical metamaterials. Shape transitions are known to be related to the type of bifurcation the system undergoes, however, to date, there is no general understanding of the mechanisms that select these bifurcations. Here we analyze numerically and analytically two systems proposed in recent literature in which an elastic strip, initially in a buckled state, is driven through shape transitions by either rotating or translating its boundaries. We show that the two systems are mathematically equivalent, and identify three cases that illustrate the entire range of transitions described by previous authors. Importantly, using reduction order methods, we establish the nature of the underlying bifurcations and explain how these bifurcations can be predicted from geometric symmetries and symmetry-breaking mechanisms, thus providing universal design rules for elastic shape transitions.

Journal: arXiv preprint arXiv:2302.12176

Abstract: Elastic strips provide a canonical system for studying the mechanisms governing elastic shape transitions. Buckling, linear snap-through, and nonlinear snap-through have been observed in boundary-actuated strips and linked to the type of bifurcation the strip undergoes at the transition. For nonlinear snap-through, previous work obtained the normal form at the bifurcation. However, to date, there is no methodology for extending this analysis to other types of transition. Here, we study a set of three systems where a buckled elastic strip is actuated through rotation of its boundaries. Depending on the direction of rotation, the system exhibits all three types of shape transitions. We introduce a simple method to analyse the dynamic characteristics of an elastic structure near a transition. This method allows us to extend, in a straightforward manner, the asymptotic analysis proposed for nonlinear snap-through to the two other types of transition. We obtain the normal forms of these bifurcations, and show how they dictate all the dynamic characteristics of the elastic strip. This analysis provides a profound understanding of the physical mechanisms governing elastic shape transitions and reliable tools to diagnose and anticipate these transitions.

Journal: Proceedings of the National Academy of Sciences

Abstract: An effective evasion strategy allows prey to survive encounters with predators. Prey are generally thought to escape in a direction that is either random or serves to maximize the minimum distance from the predator. Here, we introduce a comprehensive approach to determine the most likely evasion strategy among multiple hypotheses and the role of biomechanical constraints on the escape response of prey fish. Through a consideration of six strategies with sensorimotor noise and previous kinematic measurements, our analysis shows that zebrafish larvae generally escape in a direction orthogonal to the predator’s heading. By sensing only the predator’s heading, this orthogonal strategy maximizes the distance from fast-moving predators, and, when operating within the biomechanical constraints of the escape response, it provides the best predictions of prey behavior among all alternatives. This work demonstrates a framework for resolving the strategic basis of evasion in predator–prey interactions, which could be applied to a broad diversity of animals.

Journal: bioRxiv

Abstract: Ciliated organs that pump luminal fluids are integral to animal physiology. Such organs, including the human airways and reproductive tracts, feature ciliated ducts that direct internal flows. Although cilia organization and duct morphology vary drastically in the animal kingdom, ducts are typically classified into two, seemingly disconnected, archetypes: the familiar carpet and the intriguing flame designs. The reason behind this dichotomy and how duct design relates to fluid pumping remain unclear. Here, we apply morphometric and mechanistic analyses to ciliated ducts across all animal phyla. We find that two structural parameters, lumen diameter and cilia-to-lumen ratio, organize the observed duct diversity into a continuous spectrum that connects carpets to flames. Using a unified fluid model, we discover that carpets and flames, respectively, maximize flow rate and pressure generation, which is consistent with physiological requirements for bulk transport and filtration, whereas intermediate designs along the morphological spectrum constitute optimally efficient hybrids. We propose that convergence of ciliated organ designs follows functional constraints rather than phylogenetic distance, and we present universal design rules for ciliary pumps.

