Hard Boundary Research Articles

On the Hard Boundary Constraint Method for Fluid Flow Prediction based on the Physics-Informed Neural Network

With the rapid development of artificial intelligence technology, the physics-informed neural network (PINN) has gradually emerged as an effective and potential method for solving N-S equations. The treatment of constraints is vital to the PINN prediction accuracy. Compared to soft constraints, hard constraints are advantageous for the avoidance of difficulties in guaranteeing definite conditions and determining penalty coefficients. However, the principles on the formulation of hard constraints of PINN currently remain to be formed, which hinders the application of PINN in engineering fields. In this study, hard-constraint-based PINN models are constructed for Couette flow, plate shear flow and stenotic/aneurysmal flow with curved geometries. Particular efforts have been devoted to assessing the impact of the model parameters of hard constraints, i.e., degree and scaling factor, on the prediction accuracy of PINN at different Reynolds numbers. The results show that the degree is the most important factor that influences the prediction accuracy, followed by the scaling factor. As for the N-S equations, the degree of hard constraints should be at least two, while the scaling factor is recommended to be maintained around 1.0. The outcomes of the present work are of reference value for the development of PINN methods in fluid mechanics.

Intelligent X-ray waste detection and classification via X-ray characteristic enhancement and deep learning

Waste management plays an important role in environmental protection and resource recycling. In fact, traditional waste recycling requires a large number of workers to sort manually, which is not only labor-intensive and time-consuming, but also increases the health risk for workers. To further improve the efficiency of waste detection and classification, related scholars have used computer vision technology to achieve automatic waste management. However, existing methods tend to use RGB images as input, requiring workers to open waste bags and disperse waste items in advance. To avoid comprehensive preparation procedures and identify occluded waste items, we use deep learning models to detect and recognize waste items in X-ray images. Compared to RGB images, X-ray images can effectively preserve boundary information and reflect material types. To take advantage of the inherent characteristics of X-ray images, we propose a novel X-ray Characteristic Enhancement Module (XCEM) to improve the performance of X-ray waste detection. XCEM consists of three sub-modules, namely Hybrid Interaction Module (HIM), Multi-scale Material Perception Module (MMPM), and Boundary Guidance Module (BGM). Specifically, HIM not only generates soft and hard boundary maps, but also achieves information interaction and feature enhancement of different boundary maps. Meanwhile, MMPM can effectively capture different multi-scale material features. In addition, BGM uses auxiliary boundary information and attention mechanisms to effectively enhance critical material features. As a plug-and-play module, XCEM can be readily inserted into the most popular detection pipelines. We select several representative one-stage and two-stage methods and perform related experiments on the WIXRay dataset. Experimental results show that our method can achieve consistent performance gains on diverse detection paradigms. This study promotes the development of intelligent waste inspection and recycling.

Effect of micro-slit entries on the sound absorption and size of a labyrinthine acoustic metamaterial

Low frequency noise attenuation is a pressing issue in the field of acoustics and noise control. In this regard, labyrinthine acoustic metamaterials have shown high potential in achieving low frequency sound absorption. This paper presents two new acoustic metamaterial designs consisting of a labyrinthine of sub-wavelength straight folded air channels with a rectangular cross-section surrounded by sound hard boundaries, combined with a micro-perforated slit panel. Design 1 has two micro-slits at the end channels, whose lengths are the same as the width of labyrinthine channels. Design 2 has two micro-slits of unequal length at the end channels. A simulation study using COMSOL Multiphysics is conducted to check the effect of the slit lengths on the sound absorption of these metamaterials. It is found that having micro-slits with unequal lengths for sound wave entry leads to a higher absorption peak and a lower peak absorption frequency compared to that of micro-slits with equal lengths. Moreover, it leads to a better sound absorption performance with increasing thickness. The experimental results of the sound absorption coefficient are in good agreement with the simulation results. Thus, Design 2 can be used for extremely low frequency sound absorptions in machinery with space constraints.

Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains

AbstractPhysics‐informed neural networks (PINNs) are a new tool for solving boundary value problems by defining loss functions of neural networks based on governing equations, boundary conditions, and initial conditions. Recent investigations have shown that when designing loss functions for many engineering problems, using first‐order derivatives and combining equations from both strong and weak forms can lead to much better accuracy, especially when there are heterogeneity and variable jumps in the domain. This new approach is called the mixed formulation for PINNs, which takes ideas from the mixed finite element method. In this method, the PDE is reformulated as a system of equations where the primary unknowns are the fluxes or gradients of the solution, and the secondary unknowns are the solution itself. In this work, we propose applying the mixed formulation to solve multi‐physical problems, specifically a stationary thermo‐mechanically coupled system of equations. Additionally, we discuss both sequential and fully coupled unsupervised training and compare their accuracy and computational cost. To improve the accuracy of the network, we incorporate hard boundary constraints to ensure valid predictions. We then investigate how different optimizers and architectures affect accuracy and efficiency. Finally, we introduce a simple approach for parametric learning that is similar to transfer learning. This approach combines data and physics to address the limitations of PINNs regarding computational cost and improves the network's ability to predict the response of the system for unseen cases. The outcomes of this work will be useful for many other engineering applications where deep learning is employed on multiple coupled systems of equations for fast and reliable computations.

Robust Estimation of Position-Dependent Anisotropic Diffusivity Tensors from Molecular Dynamics Trajectories.

Confinement breaks translational and rotational symmetry in materials and makes all physical properties functions of position. Such spatial variations are key to modulating material properties at the nanoscale, and characterizing them accurately is therefore an intense area of research in the molecular simulations community. This is relatively easy to accomplish for basic mechanical observables. Determining spatial profiles of transport properties, such as diffusivity, is, however, much more challenging, as it requires calculating position-dependent autocorrelations of mechanical observables. In our previous paper (Domingues, T.S.; Coifman, R.; Haji-Akbari, A. J. Phys. Chem. B 2023, 127, 5273 10.1021/acs.jpcb.3c00670), we analytically derive and numerically validate a set of filtered covariance estimators (FCEs) for quantifying spatial variations of the diffusivity tensor from stochastic trajectories. In this work, we adapt these estimators to extract diffusivity profiles from MD trajectories and validate them by applying them to a Lennard-Jones fluid within a slit pore. We find our MD-adapted estimator to exhibit the same qualitative features as its stochastic counterpart, as it accurately estimates the lateral diffusivity across the pore while systematically underestimating the normal diffusivity close to hard boundaries. We introduce a conceptually simple and numerically efficient correction scheme based on simulated annealing and diffusion maps to resolve the latter artifact and obtain normal diffusivity profiles that are consistent with the self-part of the van Hove correlation functions. Our findings demonstrate the potential of this MD-adapted estimator in accurately characterizing spatial variations of diffusivity in confined materials.

A deep learning framework for solving forward and inverse problems of power-law fluids

We for the first time leverage deep learning approaches to solve forward and inverse problems of two-dimensional laminar flows for power-law fluids. We propose a deep-learning framework, called Power-Law-Fluid-Net (PL-Net). We develop a surrogate model to solve the forward problems of the power-law fluids, and solve the inverse problems utilizing only a small set of measurement data under the assumption that boundary conditions (BCs) can be partially known. In the design of the methods, we incorporate the hard boundary condition constraints to accelerate the iteration of stochastic gradient descent methods for minimizing loss functions. For the forward problems, by incorporating the constitutive parameters into the input variables of neural networks, the PL-Net serves as a surrogate model for simulating the pressure-driven flows inside pipes having cross sections of varying shapes. We investigate the influences of the BC type, activation function type, and number of collocation points on the accuracy of numerical solutions. For the inverse problems, the PL-Net infers the physical quantities or constitutive parameters from a small number of measurements of flow field variables. The BCs of the inverse problems can even be partially known. We demonstrate the effects of BC type, number of sensors, and noise level on accuracy of inferred quantities. Computational examples indicate the high accuracy of the PL-Net in tackling both the forward and inverse problems of the power-law fluids.

Improved inference for a boundary parameter

AbstractThe limiting distributions of statistics used to test hypotheses about parameters on the boundary of their domains may provide very poor approximations to the finite‐sample behaviour of these statistics, even for very large samples. We review theoretical work on this problem, describe hard and soft boundaries and iceberg estimators, and give examples highlighting how the limiting results greatly underestimate the probability that the parameter lies on its boundary even in very large samples. We propose and evaluate some simple remedies for this difficulty based on normal approximation for the profile score function, and then outline how higher order approximations yield excellent results in a range of hard and soft boundary examples. We use the approach to develop an accurate test for the need for a spline component in a linear mixed model.

Don't forget the boundary problem! How EM field topology can address the overlooked cousin to the binding problem for consciousness.

