Plasmonic Brownian ratchet

Here we present a Brownian ratchet based on plasmonic interactions. By periodically turning on and off a laser beam that illuminates a periodic array of plasmonic nanostructures with broken spatial symmetry, the random thermal motion of a subwavelength dielectric bead is rectified into one direction. By means of the Molecular Dynamics technique we show a statistical directed drift in particle flow

Resolving singular forces in cavity flow: Multiscale modeling from atoms to millimeters

A multiscale approach for fluid flow is developed that retains an atomistic description in key regions. The method is applied to a classic problem where all scales contribute: The force on a moving wall bounding a fluid-filled cavity. Continuum equations predict an infinite force due to stress singularities. Following the stress over more than six decades in length in systems with characteristic scales of millimeters and milliseconds allows us to resolve the singularities and determine the force for the first time. The speedup over pure atomistic calculations is more than fourteen orders of magnitude. We find a universal dependence on the macroscopic Reynolds number, and large atomistic effects that depend on wall velocity and interactions.Comment: 4 pages,3 figure

Envy, Altruism, and the International Distribution of Trade Protection

One important puzzle in international political economy is why lower-earning and less-skilled intensive industries tend to receive relatively high levels of trade protection. This pattern of protection holds even in low-income countries in which less-skilled labor is likely to be the relatively abundant factor of production and therefore would be expected in many standard political-economy frameworks to receive relatively low, not high, levels of protection. We propose and model one possible explanation: that individual aversion to inequality—both envy and altruism—lead to systematic differences in support for trade protection across industries, with sectors employing lower-earning workers more intensively being relatively preferred recipients for trade protection. We conduct original survey experiments in China and the United States and provide strong evidence that individual policy opinions about sector-specific trade protection depend on the earnings of workers in the sector. We also present structural estimates of the influence of envy and altruism on sector-specific trade policy preferences. Our estimates indicate that both envy and altruism influence support for trade protection in the United States and that altruism influences policy opinions in China.

Delineating the biosynthesis of gentamicin x2, the common precursor of the gentamicin C antibiotic complex.

Gentamicin C complex is a mixture of aminoglycoside antibiotics used worldwide to treat severe Gram-negative bacterial infections. Despite its clinical importance, the enzymology of its biosynthetic pathway has remained obscure. We report here insights into the four enzyme-catalyzed steps that lead from the first-formed pseudotrisaccharide gentamicin A2 to gentamicin X2, the last common intermediate for all components of the C complex. We have used both targeted mutations of individual genes and reconstitution of portions of the pathway in vitro to show that the secondary alcohol function at C-3″ of A2 is first converted to an amine, catalyzed by the tandem operation of oxidoreductase GenD2 and transaminase GenS2. The amine is then specifically methylated by the S-adenosyl-l-methionine (SAM)-dependent N-methyltransferase GenN to form gentamicin A. Finally, C-methylation at C-4″ to form gentamicin X2 is catalyzed by the radical SAM-dependent and cobalamin-dependent enzyme GenD1.This work was supported by a project grant from the Medical Research Council, UK (G1001687) to P.F.L.; and by the 973 and 863 programs from the Ministry of Science and Technology of China, National Science Foundation of China, and the Translational Medical Research Fund of Wuhan University School of Medicine to Y.S.; E.M. thanks the Gates Cambridge Trust for a scholarship. We also gratefully acknowledge Dr. Xinzhou Yang, SouthCentral University for Nationalities, for his assistance in separation of gentamicin A2. We thank Dr. Andrew Truman (John Innes Institute) for helpful discussions.This is the final published version. It was originally published in Chemistry and Biology, Volume 22, Issue 2, 19 February 2015, Pages 251–261, doi:10.1016/j.chembiol.2014.12.01

Numerical simulation of sediment related processes in water quality model

Proceedings of the Seventh International Conference on Hydroscience and Engineering, Philadelphia, PA, September 2006. http://hdl.handle.net/1860/732Sediment is a major nonpoint-source pollutant, and the exchange of materials between water and sediment is an important component of the lake eutrophication process. Suspended sediment increases water surface reflectivity and light attenuation in the water column. Nutrients can be absorbed to sediment particles and desorb from sediment to the water. In addition, nutrients can also be released from bed sediment. In this study, a water quality model, CCHE3D_WQ, was applied to simulate the concentrations of phytoplankton and nutrients in a shallow, natural lake with special emphasis on sediment-related processes. A formula was generated from field measurements to calculate the light attenuation coefficient using the concentration of chlorophyll and suspended sediment. The concentrations of adsorbed and dissolved nutrients due to adsorption-desorption were calculated using two formulas derived based on the Langmuir Equation. The release rates of nutrients from the lake bed were calculated by considering the effects of the concentration gradient across the water-sediment interface, pH, temperature, dissolved oxygen concentration, and flow conditions. Model algorithms describing the adsorption and desorption of nutrients from sediment particles as well as their release from bed sediment were tested using laboratory experimental data. Model simulation results show that there are strong interactions between sediment-related processes and nutrient concentrations

Programming temporal shapeshifting

Shapeshifting enables a wide range of engineering and biomedical applications, but until now transformations have required external triggers. This prerequisite limits viability in closed or inert systems and puts forward the challenge of developing materials with intrinsically encoded shape evolution. Herein we demonstrate programmable shape-memory materials that perform a sequence of encoded actuations under constant environment conditions without using an external trigger. We employ dual network hydrogels: in the first network, covalent crosslinks are introduced for elastic energy storage, and in the second one, temporary hydrogen-bonds regulate the energy release rate. Through strain-induced and time-dependent reorganization of the reversible hydrogen-bonds, this dual network allows for encoding both the rate and pathway of shape transformations on timescales from seconds to hours. This generic mechanism for programming trigger-free shapeshifting opens new ways to design autonomous actuators, drug-release systems and active implants

Space-time crystals of trapped ions

Spontaneous symmetry breaking can lead to the formation of time crystals, as well as spatial crystals. Here we propose a space-time crystal of trapped ions and a method to realize it experimentally by confining ions in a ring-shaped trapping potential with a static magnetic field. The ions spontaneously form a spatial ring crystal due to Coulomb repulsion. This ion crystal can rotate persistently at the lowest quantum energy state in magnetic fields with fractional fluxes. The persistent rotation of trapped ions produces the temporal order, leading to the formation of a space-time crystal. We show that these space-time crystals are robust for direct experimental observation. We also study the effects of finite temperatures on the persistent rotation. The proposed space-time crystals of trapped ions provide a new dimension for exploring many-body physics and emerging properties of matter.Comment: updated to the version published in PR

Energy Consumption Forecasting Using Ensemble Learning Algorithms

DCAI 2019: Distributed Computing and Artificial Intelligence, 16th International Conference, Special SessionsThe increase of renewable energy sources of intermittent nature has brought several new challenges for power and energy systems. In order to deal with the variability from the generation side, there is the need to balance it by managing consumption appropriately. Forecasting energy consumption becomes, therefore, more relevant than ever. This paper presents and compares three different ensemble learning methods, namely random forests, gradient boosted regression trees and Adaboost. Hour-ahead electricity load forecasts are presented for the building N of GECAD at ISEP campus. The performance of the forecasting models is assessed, and results show that the Adaboost model is superior to the other considered models for the one-hour ahead forecasts. The results of this study compared to previous works indicates that ensemble learning methods are a viable choice for short-term load forecast.This work has received funding from National Funds through FCT (Fundaçao da Ciencia e Tecnologia) under the project SPET – 29165, call SAICT 2017.info:eu-repo/semantics/publishedVersio