Journal: Journal of Sound and Vibration

Abstract: An approach for vibration analysis of three-dimensional motion beam systems is formulated. The proposed method employs analytical solutions that are used to formulate a unified, three-dimensional dynamic representation the elements of beam systems, including beams, rigid bodies, and point masses. Interconnections between system’s components and their connections to the inertial frame are formulated as motion constraints. The interconnections can be rigid or flexible and can be located at arbitrary locations on the system components. The unified representations of different types of system elements and of different interconnection allows vibration analysis of complex beam systems. A procedure is developed for computation of natural frequencies and mode shapes by employing a sequence of orthogonal transformations. A three-dimensional structure that includes flexibly and rigidly connected beams and rigid bodies with different types of connections, is used to demonstrate the capabilities of the proposed method. The accuracy of the approach is verified by restricting the structure to planar motion with rigid connections and comparing the results to results of obtained in previous studies. The example structure is used to demonstrate the ability of the proposed approach to conduct vibrational analysis study of three-dimensional structures with different boundary conditions. Moreover, it also demonstrates suitability of the approach for studying sensitivity of vibrations characteristics, such as modal frequencies, with respect to system parameters, e.g. interconnections stiffness.

Journal: Mathematics of Operations Research

Abstract: We introduce a simple geometric model of opinion polarization. It is a model of political persuasion as well as marketing and advertising, utilizing social values. It focuses on the interplay between different topics and persuasion efforts. We demonstrate that societal opinion polarization often arises as an unintended by-product of influencers attempting to promote a product or idea. We discuss a number of mechanisms for the emergence of polarization involving one or more influencers, sending messages strategically, heuristically, or randomly. We also examine some computational aspects of choosing the most effective means of influencing agents and the effects of those strategic considerations on polarization.

Journal: Journal of Vibration and Acoustics

Abstract: The Inductrack system provides a novel way to achieve magnetic levitation by using Halbach arrays of permanent magnets (PMs). Due to the complexities of the nonlinear electro-magneto-mechanical coupling in the system, most previous analyses of the Inductrack system rely on steady-state results and consequently cannot fully capture the dynamic behaviors of the system in transient scenarios. In this article, a new three degrees-of-freedom (3DOF) transient model of the Inductrack system is proposed. This model describes the rigid-body motion of the Inductrack vehicle with axial (longitudinal) and vertical (transverse) displacements and pitch rotation, and it is derived without any assumption of steady-state quantities. Compared to a recently available 2DOF lumped-mass model developed by the authors, the inclusion of the pitch rotation in the new model results in a much more complicated mechanism of electro-magneto-mechanical coupling. Numerical results show that the pitch rotation can have a significant effect on the dynamic response and stability of the Inductrack system, which necessities vibration control for the safe operation of the Inductrack system.

Journal: SSRN

Abstract: This paper presents a self-supervised learning approach to learning spatially varying process parameter models for task execution by a robot. In many applications, some regions of the part enable the robot to efficiently execute the task. On the other hand, some other regions on the part may require the robot to move cautiously to ensure safety. Compared to the constant process parameter models, spatially varying process parameter models are more complex to learn. Our approach consists of utilizing an initial parameter space exploration method, surrogate modeling, selection of region sequencing policy, and development of process parameter selection policy. We show that by carefully selecting and optimizing learning components, this approach enables a robot to efficiently learn spatially varying process parameter models while satisfying task performance constraints. We demonstrated the effectiveness of our approach through computational simulations and physical experiments of a robotic sanding task. Our work shows that a learning approach that has been optimized based on task characteristics significantly outperforms an unoptimized learning approach based on task completion performance metric.

Journal: Annals of Biomedical Engineering

Abstract: Intraventricular hemorrhage is characterized by blood leaking into the cerebral ventricles and mixing with cerebrospinal fluid. A standard treatment method involves inserting a passive drainage catheter, known as an external ventricular drain (EVD), into the ventricle. EVDs have common adverse complications, including the occlusion of the catheter, that may lead to permanent neural damage or even mortality. In order to prevent such complications, a novel dual-lumen catheter (IRRAflow®) utilizing an active fluid exchange mechanism has been recently developed. However, the fluid dynamics of the exchange system have not been investigated. In this study, convective flow in a three-dimensional cerebral lateral ventricle with an inserted catheter is evaluated using an in-house lattice-Boltzmann-based fluid–solid interaction solver. Different treatment conditions are simulated, including injection temperature and patient position. Thermal and gravitational effects on medication distribution are studied using a dye simulator based on a recently-introduced (pseudo)spectral convection–diffusion equation solver. The effects of injection temperature and patient position on catheter performance are presented and discussed in terms of hematoma irrigation, vortical structures, mixing, and medication volume distribution. Results suggest that cold-temperature injections can increase catheter efficacy in terms of dye distribution and irrigation potential, both of which can be further guided by patient positioning.

