Technical Note: An efficient method for accelerating the spin-up process for process-based biogeochemistry models

To better understand the role of terrestrial ecosystems in the global carbon cycle and their feedbacks to the global climate system, process-based biogeochemistry models need to be improved with respect to model parameterization and model structure. To achieve these improvements, the spin-up time for those differential equation-based models needs to be shortened. Here, an algorithm for a fast spin-up was developed by finding the exact solution of a linearized system representing the cyclo-stationary state of a model and implemented in a biogeochemistry model, the Terrestrial Ecosystem Model (TEM). With the new spin-up algorithm, we showed that the model reached a steady state in less than 10 years of computing time, while the original method requires more than 200 years on average of model run. For the test sites with five different plant functional types, the new method saves over 90 % of the original spin-up time in site-level simulations. In North American simulations, average spin-up time savings for all grid cells is 85 % for either the daily or monthly version of TEM. The developed spin-up method shall be used for future quantification of carbon dynamics at fine spatial and temporal scales.</p

Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm

Reservoirs and dams are vital human-built infrastructures that play essential roles in flood control, hydroelectric power generation, water supply, navigation, and other functions. The realization of those functions requires efficient reservoir operation, and the effective controls on the outflow from a reservoir or dam. Over the last decade, artificial intelligence (AI) techniques have become increasingly popular in the field of streamflow forecasts, reservoir operation planning and scheduling approaches. In this study, three AI models, namely, the backpropagation (BP) neural network, support vector regression (SVR) technique, and long short-term memory (LSTM) model, are employed to simulate reservoir operation at monthly, daily, and hourly time scales, using approximately 30 years of historical reservoir operation records. This study aims to summarize the influence of the parameter settings on model performance and to explore the applicability of the LSTM model to reservoir operation simulation. The results show the following: (1) for the BP neural network and LSTM model, the effects of the number of maximum iterations on model performance should be prioritized; for the SVR model, the simulation performance is directly related to the selection of the kernel function, and sigmoid and RBF kernel functions should be prioritized; (2) the BP neural network and SVR are suitable for the model to learn the operation rules of a reservoir from a small amount of data; and (3) the LSTM model is able to effectively reduce the time consumption and memory storage required by other AI models, and demonstrate good capability in simulating low-flow conditions and the outflow curve for the peak operation period

Active optical clock based on four-level quantum system

Active optical clock, a new conception of atomic clock, has been proposed recently. In this report, we propose a scheme of active optical clock based on four-level quantum system. The final accuracy and stability of two-level quantum system are limited by second-order Doppler shift of thermal atomic beam. To three-level quantum system, they are mainly limited by light shift of pumping laser field. These limitations can be avoided effectively by applying the scheme proposed here. Rubidium atom four-level quantum system, as a typical example, is discussed in this paper. The population inversion between $6S_{1/2}$ and $5P_{3/2}$ states can be built up at a time scale of $10^{-6}$s. With the mechanism of active optical clock, in which the cavity mode linewidth is much wider than that of the laser gain profile, it can output a laser with quantum-limited linewidth narrower than 1 Hz in theory. An experimental configuration is designed to realize this active optical clock.Comment: 5 page

Deconfinement Phase Transition in an Expanding Quark system in Relaxation Time Approximation

We investigated the effects of nonequilibrium and collision terms on the deconfinement phase transition of an expanding quark system in Friedberg-Lee model in relaxation time approximation. By calculating the effective quark potential, the critical temperature of the phase transition is dominated by the mean field, while the collisions among quarks and mesons change the time structure of the phase transition significantly.Comment: 7 pages, 7 figure

A new understanding of the effect of filler minerals on the precipitation of synthetic C–S–H

The filler effect is the most important physical mechanism of mineral admixtures in the early hydration of cement whose chemical properties greatly affect the precipitation of C–S–H. In this study, calcite, strontianite, magnesite, dolomite, quartz, whewellite and whitlockite were selected as the fillers. The morphology and reaction kinetics of synthetic C–S–H precipitated on the surfaces of different fillers were studied via electron microscopy observations and electrical conductivity and ion concentration measurements. The precipitation rate of C–S–H has a positive correlation with the affinity of Ca2+ for adsorption on the fillers, which can be explained by the nucleation barrier of C–S–H. Extremely ordered honeycomb-like morphology of the C–S–H is found on calcite and strontianite surfaces, while less regular leaf-like or honeycomb-like C–S–H is found on whewellite and whitlockite. The ordered C–S–H pattern is related to the lattice cleavage of the ionic compound filler. In the case of quartz, C–S–H prefers growth along the tangential direction, which is quite different from the normal-direction growth on ionic compounds. The in-plane growth of C–S–H on quartz is believed to be induced by a layer of loosely physically adsorbed Ca2+