The boundary problem is related to the binding problem, part of a family of puzzles and phenomenal experiences that theories of consciousness (ToC) must either explain or eliminate. By comparison with the phenomenal binding problem, the boundary problem has received very little scholarly attention since first framed in detail by Rosengard in 1998, despite discussion by Chalmers in his widely cited 2016 work on the combination problem. However, any ToC that addresses the binding problem must also address the boundary problem. The binding problem asks how a unified first person perspective (1PP) can bind experiences across multiple physically distinct activities, whether billions of individual neurons firing or some other underlying phenomenon. To a first approximation, the boundary problem asks why we experience hard boundaries around those unified 1PPs and why the boundaries operate at their apparent spatiotemporal scale. We review recent discussion of the boundary problem, identifying several promising avenues but none that yet address all aspects of the problem. We set out five specific boundary problems to aid precision in future efforts. We also examine electromagnetic (EM) field theories in detail, given their previous success with the binding problem, and introduce a feature with the necessary characteristics to address the boundary problem at a conceptual level. Topological segmentation can, in principle, create exactly the hard boundaries desired, enclosing holistic, frame-invariant units capable of effecting downward causality. The conclusion outlines a programme for testing this concept, describing how it might also differentiate between competing EM ToCs.

Physics-informed neural networks for mesh deformation with exact boundary enforcement

In this work, we have applied physics-informed neural networks (PINN) for solving mesh deformation problems. We used the collocation PINN method to capture the new positions of the vertex nodes while preserving the connectivity information. We use linear elasticity equations for mesh deformation. To prevent vertex collisions or edge overlap, the mesh movement in this work is conducted in steps with relatively small movements. For moving boundary problems, the exact position of the boundary is essential for having an accurate solution. However, PINNs are frequently unable to satisfy Dirichlet boundary conditions exactly. To overcome this issue, we have used hard boundary condition enforcement to automatically satisfy Dirichlet boundary conditions. Specifically, we first trained a PINN with soft boundary conditions to obtain a particular solution. Then, this solution was tuned with exact boundary positions and a proper distance function by using a new PINN considering only the equation residual. To assess the accuracy of our approach, we used the classical translation and rotation tests and compared them with a proper mesh quality metric considering the change in the element area and shape. The results show the accuracy of this approach is comparable with that of finite element solutions. We also solved different moving boundary problems, resembling commonly used fluid–structure interaction problems. This work provides insight into using PINN for mesh-deformation problems without needing a discretization scheme with reasonable accuracy.

Stiffness of Confinement Controls the Localization of an Object under Crowding: Macroscale Real-World and Microscale Numerical Modelings on the Specificity of Intracellular Positioning

Macroscopic systems that mimic microscopic systems can provide fundamental understanding of how macromolecules (e.g., cellular organelles) are organized in a confined space. We studied the behavior of a large particle interacting with several small particles confined in dishes with hard/soft boundary conditions under mechanical vibration using a cm-scale model. We also performed a numerical simulation for the micro-scale system under Brownian fluctuation with fluctuation–dissipation, as a simple model of living cellular cytoplastic crowding. Under a hard boundary condition, the large sphere preferred the boundary at low crowding but tended to localize to the interior under high crowding. Conversely, when the boundary was soft, the large sphere localized to the interior under low crowding and tended to migrate near the boundary when crowding increased. Interestingly, numerical modeling reproduced similar results for both the experimental hard and soft boundary conditions. Our models revealed that membrane stiffness can affect the organization of biopolymers within a confined space and may help explain cellular dynamics.

Robust Estimation of Position-Dependent Anisotropic Diffusivity Tensors from Stochastic Trajectories.

Materials under confinement can possess properties that deviate considerably from their bulk counterparts. Indeed, confinement makes all physical properties position-dependent and possibly anisotropic, and characterizing such spatial variations and directionality has been an intense area of focus in experimental and computational studies of confined matter. While this task is fairly straightforward for simple mechanical observables, it is far more daunting for transport properties such as diffusivity that can only be estimated from autocorrelations of mechanical observables. For instance, there are well established methods for estimating diffusivity from experimentally observed or computationally generated trajectories in bulk systems. No rigorous generalizations of such methods, however, exist for confined systems. In this work, we present two filtered covariance estimators for computing anisotropic and position-dependent diffusivity tensors and validate them by applying them to stochastic trajectories generated according to known diffusivity profiles. These estimators can accurately capture spatial variations that span over several orders of magnitude and that assume different functional forms. Our kernel-based approach is also very robust to implementation details such as the localization function and time discretization and performs significantly better than estimators that are solely based on local covariance. Moreover, the kernel function does not have to be localized and can instead belong to a dictionary of orthogonal functions. Therefore, the proposed estimator can be readily used to obtain functional estimates of diffusivity rather than a tabulated collection of pointwise estimates. Nonetheless, the susceptibility of the proposed estimators to time discretization is higher at the immediate vicinity of hard boundaries. We demonstrate this heightened susceptibility to be common among all covariance-based estimators.