Journal: Science Robotics

Abstract: Bioengineering approaches that combine living cellular components with three-dimensional scaffolds to generate motion can be used to develop a new generation of miniature robots. Integrating on-board electronics and remote control in these biological machines will enable various applications across engineering, biology, and medicine. Here, we present hybrid bioelectronic robots equipped with battery-free and microinorganic light-emitting diodes for wireless control and real-time communication. Centimeter-scale walking robots were computationally designed and optimized to host on-board optoelectronics with independent stimulation of multiple optogenetic skeletal muscles, achieving remote command of walking, turning, plowing, and transport functions both at individual and collective levels. This work paves the way toward a class of biohybrid machines able to combine biological actuation and sensing with on-board computing.

Journal: Nature Medicine

Abstract: None

Journal: Nature Communications

Abstract: Thermosets such as silicone are ubiquitous. However, existing manufacturing of thermosets involves either a prolonged manufacturing cycle (e.g., reaction injection molding), low geometric complexity (e.g., casting), or limited processable materials (e.g., frontal polymerization). Here, we report an in situ dual heating (ISDH) strategy for the rapid 3D printing of thermosets with complex structures and diverse rheological properties by incorporating direct ink writing (DIW) technique and a heating-accelerated in situ gelation mechanism. Enabled by an integrated Joule heater at the printhead, extruded thermosetting inks can quickly cure in situ, allowing for DIW of various thermosets with viscosities spanning five orders of magnitude, printed height over 100 mm, and high resolution of 50 μm. We further demonstrate DIW of a set of heterogenous thermosets using multiple functional materials and present a hybrid printing of a multilayer soft electronic circuit. Our ISDH strategy paves the way for fast manufacturing of thermosets for various emerging fields.

Journal: IEEE Transactions on Biomedical Engineering

Abstract: Objective: The clinical significance of the wave intensity (WI) analysis for the diagnosis and prognosis of the cardiovascular and cerebrovascular diseases is well-established. However, this method has not been fully translated into clinical practice. From practical point of view, the main limitation of WI method is the need for concurrent measurements of both pressure and flow waveforms. To overcome this limitation, we developed a Fourier-based machine learning (F-ML) approach to evaluate WI using only the pressure waveform measurement. Methods: Tonometry recordings of the carotid pressure and ultrasound measurements for the aortic flow waveforms from the Framingham Heart Study (2640 individuals; 55% women) were used for developing the F-ML model and the blind testing. Results: Method-derived estimates are significantly correlated for the first and second forward wave peak amplitudes (Wf1, r=0.88, p < 0.05; Wf2, r=0.84, p < 0.05) and the corresponding peak times (Wf1, r=0.80, p<0.05; Wf2, r=0.97, p < 0.05). For backward components of WI (Wb1), F-ML estimates correlated strongly for the amplitude (r=0.71, p < 0.05) and moderately for the peak time (r=0.60, p < 0.05). The results show that the pressure-only F-ML model significantly outperforms the analytical pressure-only approach based on the reservoir model. In all cases, the Bland-Altman analysis shows negligible bias in the estimations. Conclusion: The proposed pressure-only F-ML approach provides accurate estimates for WI parameters. Significance: The pressure only F-ML approach introduced in this work expand the clinical usage of WI into inexpensive and non-invasive settings such as wearable telemedicine.

