A Fully Self-Consistent Treatment of Collective Fluctuations in Quantum Liquids

The problem of calculating collective density fluctuations in quantum liquids is revisited. A fully quantum mechanical self-consistent treatment based on a quantum mode-coupling theory [E. Rabani and D.R. Reichman, J. Chem. Phys.116, 6271 (2002)] is presented. The theory is compared with the maximum entropy analytic continuation approach and with available experimental results. The quantum mode-coupling theory provides semi-quantitative results for both short and long time dynamics. The proper description of long time phenomena is important in future study of problems related to the physics of glassy quantum systems, and to the study of collective fluctuations in Bose fluids.Comment: 9 pages, 4 figure

LENS® and SFF: Enabling Technologies for Optimized Structures

Optimized, lightweight, high-strength structures are needed in many applications from aerospace to automotive. In pursuit of such structures, there have been proposed analytical solutions and some specialized FEA solutions for specific structures such as automobile frames. However, generalized 3D optimization methods have been unavailable for use by most designers. Moreover, in the cases where optimized structural solutions are available, they are often hollow, curving, thin wall structures that cannot be fabricated by conventional manufacturing methods. Researchers at Sandia National Laboratories and the University of Rhode Island teamed to solve these problems. The team has been pursuing two methods of optimizing models for generalized loading conditions, and also has been investigating the methods needed to fabricate these structures using Laser Engineered Net Shaping™ (LENS®) and other rapid prototyping methods. These solid freeform fabrication (SFF) methods offer the unique ability to make hollow, high aspect ratio features out of many materials. The manufacturing development required for LENS to make these complex structures has included the addition of rotational axes to Sandia’s LENS machine bringing the total to 5 controlled axes. The additional axes have required new efforts in process planning. Several of the unique structures that are only now possible through the use of SFF technology are shown as part of the discussion of this exciting new application for SFF.Mechanical Engineerin

Round-Table Group Therapy with Psychotic Patients

Although the use of group procedures for dealing with emotionally disturbed individuals goes back centuries, it is only within comparatively recent years that it has been used extensively and intensively in our mental hospitals with an awareness of group mechanisms and the forces that make up for restoring the desirable state of psychic equilibrium. As applied today group psychotherapy is initially a concession to the fact that there are too few therapists for the many patients in our mental hospitals and that, in order to reach as many patients as possible, group techniques must be applied

Multiscale coarse-graining of the protein energy landscape

Journal ArticleA variety of coarse-grained (CG) models exists for simulation of proteins. An outstanding problem is the construction of a CG model with physically accurate conformational energetics rivaling all-atom force fields. In the present work, atomistic simulations of peptide folding and aggregation equilibria are force-matched using multiscale coarse-graining to develop and test a CG interaction potential of general utility for the simulation of proteins of arbitrary sequence. The reduced representation relies on multiple interaction sites to maintain the anisotropic packing and polarity of individual sidechains. CG energy landscapes computed from replica exchange simulations of the folding of Trpzip, Trp-cage and adenylate kinase resemble those of other reduced representations; non-native structures are observed with energies similar to those of the native state. The artifactual stabilization of misfolded states implies that non-native interactions play a deciding role in deviations from ideal funnel-like cooperative folding. The role of surface tension, backbone hydrogen bonding and the smooth pairwise CG landscape is discussed. Ab initio folding aside, the improved treatment of sidechain rotamers results in stability of the native state in constant temperature simulations of Trpzip, Trp-cage, and the open to closed conformational transition of adenylate kinase, illustrating the potential value of the CG force field for simulating protein complexes and transitions between well-defined structural states

Multiscale Coarse-Graining of the Protein Energy Landscape

A variety of coarse-grained (CG) models exists for simulation of proteins. An outstanding problem is the construction of a CG model with physically accurate conformational energetics rivaling all-atom force fields. In the present work, atomistic simulations of peptide folding and aggregation equilibria are force-matched using multiscale coarse-graining to develop and test a CG interaction potential of general utility for the simulation of proteins of arbitrary sequence. The reduced representation relies on multiple interaction sites to maintain the anisotropic packing and polarity of individual sidechains. CG energy landscapes computed from replica exchange simulations of the folding of Trpzip, Trp-cage and adenylate kinase resemble those of other reduced representations; non-native structures are observed with energies similar to those of the native state. The artifactual stabilization of misfolded states implies that non-native interactions play a deciding role in deviations from ideal funnel-like cooperative folding. The role of surface tension, backbone hydrogen bonding and the smooth pairwise CG landscape is discussed. Ab initio folding aside, the improved treatment of sidechain rotamers results in stability of the native state in constant temperature simulations of Trpzip, Trp-cage, and the open to closed conformational transition of adenylate kinase, illustrating the potential value of the CG force field for simulating protein complexes and transitions between well-defined structural states.</p

