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

π0→γ∗γ\pi^0\to\gamma^*\gamma transition form factor within Light Front Quark Model

We study the transition form factor of $\pi^0\to\gamma^* \gamma$ as a function of the momentum transfer $Q^2$ 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 $Q^2$ 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

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

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

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