Journal: bioRxiv

Abstract: Evolution of multicellularity from early unicellular ancestors is arguably one of the most important transitions since the origin of life1,2. Multicellularity is often associated with higher nutrient uptake3, better defense against predation, cell specialization and better division of labor4. While many single-celled organisms exhibit both solitary and colonial existence3,5,6, the organizing principles governing the transition and the benefits endowed are less clear. Using the suspension-feeding unicellular protist Stentor coeruleus, we show that hydrodynamic coupling between proximal neighbors results in faster feeding flows that depend on the separation between individuals. Moreover, we find that the accrued benefits in feeding current enhancement are typically asymmetric– individuals with slower solitary currents gain more from partnering than those with faster currents. We find that colony-formation is ephemeral in Stentor and individuals in colonies are highly dynamic unlike other colony-forming organisms like Volvox carteri 3. Our results demonstrate benefits endowed by the colonial organization in a simple unicellular organism and can potentially provide fundamental insights into the selective forces favoring early evolution of multicellular organization.

Journal: International Journal for Numerical Methods in Biomedical Engineering

Abstract: Path planning and collision avoidance are common problems for researchers in vehicle and robotics engineering design. In the case of autonomous ships, the navigation is guided by the regulations for preventing collisions at sea (COLREGs). However, COLREGs do not provide specific guidance for collision avoidance, especially for multi-ship encounters, which is a challenging task even for humans. In short-range path planning and collision avoidance problems, the motion of target ships is often considered as moving at a constant velocity and direction, which cannot be assumed in long-range planning and complex environments. The research challenge here is how to factor in the uncertainty of the motion of the target ships when making long-range path plans. In this paper, we introduce a long-range path planning algorithm for autonomous ships navigating in complex and dynamic environments to reduce the risk of encountering other ships during future motion. Based on the information on the position, speed over ground, and course over ground of other ships, our algorithm can estimate their intentions and future motions based on the probabilistic roadmap algorithm and use a risk-aware A* algorithm to find the optimal path that has low accumulated risk of encountering other ships. A case study is carried out on real automatic identification systems (AIS) datasets. The result shows that our algorithm can help reduce multi-ship encounters in long-term path planning.

Journal: IEEE Transactions on Automation Science and Engineering

Abstract: In a mission with significant uncertainty due to intermittent communications, delayed information flow, and robotic failures, the role of human supervisors is extremely challenging. As and when any new information arrives, humans must infer both the existing and predicted future states, identify potential contingencies, and update task assignments to robots rapidly. We propose methodologies for automated generation of task reallocation suggestions to humans to assist in the decision-making process. Our generated robot retasking plan minimizes a modified makespan of the mission, which incorporates task criticality and penalty for incomplete tasks. The plan considers the effects of potential mission contingencies on the tasks, the robots, and the future performance of the robots operating based on the previous task plans. Our method includes the incorporation of two optional tasks, i.e., relay and robot rescue, for performance improvement. The rescue task has probabilistic outcomes affecting the team size. One or more rescues are incorporated in a way that can minimize the expected value of the modified makespan overall possibilities of rescue outcomes. We have conducted performance evaluation using simulation, demonstrating the value of the optional tasks and performance enhancement using our method of incorporating them. Note to Practitioners —The work reported in this paper will be useful in applications where a team of agents is deployed to carry on a large-scale mission with communication constraints where the number of functional agents can change probabilistically. Typically, such uncertainty is encountered in applications that are challenging or dangerous in nature. Agents can have non-zero probabilities to fail while doing certain risky tasks and to get recovered by other agents. The proposed centralized multi-agent task reallocation method can help in proactively addressing potential contingencies in surveillance, search and rescue, or disaster relief to support resilient operations while having a supervisor in the higher chain of command.