Study on boundary reflection mechanism of continuous wave mud pulse in the flow loop

Continuous wave (CW) mud pulse is one of the essential ways of high speed data transmission while drilling. The boundary reflection process and mechanism need to be clarified, which seriously affects the analog interpretation of pressure waves in flow loops and drilling conditions. In this paper, the boundary reflection of mass inlet and pressure outlet is studied by using traditional acoustic theory and numerical simulation methods, and mutual verification is carried out with flow loop. The results show that the pressure wave generated by the CW generator can be divided into compression wave and expansion wave and transmitted to the upstream and downstream pipelines. The inlet boundary reflection is the hard boundary reflection in acoustics. The phase of reflected wave is the same as that of the incident wave. The outlet boundary is the soft boundary, the reflected wave phase is 180° opposite to the incident wave. In particular, the process and mechanism of secondary reflection of stator and rotor structure on pressure wave are analyzed for the first time, which provides a theoretical basis for pressure wave amplitude. Finally, due to the possibility of cavitation phenomenon in the flow loop, it is different from the pressure environment of the bottom under drilling conditions and should to study further.

Archaeology, Nation, and Race: Confronting the Past, Decolonizing the Future in Greece and Israel by Raphael Greenberg and Yannis Hamilakis

Reviewed by: Archaeology, Nation, and Race: Confronting the Past, Decolonizing the Future in Greece and Israel by Raphael Greenberg and Yannis Hamilakis Dimitri Nakassis (bio) Raphael Greenberg and Yannis Hamilakis, Archaeology, Nation, and Race: Confronting the Past, Decolonizing the Future in Greece and Israel. Cambridge, UK: Cambridge University Press, 2022. Pp. xvi + 218. 25 figures. Paperback $25.99. In a seminal article in 1973, David Clarke declared archaeology’s “loss of innocence,” which he understood as the ongoing process of critical disciplinary definition. Clarke’s paper sketched the radical changes, especially the renewed interest in theory, that then roiled the discipline. Fifty years later, Raphael Greenberg and Yannis Hamilakis present a discussion of a new loss of innocence (161) and a new horizon in archaeology’s development, one that promises to be fertile ground for future exploration. Their aim is to expose archaeology’s “deep roots in colonialism and racism” and “to rebuild archaeology on entirely new foundations” (3). Their method for doing so is not a conventional academic monograph, but rather a series of structured conversations with themselves as interlocutors. Individual chapters focus on archaeology’s colonial origins, its practice in crypto-colonial contexts, the notion of purification, the role of nationalism and race in the development of the discipline, and decolonization. These themes are explored through engagement with the archaeology and politics of the modern nations of Israel and Greece. Clarke (1973, 8) observed that “each archaeology is of its time,” and this book is especially and avowedly so. The influence of the Black Lives Matter movement in particular, but also that of the pandemic, are tangibly felt and indeed highlighted by the authors (3). The book owes its genesis to a seminar taught by the authors at Brown University in the spring of 2020, followed by conversations via video conferencing during the lockdown. It is perhaps unsurprising, therefore, that the main novelty of this book, at least for this reviewer, is its emphasis on race in Mediterranean archaeology. In a Greek context, of course, the issue of race has become especially pressing with the rise of racialized politics and violence against migrants from Africa and Asia, prompting a growing interest in race among Greek scholars (e.g., Avdela et al. 2017). What emerges clearly from that research and the present book is how powerful a lens race is for the study of archaeology’s development: it is critical for analysis not only of the present state of the discipline but also of its history, and it is a sufficiently productive frame that it must be considered on a par with the concept of the nation, to which it has long been subordinated (Hamilakis 2007, 2020). Greenberg and Hamilakis range widely over a broad intellectual terrain. A brief introduction to the authors’ intellectual histories and orientations (chapter 1) sets the stage for an engaging discussion in chapter 2 about the origins and historical trajectories of Greek and Israeli archaeologies. [End Page 149] Crypto-colonialism—that fertile concept first articulated by Michael Herzfeld (2002)—is the subject of chapter 3, which provides a productive lens for comparison of the Greek and Israeli cases and their “superimposed colonialisms” (58). Chapter 4 focuses on archaeology as purification, from the practice of removing material traces and communities seen as out of place to the archaeological desire for purity of categories (e.g., the hard boundaries between chronological periods). Race is the analytical focus of chapter 5, which concludes with a detailed discussion of archaeogenetic studies and the ways that they re-inscribe racial categories. The penultimate chapter (chapter 6) meditates on decolonization, understood not simply as an academic endeavor but as a broad political project. A short conclusion (chapter 7) summarizes the aims of the volume. This is an important book that should have a significant impact on archaeology, for it sets an ambitious agenda for the future of the discipline. Greenberg and Hamilakis’s goal is nothing less than an emancipatory archaeology with the potential to liberate us all from the coloniality that defines our relationship with the past and present (171). Greece and Israel are potent sites for such an undertaking, both because of the dense entanglement of the ancient...

