3,361 research outputs found
Ground states of stealthy hyperuniform potentials. II. Stacked-slider phases
Stealthy potentials, a family of long-range isotropic pair potentials,
produce infinitely degenerate disordered ground states at high densities and
crystalline ground states at low densities in d-dimensional Euclidean space
R^d. In the previous paper in this series, we numerically studied the
entropically favored ground states in the canonical ensemble in the
zero-temperature limit across the first three Euclidean space dimensions. In
this paper, we investigate using both numerical and theoretical techniques
metastable stacked-slider phases, which are part of the ground-state manifold
of stealthy potentials at densities in which crystal ground states are favored
entropically. Our numerical results enable us to devise analytical models of
this phase in two, three, and higher dimensions. Utilizing this model, we
estimated the size of the feasible region in configuration space of the
stacked-slider phase, finding it to be smaller than that of crystal structures
in the infinite-system-size limit, which is consistent with our recent previous
work. In two dimensions, we also determine exact expressions for the pair
correlation function and structure factor of the analytical model of
stacked-slider phases and analyze the connectedness of the ground-state
manifold of stealthy potentials in this density regime. We demonstrate that
stacked-slider phases are distinguishable states of matter; they are
nonperiodic, statistically anisotropic structures that possess long-range
orientational order but have zero shear modulus. We outline some possible
future avenues of research to elucidate our understanding of this unusual phase
of matter
Ground states of stealthy hyperuniform potentials: I. Entropically favored configurations
Systems of particles interacting with "stealthy" pair potentials have been
shown to possess infinitely degenerate disordered hyperuniform classical ground
states with novel physical properties. Previous attempts to sample the
infinitely degenerate ground states used energy minimization techniques,
introducing algorithmic dependence that is artificial in nature. Recently, an
ensemble theory of stealthy hyperuniform ground states was formulated to
predict the structure and thermodynamics that was shown to be in excellent
agreement with corresponding computer simulation results in the canonical
ensemble (in the zero-temperature limit). In this paper, we provide details and
justifications of the simulation procedure, which involves performing molecular
dynamics simulations at sufficiently low temperatures and minimizing the energy
of the snapshots for both the high-density disordered regime, where the theory
applies, as well as lower densities. We also use numerical simulations to
extend our study to the lower-density regime. We report results for the pair
correlation functions, structure factors, and Voronoi cell statistics. In the
high-density regime, we verify the theoretical ansatz that stealthy disordered
ground states behave like "pseudo" disordered equilibrium hard-sphere systems
in Fourier space. These results show that as the density decreases from the
high-density limit, the disordered ground states in the canonical ensemble are
characterized by an increasing degree of short-range order and eventually the
system undergoes a phase transition to crystalline ground states. We also
provide numerical evidence suggesting that different forms of stealthy pair
potentials produce the same ground-state ensemble in the zero-temperature
limit. Our techniques may be applied to sample this limit of the canonical
ensemble of other potentials with highly degenerate ground states
Do methanethiol adsorbates on the Au(111) surface dissociate?
The interaction of methanethiol molecules CHSH with the Au(111) surface
is investigated, and it is found for the first time that the S-H bond remains
intact when the methanethiol molecules are adsorbed on the regular Au(111)
surface. However, it breaks if defects are present in the Au(111) surface. At
low coverage, the fcc region is favored for S atom adsorption, but at saturated
coverage the adsorption energies at various sites are almost iso-energetic. The
presented calculations show that a methanethiol layer on the regular Au(111)
surface does not dimerize.Comment: 4 pages, 2 figures, 4 tables, submitted to Phys. Rev. Let
An enhanced TCP congestion avoidance scheme and its performance evaluation in high speed satellite networks
High speed satellite communication networks are
emerging as part of the future global wireless communication
systems. However, existing transmission control protocols for
satellite networks do not provide satisfactory performance over
high speed satellite links due to their inefficient congestion
avoidance algorithms. This paper identifies the reason for low
throughput of a widely used protocol Space Communications
Protocol Specification (SCPS) in such networks and proposes a
new Transmission Control Protocol (TCP) congestion avoidance
algorithm to overcome the drawback of the congestion avoidance
algorithm used in the SCPS protocol. Numerical results through
simulations demonstrate that the proposed new algorithm can
achieve significant throughput improvement over links with
variable error rates, compared with its legacy counterpart
Variance Reduction of Sequential Monte Carlo Approach for GNSS Phase Bias Estimation
Global navigation satellite systems (GNSS) are an important tool for positioning, navigation, and timing (PNT) services. The fast and high-precision GNSS data processing relies on reliable integer ambiguity fixing, whose performance depends on phase bias estimation. However, the mathematic model of GNSS phase bias estimation encounters the rank-deficiency problem, making bias estimation a difficult task. Combining the Monte-Carlo-based methods and GNSS data processing procedure can overcome the problem and provide fast-converging bias estimates. The variance reduction of the estimation algorithm has the potential to improve the accuracy of the estimates and is meaningful for precise and efficient PNT services. In this paper, firstly, we present the difficulty in phase bias estimation and introduce the sequential quasi-Monte Carlo (SQMC) method, then develop the SQMC-based GNSS phase bias estimation algorithm, and investigate the effects of the low-discrepancy sequence on variance reduction. Experiments with practical data show that the low-discrepancy sequence in the algorithm can significantly reduce the standard deviation of the estimates and shorten the convergence time of the filtering
Coverage dependence of the 1-propanol adsorption on the Si(001) surface and fragmentation dynamics
The geometric, electronic, energetic, and dynamic properties of 1-propanol
adsorbed on the Si(001)-2x1 surface are studied from first principles by use of
a slab approach. The 1-propanol molecule initially interacts with the Si
surface through formation of a dative bond, subsequently the physisorbed
1-propanol molecule reacts with the surface by cleavage of the O-H bond, and
the Si(001)-2x1 surface undergoes further reconstruction as a result of the
adsorption of the organic species. The band structure and density of states
(DOS) are first analyzed for this system. The band gap of the Si/1-propanol
film increases as the coverage level is enhanced. Good agreement is found with
available experimental data.Comment: 29 pages, 15 figures, 8 tables, submitted to Phys. Rev.
AICropCAM: Deploying classification, segmentation, detection, and counting deep-learning models for crop monitoring on the edge
Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with \u3e43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations
Physical Constraints and Functional Characteristics of Transcription Factor-DNA Interaction
We study theoretical ``design principles'' for transcription factor-DNA
interaction in bacteria, focusing particularly on the statistical interaction
of the transcription factors (TF's) with the genomic background (i.e., the
genome without the target sites). We introduce and motivate the concept of
`programmability', i.e. the ability to set the threshold concentration for TF
binding over a wide range merely by mutating the binding sequence of a target
site. This functional demand, together with physical constraints arising from
the thermodynamics and kinetics of TF-DNA interaction, leads us to a narrow
range of ``optimal'' interaction parameters. We find that this parameter set
agrees well with experimental data for the interaction parameters of a few
exemplary prokaryotic TF's. This indicates that TF-DNA interaction is indeed
programmable. We suggest further experiments to test whether this is a general
feature for a large class of TF's.Comment: 9 pages, 4 figures; revised version as published in PNA
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