Journal: Journal of Composite Materials

Abstract: Morphing aircraft offer the promise of performance that can be optimized for a range of flight profiles. But such capability places challenging demands on the morphing system that cannot easily be met with conventional actuators or smart materials. Electrolaminates use electrostatic forces controlled by the application of a voltage (typically high-voltage but extremely low-current) to controllably bond or unbond layers of a laminated composite structure together. Such on-demand bonding can be used to effectively change the stiffness of the structure or control elongation or twist of the structure. This technology has multiple benefits compared to other smart material or conventional actuator-driven approaches; it is thin and light, has very low energy requirements, and offers rapid response capabilities controlled by simple electronics. These capabilities can enable bio-inspired morphing with large numbers of degrees of freedom and high spatial resolution. We designed, fabricated, and tested a fully functional morphing-wing unmanned aerial vehicle (UAV) with telescoping wings and a splaying, bird-like tail enabled by electrolaminates. Key features of the current UAV configuration include: a variable wingspan of 1.2–2.4 m with a corresponding change in the wing area of nearly a factor of four; no vertical tail; and a horizontal tail area variable by a factor of three. The ability to asymmetrically control the tail area allows for independent pitch and body circulation control. The tail area is changed by a separate electrolaminate clamping mechanism. The variable-area tail uses ‘feathers’ that can be overlapped or splayed as needed. The practical shape-changing is enabled by electrolaminate materials that can rapidly lock the orientation of the feathers. The electrolaminate clutches support more than 5 nm of torque and are sufficient to resist expected flight loads. We also designed, fabricated, and tested other morphing-wing designs enabled by electrolaminate technology to create a wing with smoothly sliding skin that is capable of changing chord and camber with a single linear actuator. Our results suggest that electrolaminates can practically enable bio-inspired, small (Group 1 and 2) morphing UAVs.

Journal: Journal of Computing and Information Science in Engineering

Abstract: Path planning and collision avoidance are common problems for researchers in vehicle and robotics engineering design. In the case of autonomous ships, the navigation is guided by the regulations for preventing collisions at sea (COLREGs). However, COLREGs do not provide specific guidance for collision avoidance, especially for multi-ship encounters, which is a challenging task even for humans. In short-range path planning and collision avoidance problems, the motion of target ships is often considered as moving at a constant velocity and direction, which cannot be assumed in long-range planning and complex environments. The research challenge here is how to factor in the uncertainty of the motion of the target ships when making long-range path plans. In this paper, we introduce a long-range path planning algorithm for autonomous ships navigating in complex and dynamic environments to reduce the risk of encountering other ships during future motion. Based on the information on the position, speed over ground, and course over ground of other ships, our algorithm can estimate their intentions and future motions based on the probabilistic roadmap algorithm and use a risk-aware A* algorithm to find the optimal path that has low accumulated risk of encountering other ships. A case study is carried out on real automatic identification systems (AIS) datasets. The result shows that our algorithm can help reduce multi-ship encounters in long-term path planning.

Journal: IEEE Control Systems Letters

Abstract: This letter presents a novel Adaptive-frequency MPC framework for bipedal locomotion over terrain with uneven stepping stones. In detail, we intend to achieve adaptive gait periods with variable MPC frequency for bipedal periodic walking gait to traverse terrain with discontinuities without slowing down. We pair this adaptive-frequency MPC with kino-dynamics trajectory optimization to obtain MPC adaptive frequencies (in terms of sampling times), center of mass (CoM) trajectory, and foot placements. We use whole-body control (WBC) along with adaptive-frequency MPC to track the optimal trajectories from offline optimization. In numerical validations, our adaptive-frequency optimization and MPC framework have shown advantages over fixed-frequency MPC. The proposed framework can control the bipedal robot to traverse through uneven stepping stone terrains with perturbed stone heights, widths, and surface shapes while maintaining an average speed of 1.5 m/s.

