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Youthful idealism, institutional ambition, and Cold War sensibilities all helped shape the Michigan Memorial–Phoenix Project, the University of Michigan’s tribute to fallen World War II soldiers.
Subjects used a position control system to perform compensatory tracking of a repeated input pattern. The input pattern was 20 seconds in duration and was either an arctangent function or the sum of two sine waves. Tracking error decreased with practice and increased with the addition of a concurrent memory task. The shape of the ensemble-average tracking error resembled the shape of the input velocity signal throughout these changes in performance. Regression analyses were used to parameterize these effects and compare these results with the predictions of several conceptualizations of perceptual-motor learning.
Active colloidal fluids, biological and synthetic, often demonstrate complex self-organization and the emergence of collective behavior. Spontaneous formation of multiple vortices has been recently observed in a variety of active matter systems, however, the generation and tunability of the active vortices not controlled by geometrical confinement remain challenging. Here, we exploit the persistence length of individual particles in ensembles of active rollers to tune the formation of vortices and to orchestrate their characteristic sizes. We use two systems and employ two different approaches exploiting shape anisotropy or polarization memory of individual units for control of the persistence length. We characterize the dynamics of emergent multi-vortex states and reveal a direct link between the behavior of the persistence length and properties of the emergent vortices. We further demonstrate common features between the two systems including anti-ferromagnetic ordering of the neighboring vortices and active turbulent behavior with a characteristic energy cascade in the particles velocity field energy spectra. Finally, our findings provide insights into the onset of spatiotemporal coherence in active roller systems and suggest a control knob for manipulation of dynamic self-assembly in active colloidal ensembles.
We have conducted extensive lattice simulations to study the postinflation dynamics of multifield models involving nonminimal couplings. In this work, we explore the parameter dependence of preheating in these models and describe the various time scales that control such nonlinear processes as energy transfer, rescattering, and the approach to radiation domination and thermalization. In the limit of large nonminimal couplings ($ξ_I$ ~ 100), we find that efficient transfer of energy from the inflaton condensate to radiative degrees of freedom, emergence of a radiation-dominated equation of state, and the onset of thermalization each consistently occur within $N_{\text{reh}} ≲ 3 e$-folds after the end of inflation, largely independent of the values of the other couplings in the models. The exception is the case of negative ellipticity, in which there is a misalignment between the dominant direction in field space along which the system evolves and the larger of the nonminimal couplings $ξ_I$. In those cases, the field-space-driven parametric resonance is effectively shut off. More generally, the competition between the scalar fields’ potential and the field-space manifold structure can yield interesting phenomena such as two-stage resonances. Across many regions of parameter space, we find efficient re-scattering between the distinct fields, leading to a partial memory loss of the shape of the initial fluctuation spectrum. Despite the explosive particle production, which can lead to a quick depletion of the background energy density, the nonlinear processes do not induce any superhorizon correlations after the end of inflation in these models, which keeps predictions for cosmic microwave background observables unaffected by the late-time amplification of isocurvature fluctuations. Hence the excellent agreement between primordial observables and recent observations is preserved for this class of models, even when we consider postinflation dynamics.
Self-assembly, a process in which molecules, polymers, and particles are driven by local interactions to organize into patterns and functional structures, is being exploited in advancing silicon electronics and in emerging, unconventional electronics. Additionally, silicon electronics has relied on lithographic patterning of polymer resists at progressively smaller lengths to scale down device dimensions. Yet, this has become increasingly difficult and costly. Assembly of block copolymers and colloidal nanoparticles allows resolution enhancement and the definition of essential shapes to pattern circuits and memory devices. As we look to a future in which electronics are integrated at large numbers and in new forms for the Internet of Things and wearable and implantable technologies, we also explore a broader material set. Semiconductor nanoparticles and biomolecules are prized for their size-, shape-, and composition-dependent properties and for their solution-based assembly and integration into devices that are enabling unconventional manufacturing and new device functions.
Another commercial application of memory metal technology is found in a "smart" eyeglass frame that remembers its shape and its wearer's fit. A patented "memory encoding process" makes this possible. Heat is not required to return the glasses to shape. A large commercial market is anticipated.
Active colloidal fluids exhibit spontaneous emergence of correlated states, characterized by complex collective dynamics and self-organization. In geometrically confined systems, activity modulations trigger robust polar state reversals of a macroscopic vortex formed by colloidal rollers. Here, we reveal that the shape anisotropy of dielectric rollers transforms the system into a chiral active fluid comprised of rollers of arbitrary handedness. The chiral rollers self-assemble into multiple freestanding vortices with a spontaneously selected sense of rotation. We demonstrate that upon reactivation of the system after a complete cessation of activity beyond all relevant timescales, the vortices simultaneously restore their previous chiral states in striking contrast to the chiral state reversals demonstrated by spherical rollers. The analysis reveals that shape-anisotropy modifies the collective state memory of the ensemble. The findings provide insights into the emergence of complex collective behavior in chiral colloidal fluids governed by an interplay between shape anisotropy, chiral motion, and activity modulations.
