Video semantic recognition usually suffers from the curse of dimensionality and the absence of enough high-quality labeled instances, thus semisupervised feature selection gains increasing attentions for its efficiency and comprehensibility. Most of the previous methods assume that videos with close distance (neighbors) have similar labels and characterize the intrinsic local structure through a predetermined graph of both labeled and unlabeled data. However, besides the parameter tuning problem underlying the construction of the graph, the affinity measurement in the original feature space usually suffers from the curse of dimensionality. Additionally, the predetermined graph separates itself from the procedure of feature selection, which might lead to downgraded performance for video semantic recognition. In this paper, we exploit a novel semisupervised feature selection method from a new perspective. The primary assumption underlying our model is that the instances with similar labels should have a larger probability of being neighbors. Instead of using a predetermined similarity graph, we incorporate the exploration of the local structure into the procedure of joint feature selection so as to learn the optimal graph simultaneously. Moreover, an adaptive loss function is exploited to measure the label fitness, which significantly enhances model's robustness to videos with a small or substantial loss. We propose an efficient alternating optimization algorithm to solve the proposed challenging problem, together with analyses on its convergence and computational complexity in theory. Finally, extensive experimental results on benchmark datasets illustrate the effectiveness and superiority of the proposed approach on video semantic recognition related tasks.

semisupervised learning , video semantic recognition , Feature selection , manifold regularization

A new measurement of the reactor antineutrino flux and energy spectrum by the Daya Bay reactor neutrino experiment is reported. The antineutrinos were generated by six 2.9 GW(th) nuclear reactors and detected by eight antineutrino detectors deployed in two near (560 m and 600 m flux-weighted baselines) and one far (1640 m flux-weighted baseline) underground experimental halls. With 621 days of data, more than 1.2 million inverse beta decay (IBD) candidates were detected. The IBD yield in the eight detectors was measured, and the ratio of measured to predicted flux was found to be 0.946 +/- 0.020 (0.992 +/- 0.021) for the Huber+Mueller (ILL+Vogel) model. A 2.9 sigma deviation was found in the measured IBD positron energy spectrum compared to the predictions. In particular, an excess of events in the region of 4-6 MeV was found in the measured spectrum, with a local significance of 4.4 sigma. A reactor antineutrino spectrum weighted by the IBD cross section is extracted for model-independent predictions.

Daya Bay , energy spectrum , antineutrino flux , reactor

Thermoresponsive polymers that undergo reversible phase transition by responding to an environmental temperature change, in particular polymers showing lower critical solution temperature (LCST), are frequently used as smart materials that have found increasing applications. Recently, there has been a rapid growth in interest on LCST polymers and many new research groups are entering the field from a wide range of application areas. While it is great to see more researchers working on LCST polymers, the downside of this rapid growth is that the fundamentals of the LCST phase transition behavior are not always clearly known and respected. Hence, this focus article provides a systematic discussion of the key aspects of the LCST behavior of polymers starting from fundamentals of LCST behavior to practical determination of cloud point temperature (T-cp). Finally, we offer a basic set of recommended measuring conditions for determination of T-cp (10 mg mL(-1); 0.5 degrees C min(-1); 600 nm) to facilitate the comparison of the LCST behavior and T-cp values of polymers developed and studied in different laboratories around the globe, which is nowadays nearly impossible since various techniques and parameters are being utilized for the measurements. It should be noted that these recommended conditions serve as a robust tool for turbidimetry, which is one out of the many characterization techniques one should utilize to fully understand LCST behavior of polymers.

The diffusion of gas molecules on multilayer graphene surfaces is of great importance for a wide range of applications in gas-related industries. This study calculates diffusion coefficients for gas diffusion on single layer or multilayer graphene surfaces based on molecular dynamics simulations with a major emphasis on the effect of the number of graphene layers. The results show that the gas diffusion on these graphene surfaces is mainly controlled by molecular collisions in the adsorption layer; because the contributions of the gas adsorption energy and the gas collision energy are always comparable with the gas adsorption energy becoming slightly stronger with increasing number of graphene layers. Therefore, the surface diffusion coefficient decreases gradually with increasing number of graphene layers owing to the larger number of adsorbed molecules on graphene surfaces with more layers. Notably, the diffusion coefficients do not depend strongly on the number of graphene layers when there are a large number of graphene layers due to the limited interaction distance between the gas molecules and the graphene atoms. Furthermore, the variations of the surface diffusion coefficient with the number of graphene layers and the gas species are confirmed from the probability distributions of the molecular jump length on the graphene surface in a given time period. © 2017 Elsevier Ltd

Adsorption , Diffusion , Diffusion in gases , Gas adsorption , Gases , Mass transfer , Molecular dynamics , Molecules , Multilayers , Probability distributions , Surface diffusion , Adsorbed molecules , Adsorption energies , Adsorption layer , Graphene layers , Interaction distances , Molecular collisions , Molecular dynamics simulations , Multilayer graphene