Journal: Translational Vision Science & Technology

Abstract: Purpose: To study the relationship between the circumferential extent of angle closure and elevation in intraocular pressure (IOP) using a novel mechanistic model of aqueous humor (AH) flow. Methods: AH flow through conventional and unconventional outflow pathways was modeled using the unified Stokes and Darcy equations, which were solved using the finite element method. The severity and circumferential extent of angle closure were modeled by lowering the permeability of the outflow pathways. The IOP predicted by the model was compared with biometric and IOP data from the Chinese American Eye Study, wherein the circumferential extent of angle closure was determined using anterior segment OCT measurements of angle opening distance. Results: The mechanistic model predicted an initial linear rise in IOP with increasing extent of angle closure which became nonlinear when the extent of closure exceeded around one-half of the circumference. The nonlinear rise in IOP was associated with a nonlinear increase in AH outflow velocity in the open regions of the angle. These predictions were consistent with the nonlinear relationship between angle closure and IOP observed in the clinical data. Conclusions: IOP increases rapidly when the circumferential extent of angle closure exceeds 180°. Residual AH outflow may explain why not all angle closure eyes develop elevated IOP when angle closure is extensive. Translational Relevance: This study provides insight into the extent of angle closure that is clinically relevant and confers increased risk of elevated IOP. The proposed model can be utilized to study other mechanisms of impaired aqueous outflow.

Journal: arXiv preprint arXiv:2301.00942

Abstract: "These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics. The main objective of these notes is to introduce a student who is familiar with concepts in linear algebra and partial differential equations to select topics in deep learning. These lecture notes exploit the strong connections between deep learning algorithms and the more conventional techniques of computational physics to achieve two goals. First, they use concepts from computational physics to develop an understanding of deep learning algorithms. Not surprisingly, many concepts in deep learning can be connected to similar concepts in computational physics, and one can utilize this connection to better understand these algorithms. Second, several novel deep learning algorithms can be used to solve challenging problems in computational physics. Thus, they offer someone who is interested in modeling a physical phenomena with a complementary set of tools."

Journal: Composites Part A: Applied Science and Manufacturing

Abstract: Carbon-fibre-reinforced-polyetherketonketone (CF/PEKK) has attracted increasing interest in the aviation industry due to its self-healing properties and ease of recycle and repair. However, the machining performance of CF/PEKK is not well understood and there is a lack of optimization study for minimizing its hole damage and improving the production efficiency. Here, we report the first multi-objective optimization study for CF/PEKK drilling. A hybrid optimization algorithm integrating Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) is deployed to obtain the Pareto solutions and rank the multiple solutions based on closeness to ideal solutions. To highlight the impact of different matrix properties on the optimization outcome, comparative study with conventional thermoset carbon fibre reinforced epoxy composite (CF/epoxy) is carried out for the first time. Experimental validation shows the proposed method can achieve 91.5–95.7% prediction accuracy and the Pareto solutions effectively controlled the delamination and thermal damage within permissible tolerance. The vastly different optimal drilling parameters identified for CF/PEKK as compared to CF/epoxy is attributed to the thermoplastic nature of CF/PEKK and the unique thermal/mechanical interaction characteristics displayed during the machining process.

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Journal: Journal of Materials Research and Technology

Abstract: The particle/matrix interface of metal matrix composites (MMCs) can give rise to extra strength and then affect the local deformation behavior. This strengthening effect originates from the plastic strain gradients due to the incompatibility of plastic deformation between the particle and matrix. However, only limited researches utilized the strain gradient plasticity to study the damage evolution and fracture behavior, in which only one or two of the damage mechanisms (i.e., matrix damage, interface debonding, and particle fracture) was considered. In this work, all of these damage mechanisms were coupled into the finite element model under conventional theory of mechanism-based strain-gradient (CMSG) plasticity. Besides, a new numerical algorithm of geometrically necessary dislocations (GNDs) was proposed for a multi-scale model, and then this model was used to analyze the damage evolution and failure behavior of SiCp/AZ91 composite. The results show that the strengthening effects of plastic strain gradients can describe interface debonding, break the monotonicity of the effective plastic strain with the effective stress at the local area close to the particle/matrix interface, and give a more reasonable distribution of stress and plastic strain compared to the classical J2 flow theory. If the probability distribution of the interfacial strength is considered, the CMSG model has the potential capacity to capture the crack initiation in the matrix. When using the continuum damage mechanics approach to describe the fracture process based on the multi-scale model, the weakening exponent value should be considered.