Naive and memory CD4 + T cells reactive with human immunodeficiency virus type 1 (HIV-1) are detectable in unexposed, unimmunized individuals. The contribution of preexisting CD4 + T cells to a primary immune response was investigated in 20 HIV-1–seronegative volunteers vaccinated with an HIV-1 envelope (Env) plasmid DNA prime and recombinant modified vaccinia virus Ankara (MVA) boost in the HVTN 106 vaccine trial (clinicaltrials.gov NCT02296541). Prevaccination naive or memory CD4 + T cell responses directed against peptide epitopes in Env were identified in 14 individuals. After priming with DNA, 40% (8/20) of the elicited responses matched epitopes detected in the corresponding preimmunization memory repertoires, and clonotypes were shared before and after vaccination in 2 representative volunteers. In contrast, there were no shared epitope specificities between the preimmunization memory compartment and responses detected after boosting with recombinant MVA expressing a heterologous Env. Preexisting memory CD4 + T cells therefore shape the early immune response to vaccination with a previously unencountered HIV-1 antigen.
Thermoelectric elements with wire-brush contacts remove heat from or add heat to memory-metal actuators and thereby enable them to respond faster than previously possible. Memory-metal actuator formed to shape while hot, then to another shape while cold. When cold actuator heated so temperature rises above critical point, snaps from cold shape to hot shape. Upon cooling, actuator returns to cold shape, ready for another cycle. Clearly, faster actuator is heated and cooled, the faster it operates.
Studying ferroelectric hafnium oxide with focus on memory applications for the past years, discussions frequently involved the shape of measured polarization hystereses, its relation to the device performance, and how to optimize it. A perfect model-like hysteresis is of nearly rectangular shape and all deviations from this situation have to have a certain physical origin. Different phenomena and their impact on the shape of the polarization hystereses were reported in literature: Aging, imprint, fatigue, or dielectric interface layers to name a few examples. A collection of these phenomena is not easily found up to now. Thus, filling or at least reducing this gap is one of the goals of this work. Moreover, observing a pinched, slanted, or displaced hysteresis, it is quite tempting to try the reverse approach: a derivation of potential structural origins for this curve shape. First, the basics of the dynamic hysteresis measurement and the ferroelectric memories are briefly reviewed. The figures of interest are derived to ensure a proper assessment of imperfections in the hysteresis shape and their influence. It is discussed how a closer look on the polarization loop helps to draw conclusions on what might have caused such a shape or at least how to rule out some phenomena if the expected indications are not reflected in the present curve. Of course, further structural or electrical studies, as they are exemplarily pointed out, are indispensable to find the root cause(s) for the deviations from the ideal hysteresis. But sophisticated methods are not always accessible straightaway and, moreover, a pointer on where to start is always helpful. Especially, the transient currents recorded during a dynamic hysteresis measurement are stressed as a valuable instrument for this purpose. Despite their known potential, these currents are seldom shown in literature.
Combination of two dimensional graphene and semi-conducting molybdenum disulfide (MoS{sub 2}) is of great interest for various electronic device applications. Here, we demonstrate fabrication of a hybridized structure with the chemical vapor deposited graphene and MoS{sub 2} crystals to configure a memory device. Elongated hexagonal and rhombus shaped MoS{sub 2} crystals are synthesized by sulfurization of thermally evaporated molybdenum oxide (MoO{sub 3}) thin film. Scanning transmission electron microscope studies reveal atomic level structure of the synthesized high quality MoS{sub 2} crystals. In the prospect of a memory device fabrication, poly(methyl methacrylate) (PMMA) is used as an insulating dielectric material as well as a supporting layer to transfer the MoS{sub 2} crystals. In the fabricated device, PMMA-MoS{sub 2} and graphene layers act as the functional and electrode materials, respectively. Distinctive bistable electrical switching and nonvolatile rewritable memory effect is observed in the fabricated PMMA-MoS{sub 2}/graphene heterostructure. The developed material system and demonstrated memory device fabrication can be significant for next generation data storage applications.