Sources for Christian Bioethics: The Orthodox Discourse on Sin

AbstractThe article discusses ways of developing bioethical guidance in the Orthodox Christian discourse. Here, “ethical” refers to what contributes to holiness, “un-ethical” refers to sin as what hinders man’s foundational calling to holiness. To explore the development of guidance for emerging bioethical issues, we use the “therapeutic” understanding of treatment for sin in two senses. (1) It refers to the spiritual means provided by the “hospital” of the Orthodox Church for healing the fallenness of human nature in general; and (2) it helps identify in particular cases both what counts as transgression of unconditional boundaries (defining what is illicit for everyone) and what is advisable in order to help particular persons to choose rightly within the area of the “more or less licit” (or “permissible”). Sources of the Orthodox faith that frame the general understanding of the boundaries between the permissible and the impermissible reflect the work of the Holy Spirit in the Church framing her Sacred (Holy) Tradition. In Holy Tradition, prayers, as communion with God, occupy a central position. This is why Orthodox moral reasoning, and thus also Orthodox bioethics, takes the form of liturgical bioethics. Penalties of excommunication determine hard boundaries that believers should not overstep: when crossing such boundaries, they enter on a spiritually dangerous path that completely distracts them away from God. Many minor sins, by contrast, have not been codified in Canons. They are not taken to remove the sinner completely from contact with the Lord. Within this realm of actions, it depends on a person’s spiritual maturity whether such actions are classified as sinful. Thus, an act can be counted merely as a small offense if committed by a beginner, while that same act can become a grave sin for an advanced believer. Due to acceleration of the technological progress, Orthodox recourse to Holy Tradition encounters ever-new challenges. There are bioethical issues that had not surfaced at the time of the Fathers of the Church. Today, Bishops gathered at a local council can supply the guidance lacking on a newly emerging bioethical problem. Even if ratified only by such local councils, the resulting decrees can also be regarded as the expression of the Holy Spirit working within a local Church and conveying His guidance. The article illustrates this source of Christian bioethics by reference to how the Russian Orthodox Church orients and develops its position on the ethics of reproductive technologies.

There's Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines.

The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as "polycomputing"-the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.

Pattern formation via cell–cell adhesion and contact inhibition of locomotion in active matter

Cell wetting and dewetting in soft substrates present a collection of non-cohesive and cohesive patterns. Prediction of this wide diversity is of critical importance in order to design experiments with polar active matter under confinement. Although in vivo, cells and the extracellular matrix (ECM) are enfolded by flexible substrates, at experimental realizations, hard boundaries are frequently employed. Here, the elastic forces exerted by the cells and the ECM—between a deformable layer and a solid substrate—allow to recast a continuum model that takes account of heterogeneous exchanges such as cell–substrate adhesion and averaged repolarization due to contact inhibition of locomotion (CIL). Theoretical results show that cell aggregation is enforced as increasing cell–cell adhesion and decreasing CIL strength and exhibit different phases from gaseous states to polar liquids and 3D clusters, in agreement with recent reports. Cell diffusion grows as cell rigidity increases, and reduction of ECM stiffness eases cell aggregation and cluster formation. The findings of this work provide the mechanisms that drive and resist active unstable states and can be used as a predictability tool in cell clustering and cell migration experiments.