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Journal: Fracture, Fatigue, Failure and Damage Evolution, Volume 3

Abstract: There is increasing interest in creating materials by design, like layered jamming materials, that offer enhanced or novel performance, such as the ability to vary properties in situ (i.e., programmable). Many of these materials rely on microstructural characteristics consisting of interfaces, material distributions, and geometric complexity that provide complex stress distributions that result in the desired properties. It has become increasingly apparent that these materials are exhibiting fatigue behavior that has not been observed in conventional materials. In this research effort, we describe efforts to characterize and model fatigue in layered jamming materials, where the elastic-plastic behavior is programmed via vacuum pressure. In order to understand the relationship between the interfacial structure and the subsequent fatigue behavior, we utilized low cycle fatigue experiments conducted on cantilever beams with layers composed of three different types of surfaces: (a) laminated paper, (b) sandpaper, and (c) multi-material polymer layers with 3D printed interfacial features. It was postulated that plastic deformation of surface asperities for laminated paper resulted in a slight increase in the load bearing capacity of the beam, as well as the stiffness. For sandpaper, particle interlocking and decohesion resulted in a substantially higher stiffness but a slightly lower load bearing capacity. Specimens consisting of multi-material polymer layers of rubber and hard plastic with spikes on the surface also exhibited reduction in load bearing capacity and higher stiffness but were dominated by the inherent time-dependent behavior of the layers. An elastic-plastic model we had previously developed for layered jamming materials was successfully applied to these materials. The experimental data and model can potentially be utilized by machine learning techniques to further elucidate on the relationship between the microstructure of the interfaces and the evolution of damage to optimize the macroscopic behavior of these materials by design.

Journal: Combustion Chemistry and the Carbon Neutral Future

Abstract: The combustion of hydrogen is a viable mobile power source with zero carbon consequences, or if the hydrogen is judiciously mixed with natural gas, with potentially carbon neutral implications. As described in this article, there are a number of technologies available to use hydrogen as a propulsion fuel. The ones specifically discussed are reciprocating internal combustion engines for land transportation and rotating detonation engines for rocket based space flight, in expectation of increasing space flight frequency. The implementation of these technologies will require further research, as discussed in this article, focusing on the characteristics of hydrogen laminar flames such as their speeds and extinction strain rates at engine relevant conditions. Implementation also requires research to obtain a definitive H2/O2 chemical kinetic mechanism. The mechanism needs to be comprehensively derived from a wide variety of experiments and theoretical calculations. It should also be universally predictive of ignition delays measured in shock tubes and rapid compression machines, and predictive of output species in jet stirred reactors, flow reactor species profiles, shock tube species, as well as laminar flame speeds and related flame characteristics over wide temperature and pressure ranges of engine relevance. On a practical level, hydrogen storage and handling for engine use is a major safety concern requiring further research, as is discussed.

Journal: Journal of Manufacturing Science and Engineering

Abstract: Inspired by porous morphology in nature, such as bone and lung tissues, synthetic porous materials are widely adopted in engineering applications that require lightweight, thermal resistance, energy absorption, and structural flexibility. One of the main challenges in the current porous material manufacturing techniques is their limited control over individual pore size, connectivity, and distribution. This paper presents a novel additive manufacturing process to fabricate porosity-embedded structures by integrating stereolithography and inkjet printing using a sacrificial liquid–water. A solenoid-based inkjet nozzle dispenses water droplets onto a layer of liquid photopolymer resin. Then the resin layer is photocured by a mask image projection device using a digital light processing device. The photocuring process defines the layer profile and captures the deposited water droplets in the solidified layer. The refilled fresh resin will further embed water droplets and form a new layer for the subsequent water droplet deposition. Three-dimensional (3D) structures with embedded water droplets can be printed layer-by-layer. The captured water will evaporate when heated, leaving an air-filled porous 3D structure. By selectively depositing water droplets and varying inkjet printing parameters, including pressure, nozzle opening time, and jetting frequency, the micropores whose sizes from 100 µm to 500 µm and distributions within the 3D-printed part can be modulated. This hybrid process can fabricate 3D structures with homogenously distributed pores and graded polymer structures with varying porosities. The elastic modulus of 3D-printed foam structures in different pore distributions has been tested and compared.

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