A measurement of electron antineutrino oscillation by the Daya Bay Reactor Neutrino Experiment is described in detail. Six 2.9-GWth nuclear power reactors of the Daya Bay and Ling Ao nuclear power facilities served as intense sources of nu((e) over bar)'s. Comparison of the (v) over bare rate and energy spectrum measured by antineutrino detectors far from the nuclear reactors (similar to 1500-1950 m) relative to detectors near the reactors (similar to 350-600 m) allowed a precise measurement of (v) over bar (e) disappearance. More than 2.5 million (v) over bar (e) inverse beta-decay interactions were observed, based on the combination of 217 days of operation of six antineutrino detectors (December, 2011-July, 2012) with a subsequent 1013 days using the complete configuration of eight detectors (October, 2012-July, 2015). The (v) over bar (e) rate observed at the far detectors relative to the near detectors showed a significant deficit, R = 0.949 +/- 0.002(stat) +/- 0.002(syst). The energy dependence of (v) over bar (e) disappearance showed the distinct variation predicted by neutrino oscillation. Analysis using an approximation for the three-flavor oscillation probability yielded the flavor-mixing angle sin(2)2 theta(13) = 0.0841 +/- 0.0027(stat) +/- 0.0019(syst) and the effective neutrino mass-squared difference of broken vertical bar Delta m(ee)(2)vertical bar = (2.50 +/- 0.06(stat) +/- 0.06(syst)) x 10(-3) eV(2). Analysis using the exact three-flavor probability found Delta m(32)(2) = (2.45 +/- 0.06(stat) +/- 0.06d(syst)) x 10(-3) eV(2) assuming the normal neutrino mass hierarchy and Delta m(32)(2) = (-2.56 +/- 0.06(stat) +/- 0.06(syst)) x 10(-3) eV(2) for the inverted hierarchy.

Co-pyrolysis of coal and lignocellulose biomass and coal is the key step of other co-thermochemical conversion, and conversion of co-pyrolysis char is the rate-determining step of co-gasification and co-combustion. In this paper, the influence of biomass model compounds (cellulose, hemicellulose, and lignin, abbreviated as CE, HCE and LIG) on the co-pyrolysis char structure transformation was investigated. Carbon structure and surface morphology of co-pyrolysis char were examined by Raman spectroscopy and scanning electron microscope (SEM). A comprehensive comparison of Raman spectral deconvolution methods based on various fitting functions and peak numbers was explored, and Gaussian-Lorentzian-function with no less than nine peaks showed the best performance. Three biomass model show different effects on the transformation of microstructure structure. The addition of CE increased the ordering of char structure. HCE promoted the disordering degree of microstructure structure and reached the maximum at 50% HCE mass ratio. The microstructure structure changes of co-pyrolysis char for 25% and 50% LIG mass ratios were not evident, while 75% LIG increased the disordering degree of the co-pyrolysis char. Fractal analysis was applied for describing the char surface morphology quantitatively with two and three-dimensional fractal dimensions. CE decreased the fractal dimensions of co-pyrolysis, and the influence of HCE and LIG depended on the mass ratio. © 2017

Biomass , Bituminous coal , Carbon , Cellulose , Coal , Coal combustion , Fractal dimension , Fractals , Lignin , Microstructure , Morphology , Scanning electron microscopy , Surface morphology , Comprehensive comparisons , Copyrolysis , Different effects , Fitting functions , Lignocellulosic biomass , Lorentzian functions , Rate determining step , Thermochemical Conversion

In the present study, several current S-CO2 Brayton cycle layouts are reviewed, and considered to be integrated into the existing mature molten salt solar power tower (SPT) systems. The SPT systems integrated with S-CO2 Brayton cycles are completely modeled by an integrative approach. The performances of these different cycles are compared comprehensively for applications in molten salt SPT systems from the aspects of the efficiency, the specific work, and the incorporation ability with the thermal energy storage indicated by the molten salt temperature difference across the solar receiver. The results indicate: (1) The intercooling cycle can generally offer the highest efficiency, followed by the partial-cooling cycle, and the recompression cycle; The precompression cycle can yield higher efficiency than the recompression cycle when the compressor inlet temperature is high; The increase in the hot salt temperature cannot always result in the efficiency improvement of the SPT systems. (2) The partial-cooling cycle can offer the largest specific work, while the recompression cycle and the split expansion cycle yield the lowest specific work. (3) The molten salt temperature differences of SPT systems with the simple recuperation cycle, the partial-cooling cycle, and the precompression cycle are slightly larger than those of SPT systems with the recompression cycle, the split expansion cycle, and the intercooling cycle. (4) As a classical approach to improve efficiency, reheating can decrease the system efficiency in the cases with high hot molten salt temperature; SPT systems without reheating can yield larger molten salt temperature difference than those with reheating. (5) Although the current S-CO2 Brayton cycle layouts can offer high efficiency, there are still several challenges for integrating them into the SPT systems: the specific work is relatively small, and the temperature difference across the solar receiver is narrow. Further work remains to build novel S-CO2 cycle layouts with high efficiency, large specific work, and wide temperature difference. (C) 2017 Published by Elsevier Ltd.

