8,893 research outputs found
Series of Concentration-Induced Phase Transitions in Cholesterol/Phosphatidylcholine Mixtures
In lipid membranes, temperature-induced transition from gel-to-fluid phase increases the lateral diffusion of the lipid molecules by three orders of magnitude. In cell membranes, a similar phase change may trigger the communication between the membrane components. Here concentration-induced phase transition properties of our recently developed statistical mechanical model of cholesterol/phospholipid mixtures are investigated. A slight (<1%) decrease in the model parameter values, controlling the lateral interaction energies, reveals the existence of a series of first- or second-order phase transitions. By weakening the lateral interactions first, the proportion of the ordered (i.e., superlattice) phase (Areg) is slightly and continuously decreasing at every cholesterol mole fraction. Then sudden decreases in Areg appear at the 0.18–0.26 range of cholesterol mole fractions. We point out that the sudden changes in Areg represent first- or second-order concentration-induced phase transitions from fluid to superlattice and from superlattice to fluid phase. Sudden changes like these were detected in our previous experiments at 0.2, 0.222, and 0.25 sterol mole fractions in ergosterol/DMPC mixtures. By further decreasing the lateral interactions, the fluid phase will dominate throughout the 0.18–0.26 interval, whereas outside this interval sudden increases in Areg may appear. Lipid composition-induced phase transitions as specified here should have far more important biological implications than temperature- or pressure-induced phase transitions. This is the case because temperature and pressure in cell membranes are largely invariant under physiological conditions
Longitudinal spin excitations and magnetic anisotropy in antiferromagnetically ordered BaFe2As2
We report on a spin-polarized inelastic neutron scattering study of spin
waves in the antiferromagnetically ordered state of BaFe2As2. Three distinct
excitation components are identified, with spins fluctuating along the c-axis,
perpendicular to the ordering direction in the ab-plane, and parallel to the
ordering direction. While the first two "transverse" components can be
described by a linear spin-wave theory with magnetic anisotropy and inter-layer
coupling, the third "longitudinal" component is generically incompatible with
the local moment picture. It points towards a contribution of itinerant
electrons to the magnetism already in the parent compound of this family of
Fe-based superconductors.Comment: 4 pages, 4 figures, plus Supplemental Materia
transition form factor within Light Front Quark Model
We study the transition form factor of as a
function of the momentum transfer within the light-front quark model
(LFQM). We compare our result with the experimental data by BaBar as well as
other calculations based on the LFQM in the literature. We show that our
predicted form factor fits well with the experimental data, particularly those
at the large region.Comment: 11 pages, 4 figures, accepted for publication in PR
Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning
Single-pixel imaging (SPI) is a novel imaging technique whose working
principle is based on the compressive sensing (CS) theory. In SPI, data is
obtained through a series of compressive measurements and the corresponding
image is reconstructed. Typically, the reconstruction algorithm such as basis
pursuit relies on the sparsity assumption in images. However, recent advances
in deep learning have found its uses in reconstructing CS images. Despite
showing a promising result in simulations, it is often unclear how such an
algorithm can be implemented in an actual SPI setup. In this paper, we
demonstrate the use of deep learning on the reconstruction of SPI images in
conjunction with block compressive sensing (BCS). We also proposed a novel
reconstruction model based on convolutional neural networks that outperforms
other competitive CS reconstruction algorithms. Besides, by incorporating BCS
in our deep learning model, we were able to reconstruct images of any size
above a certain smallest image size. In addition, we show that our model is
capable of reconstructing images obtained from an SPI setup while being priorly
trained on natural images, which can be vastly different from the SPI images.
This opens up opportunity for the feasibility of pretrained deep learning
models for CS reconstructions of images from various domain areas
Operational approach to the Uhlmann holonomy
We suggest a physical interpretation of the Uhlmann amplitude of a density
operator. Given this interpretation we propose an operational approach to
obtain the Uhlmann condition for parallelity. This allows us to realize
parallel transport along a sequence of density operators by an iterative
preparation procedure. At the final step the resulting Uhlmann holonomy can be
determined via interferometric measurements.Comment: Added material, references, and journal reference
Job Satisfaction as Related to Safe Performance: A Case for a Manufacturing Firm
Many companies have made significant improvements in safety records, but have eventually reached a plateau. This article examines employee safety performance in regards to their job satisfaction and its implications to managers for improving employees safety performance through job redesign
Job Satisfaction as Related to Safety Performance: A Case for a Manufacturing Firm
Many companies have made significant improvements in safety records, but have eventually reached a plateau. This article examines employee safety performance in regards to their job satisfaction and its implications to managers for improving employees safety performance through job redesign
Renewable Energy from Living Plants to Power IoT Sensor for Remote Sensing
Renewable energy which can be used to replace traditional energy sources from fossil fuel is in dire demand to protect the earth from the further negative effect of climate change resulting from mining or drilling of fossil fuel and its related pollution. There are various renewable energy sources available, however, there is none currently that does not compete for arable land in nature or land for food production to enable the installation of the renewable energy facility. Thus, in this research, it is proposed a novel type of electrical energy which can be harvested from living plants and coexist well with nature without competing for any arable lands and at the same time generate energy for human needs. Plants generate energy from photosynthesis, respiration, and intercellular activities, and this energy, although is minute, still can be harvested as a new potential energy source to power any ultra-low power sensor for remote sensing purposes. Thus, it is presented in this paper, a characterization of the specific setup condition to harvest optimum minimum 3V from living plants and a power management circuit that can further boost the energy to an optimum level to power a wireless IoT sensor for remote sensing purposes. It turns the living plant into a plant-based cell. As there is wide vegetation in forests, jungles, plantations, and agricultural lands on earth, the combination of this energy from the plants could be a promising source of new renewable energy to mankind as this vegetation can exist for both food and energy production while it does not compete for arable land for the installation of energy sources such as what happens in fossil fuel, solar or wind energy to create greener earth
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