The formerly developed preconditioned-biconjugate-gradient (PBCG) solvers for the analysis and the sensitivity equations had resulted in very large error reductions per iteration; quadratic convergence was achieved whenever the solution entered the domain of attraction to the root. Its memory requirement was also lower as compared to a direct inversion solver. However, this memory requirement was high enough to preclude the realistic, high grid-density design of a practical 3D geometry. This limitation served as the impetus to the first-year activity (March 9, 1995 to March 8, 1996). Therefore, the major activity for this period was the development of the low-memory methodology for the discrete-sensitivity-based shape optimization. This was accomplished by solving all the resulting sets of equations using an alternating-direction-implicit (ADI) approach. The results indicated that shape optimization problems which required large numbers of grid points could be resolved with a gradient-based approach. Therefore, to better utilize the computational resources, it was recommended that a number of coarse grid cases, using the PBCG method, should initially be conducted to better define the optimization problem and the design space, and obtain an improved initial shape. Subsequently, a fine grid shape optimization, which necessitates using the ADI method, should be conducted to accurately obtain the final optimized shape. The other activity during this period was the interaction with the members of the Aerodynamic and Aeroacoustic Methods Branch of Langley Research Center during one stage of their investigation to develop an adjoint-variable sensitivity method using the viscous flow equations. This method had algorithmic similarities to the variational sensitivity methods and the control-theory approach. However, unlike the prior studies, it was considered for the three-dimensional, viscous flow equations. The major accomplishment in the second period of this project (March 9, 1996 to March 8, 1997) was the extension of the shape optimization methodology for the Thin-Layer Navier-Stokes equations. Both the Euler-based and the TLNS-based analyses compared with the analyses obtained using the CFL3D code. The sensitivities, again from both levels of the flow equations, also compared very well with the finite-differenced sensitivities. A fairly large set of shape optimization cases were conducted to study a number of issues previously not well understood. The testbed for these cases was the shaping of an arrow wing in Mach 2.4 flow. All the final shapes, obtained either from a coarse-grid-based or a fine-grid-based optimization, using either a Euler-based or a TLNS-based analysis, were all re-analyzed using a fine-grid, TLNS solution for their function evaluations. This allowed for a more fair comparison of their relative merits. From the aerodynamic performance standpoint, the fine-grid TLNS-based optimization produced the best shape, and the fine-grid Euler-based optimization produced the lowest cruise efficiency.
The shape design variables have been introduced into the set of design variables of the Controls-Structure Integrated (CSI) Design of space-structures. The importance of the shape variations in improving the design (obtained with only control and sizing variables) has been aptly illustrated. Two different types of design variables that describe the shape variations of the structure have been introduced. In the first case, the nodal coordinates have been considered as design variables. This has the inherent difficulty of having too many design variables. This not only is time consuming but also memory intensive and may not yield a manufacturable shape to the structure. The second approach has been introduced to overcome this difficulty. The structure is allowed to vary in a particular pre defined pattern. The coefficients of these patterns are considered as the shape design variables. The eigenvalue and eigenvector sensitivity equations with respect to these coefficient design variables have been developed and are used to approximate the eigenvalues and eigenvectors in a perturbed design.
The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.
Low-Reynolds number testing was conducted at the 7 ft. x 10 ft. Walter H. Beech Memorial Wind Tunnel at Wichita State University to study the aerodynamic effects of ice shapes on a swept wing. A total of 17 ice shape configurations of varying geometric detail were tested. Simplified versions of an ice shape may help improve current ice accretion simulation methods and therefore aircraft design, certification, and testing. For each configuration, surface pressure, force balance, and fluorescent mini-tuft data were collected and for a selected subset of configurations oil-flow visualization and wake survey data were collected. A comparison of two ice shape geometries and two configurations with simplified geometric detail for each ice shape geometry is presented in this paper.
Significance It is well appreciated that many disordered materials deform their shape irreversibly (plastically) under an external load (e.g., memory foam). Here, we show that this plasticity can be exploited to train materials to develop novel elastic responses by straining them periodically. By applying different periodic strains to a common viscoelastic material, we are able to design a number of different responses. These include a maximally negative Poisson’s ratio, bistable behavior, and nonlocal bond-specific responses. In contrast to computer-aided design, we rely on plasticity to self-organize the system in response to local stresses. This approach shows promise to achieve an unprecedented control over behavior at large strains well beyond the linear-response regime.
G4VG reads the in-memory Geant4 geometry (the volume hierarchy and physical shape descriptions defined by a user application) and produces an equivalent VecGeom representation. This provides a direct bridge between Geant4 detector descriptions and VecGeom-based applications, eliminating the need for manual export pipelines or specialized GDML workflows. The code is adapted from the Celeritas project and developed as an independent utility for broader reuse.
With the continuing development of bigger and faster supercomputers, computational fluid dynamics (CFD) has become a useful tool for real-world engineering design and analysis. However, the number of grid points necessary to resolve realistic flow fields numerically can easily exceed the memory capacity of available computers. In addition, geometric shapes of flow fields, such as those in the Space Shuttle Main Engine (SSME) power head, may be impossible to fill with continuous grids upon which to obtain numerical solutions to the equations of fluid motion. The solution to this dilemma is simply to decompose the computational domain into subblocks of manageable size. Computer codes that are single-block by construction can be modified to handle multiple blocks, but ad-hoc changes in the FORTRAN have to be made for each geometry treated. For engineering design and analysis, what is needed is generalization so that the blocking arrangement can be specified by the user. INS3D is a computer program for the solution of steady, incompressible flow problems. It is used frequently to solve engineering problems in the CFD Branch at Marshall Space Flight Center. INS3D uses an implicit solution algorithm and the concept of artificial compressibility to provide the necessary coupling between the pressure field and the velocity field. The development of generalized multi-block capability in INS3D is described.