Entanglement-Assisted Communication Surpassing the Ultimate Classical Capacity

Entanglement underpins a variety of quantum-enhanced communication, sensing, and computing capabilities. Entanglement-assisted communication (EACOMM) leverages entanglement pre-shared by communication parties to boost the rate of classical information transmission. Pioneering theory works showed that EACOMM can enable a communication rate well beyond the ultimate classical capacity of optical communications, but an experimental demonstration of any EACOMM advantage remains elusive. Here, we report the implementation of EACOMM surpassing the classical capacity over lossy and noisy bosonic channels. We construct a high-efficiency entanglement source and a phase-conjugate quantum receiver to reap the benefit of pre-shared entanglement, despite entanglement being broken by channel loss and noise. We show that EACOMM beats the Holevo-Schumacher-Westmoreland capacity of classical communication by up to 14.6%, when both protocols are subject to the same power constraint at the transmitter. As a practical performance benchmark, a classical communication protocol without entanglement assistance is implemented, showing that EACOMM can reduce the bit-error rate by up to 69% over the same bosonic channel. Our work opens a route to provable quantum advantages in a wide range of quantum information processing tasks.Comment: 12 pages, 5 figures. Comments are welcom

Enhancement of Transition Temperature in FexSe0.5Te0.5 Film via Iron Vacancies

The effects of iron deficiency in FexSe0.5Te0.5 thin films (0.8<x<1) on superconductivity and electronic properties have been studied. A significant enhancement of the superconducting transition temperature (TC) up to 21K was observed in the most Fe deficient film (x=0.8). Based on the observed and simulated structural variation results, there is a high possibility that Fe vacancies can be formed in the FexSe0.5Te0.5 films. The enhancement of TC shows a strong relationship with the lattice strain effect induced by Fe vacancies. Importantly, the presence of Fe vacancies alters the charge carrier population by introducing electron charge carriers, with the Fe deficient film showing more metallic behavior than the defect-free film. Our study provides a means to enhance the superconductivity and tune the charge carriers via Fe vacancy, with no reliance on chemical doping.Comment: 15 pages, 4 figure

Impact of anthropogenic emission on air quality over a megacity – revealed from an intensive atmospheric campaign during the Chinese Spring Festival

The Chinese Spring Festival is one of the most important traditional festivals in China. The peak transport in the Spring Festival season (spring travel rush) provides a unique opportunity for investigating the impact of human activity on air quality in the Chinese megacities. Emission sources are varied and fluctuate greatly before, during and after the Festival. Increased vehicular emissions during the spring travel rush before the 2009 Festival resulted in high level pollutants of NOx (270 μg m−3), CO (2572 μg m−3), black carbon (BC) (8.5 μg m−3) and extremely low single scattering albedo of 0.76 in Shanghai, indicating strong, fresh combustion. Organics contributed most to PM2.5, followed by NO3−, NH4+, and SO42−. During the Chinese Lunar New Year\u27s Eve and Day, widespread usage of fireworks caused heavy pollution of extremely high aerosol concentration, scattering coefficient, SO2, and NOx. Due to the spring travel rush after the festival, anthropogenic emissions gradually climbed and mirrored corresponding increases in the aerosol components and gaseous pollutants. Secondary inorganic aerosol (SO42−, NO3−, and NH4+) accounted for a dominant fraction of 74% in PM2.5 due to an increase in human activity. There was a greater demand for energy as vast numbers of people using public transportation or driving their own vehicles returned home after the Festival. Factories and constructions sites were operating again. The potential source contribution function (PSCF) analysis illustrated the possible source areas for air pollutants of Shanghai. The effects of regional and long-range transport were both revealed. Five major sources, i.e. natural sources, vehicular emissions, burning of fireworks, industrial and metallurgical emissions, and coal burning were identified using the principle component analysis. The average visibility during the whole study period was less than 6 km. It had been estimated that 50% of the total light extinction was due to the high water vapor in the atmosphere. This study demonstrates that organic aerosol was the largest contributor to aerosol extinction at 47%, followed by sulfate ammonium, nitrate ammonium, and EC at 22%, 14%, and 12%, respectively. Our results indicated the dominant role of traffic-related aerosol species (i.e. organic aerosol, nitrate and EC) on the formation of air pollution, and suggested the importance of controlling vehicle numbers and emissions in mega-cities of China as its population and economy continue to grow