Fencing Can Alter Gene Flow of Asian Elephant Populations within Protected Areas

The Asian elephant is mostly confined to mountainous ranges and therefore risks population fragmentation if hard protected area (PA) boundaries near steep slopes prevent movement. We tested whether elephant gene flow is (i) controlled by slope and (ii) affected by the interplay between barriers and slope. We used 176 unique genotypes obtained non-invasively from fresh elephant dung to assess individual-by-individual genetic distance across the Western Ghats of India, a biodiversity hotspot. To assess landscape distance, 36 resistance models were produced by transforming a slope raster. Core areas and corridors were calculated from the raster that provided the best correlation between the genetic and distance matrices. The influence of the closure of PAs on gene flow was examined for one region, the Nilgiri Biosphere Reserve. The best resistance raster obtained by transforming the slope occupancy model was better than Euclidean distance for explaining genetic distance, indicating that slope partially controls gene flow. Fencing elephant PAs on hilly terrain reduces core areas and disrupts corridors. Consequently, hard PA boundaries abutting slopes can fragment elephant populations, but this can be ameliorated by protecting the adjacent flatter terrain.

Ship of Darkness: Jazz and/as Affect in the Global Cruise Industry

Ship of Darkness: Jazz and/as Affect in the Global Cruise Industry

Variational and phase response analysis for limit cycles with hard boundaries, with applications to neuromechanical control problems

Motor systems show an overall robustness, but because they are highly nonlinear, understanding how they achieve robustness is difficult. In many rhythmic systems, robustness against perturbations involves response of both the shape and the timing of the trajectory. This makes the study of robustness even more challenging. To understand how a motor system produces robust behaviors in a variable environment, we consider a neuromechanical model of motor patterns in the feeding apparatus of the marine mollusk Aplysia californica (Shaw et al. in J Comput Neurosci 38(1):25–51, 2015; Lyttle et al. in Biol Cybern 111(1):25–47, 2017). We established in (Wang et al. in SIAM J Appl Dyn Syst 20(2):701–744, 2021. https://doi.org/10.1137/20M1344974) the tools for studying combined shape and timing responses of limit cycle systems under sustained perturbations and here apply them to study robustness of the neuromechanical model against increased mechanical load during swallowing. Interestingly, we discover that nonlinear biomechanical properties confer resilience by immediately increasing resistance to applied loads. In contrast, the effect of changed sensory feedback signal is significantly delayed by the firing rates’ hard boundary properties. Our analysis suggests that sensory feedback contributes to robustness in swallowing primarily by shifting the timing of neural activation involved in the power stroke of the motor cycle (retraction). This effect enables the system to generate stronger retractor muscle forces to compensate for the increased load, and hence achieve strong robustness. The approaches that we are applying to understanding a neuromechanical model in Aplysia, and the results that we have obtained, are likely to provide insights into the function of other motor systems that encounter changing mechanical loads and hard boundaries, both due to mechanical and neuronal firing properties.

Variation in adhesion properties and film morphologies of waterborne pressure‐sensitive adhesives containing an acid‐rich diblock copolymer additive

AbstractOne challenge for waterborne pressure‐sensitive adhesives (PSA)s is the typical tradeoff between cohesion and adhesion. Others have shown that waterborne PSA films with thin, hard boundaries formed from short poly(acid) segments surrounding soft particles can have simultaneous improvements in adhesion and cohesion. To study this approach in commercial‐type acrylic waterborne PSAs, a diblock copolymer (BCP) composed of poly(acrylic acid)‐block‐(n‐butyl acrylate) (PAA‐b‐PBA) is added at 1–5 wt% to PSAs composed of 100% PBA (PSA (0% acid)) or 99% BA/1% AA (PSA (1% acid)). Films of these blends prepared using industrially relevant conditions reveal complex nanodomains in atomic force microscopy (AFM) images. PSA performance tests showed that 1–2 wt% BCP additive nearly tripled adhesion to stainless steel for PSA (0% acid) while improving cohesion. Nonetheless, AFM images of PSA (0% acid) with 1–2 wt% BCP display ill‐defined morphologies, with clear percolated structures apparent primarily in films with 5% BCP. This indicates that film morphologies which enhance adhesion may not correlate with AFM features. Moreover, the balance of adhesion and cohesion of PSA (1% acid) without BCP exceeds that of any of the BCP blends, demonstrating the challenge of using designed microstructures to improve upon conventional PSAs containing copolymerized acids.