Dynamic admittance of atomic wires

We have investigated the low-frequency admittance of quantum wires in which a section consists of several Al atoms. The atomic section is connected to two three-dimensional leads that are modeled by the jellium model. The quantum scattering problem is solved by combining the first-principles ab initio method with a transfer-matrix evaluation of the scattering matrix. The ac admittance is then computed by evaluating various partial densities of states. The nature of the ac responses are predicted for these Al atomic wires.published_or_final_versio

Quantum transport through atomic wires

We have investigated quantum transport through long wires in which a section consists of one or several Al atoms in a chain. The self-consistent ground state electronic potential is obtained using the first principles ab initio method and the conductance is calculated by solving a three-dimensional quantum scattering problem. We have observed quantized conductance when there are two or more Al atoms in the chain. Resistance is calculated for these wires at the Fermi level. ©1997 American Institute of Physics.published_or_final_versio

Structural and transport properties of aluminum atomic wires

We report a first-principles calculation of structural properties and quantum conductance of aluminum atomic wires. Our data together with a simple model allows us to predict the behavior of the elastic constant C11 as a function of the cross-sectional size of the free-standing wires. The quantum molecular dynamics, performed at both 0 and 300 K, provides information concerning the stability of these atomic wires. For the most stable wire, relaxation at 0 K causes a change of approximately 2-4 % in atomic positions, and room temperature contributes another 4–6 %. We obtain the quantum conductance of these wires by combining density functional theory and a three-dimensional evaluation of the scattering matrix. The structures obtained from the quantum molecular-dynamics simulations are examined and transport properties compared.published_or_final_versio

Capacitance of Atomic Junctions

We report the behavior of the electrochemical capacitance for a variety of atomic junctions using ab initio methods. The capacitance can be classified according to the nature of conductance and shows a remarkable crossover from a quantum dominated regime to that of a classical-like geometric behavior. Clear anomalies arise due to a finite density of states of the atomic junction as well as the role played by the atomic valence orbitals. The results suggest several experiments to study contributions due to quantum effects and the atomic degree of freedom.published_or_final_versio

A review of physical supply and EROI of fossil fuels in China

This paper reviews China’s future fossil fuel supply from the perspectives of physical output and net energy output. Comprehensive analyses of physical output of fossil fuels suggest that China’s total oil production will likely reach its peak, at about 230 Mt/year (or 9.6 EJ/year), in 2018; its total gas production will peak at around 350 Bcm/year (or 13.6 EJ/year) in 2040, while coal production will peak at about 4400 Mt/year (or 91.9 EJ/year) around 2020 or so. In terms of the forecast production of these fuels, there are significant differences among current studies. These differences can be mainly explained by different ultimately recoverable resources assumptions, the nature of the models used, and differences in the historical production data. Due to the future constraints on fossil fuels production, a large gap is projected to grow between domestic supply and demand, which will need to be met by increasing imports. Net energy analyses show that both coal and oil and gas production show a steady declining trend of EROI (energy return on investment) due to the depletion of shallow-buried coal resources and conventional oil and gas resources, which is generally consistent with the approaching peaks of physical production of fossil fuels. The peaks of fossil fuels production, coupled with the decline in EROI ratios, are likely to challenge the sustainable development of Chinese society unless new abundant energy resources with high EROI values can be found

Two-dimensional amine and hydroxy functionalized fused aromatic covalent organic framework

Ordered two-dimensional covalent organic frameworks (COFs) have generally been synthesized using reversible reactions. It has been difficult to synthesize a similar degree of ordered COFs using irreversible reactions. Developing COFs with a fused aromatic ring system via an irreversible reaction is highly desirable but has remained a significant challenge. Here we demonstrate a COF that can be synthesized from organic building blocks via irreversible condensation (aromatization). The as-synthesized robust fused aromatic COF (F-COF) exhibits high crystallinity. Its lattice structure is characterized by scanning tunneling microscopy and X-ray diffraction pattern. Because of its fused aromatic ring system, the F-COF structure possesses high physiochemical stability, due to the absence of hydrolysable weak covalent bonds

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

Pulmonary Predictors of Incident Diabetes in Smokers.

BACKGROUND: Diabetes mellitus and its complications are a large and increasing burden for health care worldwide. Reduced pulmonary function has been observed in diabetes (both type 1 and type 2), and this reduction is thought to occur prior to diagnosis. Other measures of pulmonary health are associated with diabetes, including lower exercise tolerance, greater dyspnea, lower quality of life (as measured by the St. George's Respiratory Questionaire [SGRQ]) and susceptibility to lung infection and these measures may also predate diabetes diagnosis. METHODS: We examined 7080 participants in the COPD Genetic Epidemiology (COPDGene) study who did not report diabetes at their baseline visit and who provided health status updates during 4.2 years of longitudinal follow-up (LFU). We used Cox proportional hazards modeling, censoring participants at final LFU contact, reported mortality or report of incident diabetes to model predictors of diabetes. These models were constructed using known risk factors as well as proposed markers related to pulmonary health, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FEV1/FVC, respiratory exacerbations (RE), 6-minute walk distance (6MWD), pulmonary associated quality of life (as measured by the SGRQ), corticosteroid use, chronic bronchitis and dyspnea. RESULTS: Over 21,519 person years of follow-up, 392 of 7080 participants reported incident diabetes which was associated with expected predictors; increased body mass index (BMI), high blood pressure, high cholesterol and current smoking status. Age, gender and accumulated smoking exposure were not associated with incident diabetes. Additionally, preserved ratio with impaired spirometry (PRISm) pattern pulmonary function, reduced 6MWD and any report of serious pulmonary events were associated with incident diabetes. CONCLUSIONS: This cluster of pulmonary indicators may aid clinicians in identifying and treating patients with pre- or undiagnosed diabetes

Improving I/O Performance for Exascale Applications through Online Data Layout Reorganization

The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity and record turn-around time. Many of these codes are based on particle-mesh methods and use advanced algorithms, especially dynamic load-balancing and mesh-refinement, to achieve high performance on Exascale machines. Yet, as such algorithms improve parallel application efficiency, they raise new challenges for I/O logic due to their irregular and dynamic data distributions. Thus, while the enormous data rates of Exascale simulations already challenge existing file system write strategies, the need for efficient read and processing of generated data introduces additional constraints on the data layout strategies that can be used when writing data to secondary storage. We review these I/O challenges and introduce two online data layout reorganization approaches for achieving good tradeoffs between read and write performance. We demonstrate the benefits of using these two approaches for the ECP particle-in-cell simulation WarpX, which serves as a motif for a large class of important Exascale applications. We show that by understanding application I/O patterns and carefully designing data layouts we can increase read performance by more than 80 percent