Solar power tower , Molten salt , Thermal energy storage , Efficiency , Supercritical CO2 Brayton cycles , Specific work

Heating capacity of an air-source heat pump (ASHP) system often decreases when it is operated in winter. This is because of frosting significantly affects the heat transfer efficiency of evaporator, and thus the airflow passage blocked. In order to solve this problem, a novel frost-free ASHP system, integrated with dehumidification and thermal energy storage, has been developed. In this paper, to further investigate the dynamic characteristics of the system working at low temperature, a mathematical model of the novel frost-free ASHP system was constructed. The mathematical model was verified by comparison with experimental data that showed that the measured results were in good accordance with the numerical ones. According to the mathematical model, the research results indicated that, at relatively humidity (RH) of 80%, the system average COP increased by 56.2% when ambient temperature increased from -10 degrees C to 0 degrees C. However, it decreased by 6.7% when RH increased from 75% to 85% at temperature of 0 degrees C. In addition, the system average COP at the air velocity of 3.0 in s(-1) was higher 0.22 and 0.16 than that of 2.5 m s(-1) and 3.5 m s(-1). Finally, the correlations of the system frost-free working time and the system COP with ambient temperature and relative humidity were obtained, respectively, by multivariate linear regression. These results provided a basis in improving and optimizing the thermal system COP and other main performance parameters. (C) 2016 Elsevier Ltd. All rights reserved.

Solid desiccant , Thermal energy storage , System optimization , Frost , Mathematical model , Air-source heat pump

In this work, we develop a two-phase lattice Boltzmann method (LBM) to simulate axisymmetric thermocapillary flows. This method simulates the immiscible axisymmetric two-phase flow by an improved color-gradient model, in which the single-phase collision, perturbation and recoloring operators are all presented with the axisymmetric effect taken into account in a simple and computational consistent manner. An additional lattice Boltzmann equation is introduced to describe the evolution of the axisymmetric temperature field, which is coupled to the hydrodynamic equations through an equation of state. This method is first validated by simulations of Rayleigh-Benard convection in a vertical cylinder and thermocapillary migration of a deformable droplet at various Marangoni numbers. It is then used to simulate the thermocapillary migration of two spherical droplets in a constant applied temperature gradient along their line of centers, and the influence of the Marangoni number (Ca), initial distance between droplets (S-0), and the radius ratio of the leading to trailing droplets (A) on the migration process is systematically studied. As Ma increases, the thermal wake behind the leading droplet strengthens, resulting in the transition of the droplet migration from coalescence to non-coalescence; and also, the final distance between droplets increases with Ma for the non-coalescence cases. The variation of,S-0 does not change the final state of the droplets although it has a direct impact on the migration process. In contrast, A can significantly influence the migration process of both droplets and their final state: at low Ma, decreasing A favors the coalescence of both droplets; at high Ma, the two droplets do not coalesce eventually but migrate with the same velocity for the small values of A, and decreasing A leads to a shorter equilibrium time and a faster migration velocity. (C) 2016 Elsevier Ltd. All rights reserved.

Lattice Boltzmann method , Droplet interactions , Marangoni number , Thermocapillary migration , Axisymmetric thermal flow

Identifying the influential nodes in complex networks is a fundamental and practical topic at the moment. Local metric like degree centrality measure is relatively simple and of less effectiveness, although the global metrics such as closeness and betweenness centrality measure can better identify influential nodes, still, there are some disadvantages and limitations. In this paper, a new efficiency centrality (EffC) to rank the spreaders in the whole network is proposed, which identify influential nodes by removing each node and meanwhile considering the changing degree of the whole network efficiency after removal. To evaluate the performance of our method, Susceptible-Infected-Recovered (SIR) model is used to simulate the epidemic spreading in four real networks. The experimental and simulated results show the efficiency and practicability of the proposed method. Thus, it is significant to rank spreaders in complex networks by using Network Efficiency. And our proposed EffC is proved to be a feasible and effective measure to identify influential nodes. © 2016 Elsevier B.V.

Efficiency , Spreaders , Betweenness centrality , Degree centrality , Effective measures , Epidemic spreading , Influential nodes , Network efficiency , Simulated results , Susceptible-infected-recovered model