NASA Micro-g NExT Challenge: Sample Container Dispensing Device

This Final Design Review (FDR) report outlines a Cal Poly San Luis Obispo senior design project developing a sample container dispensing device for NASA Johnson Space Center’s Micro-g NExT design challenge, a competition for university students. NASA aims to bring the first woman and next man to the moon through the Artemis missions beginning in 2024. The Micro-g NExT 2021 challenges focus on developing equipment which will support the Artemis mission, where Astronauts will conduct extensive geological sampling to further the scientific understanding of the moon. Our team designed, built, and tested a device that holds sample bags as they are being filled during lunar surface extravehicular activity (EVA) operations. Through participation in the design challenge, the resulting sample container dispensing device will be tested in NASA’s Neutral Buoyancy Lab, with the potential to become the baseline design for the actual mission hardware. This document begins with our Background research conducted thus far to establish the problem definition. The Objectives section discusses the scope of the project, followed by the Conceptual Design section which details the process utilized to determine the design direction. This progresses to the Final Design chapter, describing the prototype as built. Implementation and testing of the design is discussed in the Manufacturing Plan and Design Verification sections. Lastly, the Project Management section provides an overview of the project development as well as resources utilized throughout. This report is supplemented by appendices including additional visuals, matrices, analyses, and more

Local Variational Principle

A generalization of the Gibbs-Bogoliubov-Feynman inequality for spinless particles is proven and then illustrated for the simple model of a symmetric double-well quartic potential. The method gives a pointwise lower bound for the finite-temperature density matrix and it can be systematically improved by the Trotter composition rule. It is also shown to produce groundstate energies better than the ones given by the Rayleigh-Ritz principle as applied to the groundstate eigenfunctions of the reference potentials. Based on this observation, it is argued that the Local Variational Principle performs better than the equivalent methods based on the centroid path idea and on the Gibbs-Bogoliubov-Feynman variational principle, especially in the range of low temperatures.Comment: 15 pages, 5 figures, one more section adde

Coarse-Graining with Equivariant Neural Networks: A Path Towards Accurate and Data-Efficient Models

Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into these models exist, many of them involve training neural networks to act directly as the CG force field. This has several benefits, the most significant of which is accuracy. Neural networks can inherently incorporate multi-body effects during the calculation of CG forces, and a well-trained neural network force field outperforms pairwise basis sets generated from essentially any methodology. However, this comes at a significant cost. First, these models are typically slower than pairwise force fields even when accounting for specialized hardware which accelerates the training and integration of such networks. The second, and the focus of this paper, is the need for the considerable amount of data needed to train such force fields. It is common to use tens of microseconds of molecular dynamics data to train a single CG model, which approaches the point of eliminating the CG models usefulness in the first place. As we investigate in this work, it is apparent that this data-hunger trap from neural networks for predicting molecular energies and forces is caused in large part by the difficulty in learning force equivariance, i.e., the fact that force vectors should rotate while maintaining their magnitude in response to an equivalent rotation of the system. We demonstrate that for CG water, networks that inherently incorporate this equivariance into their embedding can produce functional models using datasets as small as a single frame of reference data, which networks without inherent symmetry equivariance cannot

A Demographic Approach to Race and Ethnicity in Metropolitan and Non-Metropolitan Regions of Arkansas, 1990 and 1999

This manuscript provides an empirical portrait of emergent trends in the growth, distribution, and racial and ethnic composition of Arkansas’ resident population. Particular attention is given to variation in the racial and ethnic composition of the estimated population among different regions of the state. During the 1990’s, racial and ethnic diversity increased statewide due in large part to Hispanic population growth in all regions. Black population growth was greatest in central Arkansas while Asian and Native American population growth increased most rapidly in the northwest metropolitan regions of the state. Overall, both metropolitan and non-metropolitan Arkansas communities have a more diverse mix of ethnic populations than has been known in the past