Description: © The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Biogeosciences 13 (2016): 2011-2028, doi:10.5194/bg-13-2011-2016.

Description: As three-dimensional (3-D) aquatic ecosystem models are used more frequently for operational water quality forecasts and ecological management decisions, it is important to understand the relative strengths and limitations of existing 3-D models of varying spatial resolution and biogeochemical complexity. To this end, 2-year simulations of the Chesapeake Bay from eight hydrodynamic-oxygen models have been statistically compared to each other and to historical monitoring data. Results show that although models have difficulty resolving the variables typically thought to be the main drivers of dissolved oxygen variability (stratification, nutrients, and chlorophyll), all eight models have significant skill in reproducing the mean and seasonal variability of dissolved oxygen. In addition, models with constant net respiration rates independent of nutrient supply and temperature reproduced observed dissolved oxygen concentrations about as well as much more complex, nutrient-dependent biogeochemical models. This finding has significant ramifications for short-term hypoxia forecasts in the Chesapeake Bay, which may be possible with very simple oxygen parameterizations, in contrast to the more complex full biogeochemical models required for scenario-based forecasting. However, models have difficulty simulating correct density and oxygen mixed layer depths, which are important ecologically in terms of habitat compression. Observations indicate a much stronger correlation between the depths of the top of the pycnocline and oxycline than between their maximum vertical gradients, highlighting the importance of the mixing depth in defining the region of aerobic habitat in the Chesapeake Bay when low-oxygen bottom waters are present. Improvement in hypoxia simulations will thus depend more on the ability of models to reproduce the correct mean and variability of the depth of the physically driven surface mixed layer than the precise magnitude of the vertical density gradient.

Description: This work was supported by the NOAA IOOS program as part of the Coastal Ocean Modeling Testbed.

Description: Author(s): Limei Wang, Hao Zhang, Linjie Zhang, Georg Raithel, Jianming Zhao, and Suotang Jia Mixed adiabatic and diabatic passages through avoided crossings in the Stark map of cesium Rydberg atoms are employed as beam splitters and recombiners in an atom-interferometric measurement of energy-level splittings. We subject cold cesium atoms to laser-excitation, electric-field, and detection s… [Phys. Rev. A 92, 033619] Published Mon Sep 21, 2015

Description: The detection of existence of fullerenes (C60 and C70) makes it necessary to explore whether soot nanoparticles can participate in new nanometer-sized particle formation and growth in the atmosphere. This study describes a theoretical investigation at multiple levels on the role of the fullerenes (as model compounds to represent nanoparticles of soot) in the formation of complexes with a common atmospheric nucleating precursor (sulfuric acid, SA) and a biogenic organic acid (cis-pinonic acid, CPA), as well as initial growth of nano-sized biogenic aerosols. Quantum chemical density-functional theory calculations identify the formation of stable fullerene-[CPA-SA] ternary complexes, which likely leads to an enhanced nucleation of SA with CPA. Relevant thermochemical parameters including the changes of Gibbs free energy, enthalpy, and entropy for the complex formation also support that fullerene-[CPA-SA] is most likely to be a newly formed nuclei. The sizes of the critical nucleus of the fullerene-[CPA-SA-H2O] systems were found to be approximately 1.3 nm by large-scale molecular dynamics simulations. This study may provide a new insight into the mechanisms underlying the formation of new particle in the atmospheric environment.

Description: In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptively partitioned into two parts, persistent and birth measurement sets, for updating the persistent and birth target Probability Hypothesis Density, respectively. Furthermore, the collaboration mechanism of multiple probability hypothesis density (PHDs) is established, where tracks can be automatically extracted. Simulation results reveal that the proposed filter yields considerable computational savings in processing requirements and significant improvement in tracking accuracy.

Description: A new approach for three-dimensional (3-D) reconstruction of building roofs from airborne light detection and ranging (LiDAR) data is proposed, and it includes four steps. Building roof points are first extracted from LiDAR data by using the reversed iterative mathematic morphological (RIMM) algorithm and the density-based method. The corresponding relations between points and rooftop patches are then established through a smoothness strategy involving “seed point selection, patch growth, and patch smoothing.” Layer-connection points are then generated to represent a layer in the horizontal direction and to connect different layers in the vertical direction. Finally, by connecting neighboring layer-connection points, building models are constructed with the second level of detailed data. The key contributions of this approach are the use of layer-connection points and the smoothness strategy for building model reconstruction. Experimental results are analyzed from several aspects, namely, the correctness and completeness, deviation analysis of the reconstructed building roofs, and the influence of elevation to 3-D roof reconstruction. In the two experimental regions used in this paper, the completeness and correctness of the reconstructed rooftop patches were about 90% and 95%, respectively. For the deviation accuracy, the average deviation distance and standard deviation in the best case were 0.05 m and 0.18 m, respectively; and those in the worst case were 0.12 m and 0.25 m. The experimental results demonstrated promising correctness, completeness, and deviation accuracy with satisfactory 3-D building roof models.

Description: Author(s): Yan-Feng Lv, Wen-Lin Wang, Hao Ding, Yang Wang, Ying Ding, Ruidan Zhong, John Schneeloch, G. D. Gu, Lili Wang, Ke He, Shuai-Hua Ji, Lin Zhao, Xing-Jiang Zhou, Can-Li Song, Xu-Cun Ma, and Qi-Kun Xue By means of low-temperature scanning tunneling microscopy, we report on the electronic structures of the BiO and SrO planes of the Bi 2 Sr 2 CuO 6 + δ (Bi-2201) superconductor prepared by argon-ion bombardment and annealing. Depending on post annealing conditions, the BiO planes exhibit either a pseudogap … [Phys. Rev. B 93, 140504(R)] Published Fri Apr 15, 2016

Description: Author(s): Ping Wang, Hao Zha, Igor Syratchev, Jiaru Shi, and Huaibi Chen We present an X-band high-power pulse compression system for a klystron-based compact linear collider. In this system design, one rf power unit comprises two klystrons, a correction cavity chain, and two SLAC Energy Doubler (SLED)-type X-band pulse compressors (SLEDX). An rf pulse passes the correct... [Phys. Rev. Accel. Beams 20, 112001] Published Fri Nov 03, 2017

Description: Sustainability, Vol. 9, Pages 1853: Analysis of the Driving Forces in Vegetation Variation in the Grain for Green Program Region, China Sustainability doi: 10.3390/su9101853 Authors: Wang Hao Liu Guohua Li Zongshan Ye Xin Fu Bojie Lü Yihe The Chinese government introduced six ecological restoration programs to improve its natural environment. Although these programs have proven successful in improving local environmental conditions, some studies have questioned their effectiveness when regions suffer from extreme weather conditions. Using the Grain for Green Program (GGP) region as a study area, we estimated vegetation activities in the GGP region from 2000 to 2010 to clarify the trends in vegetation growth and their driving forces. The results showed that: (1) vegetation activities improved in the GGP region during 2000-2010, with 58.94% of the area showing an increased trend in the NDVI (normalized difference vegetation index); (2) 26.33% of the increased vegetation was caused by human interference, and 11.61% by climate variation, human activity was the dominant cause, and resulted in 54.68% of the degradation compared to 4.74% from climate change; and, (3) the contribution of different land use types to the NDVI interannual variations showed that high contribution regions were focused in the arid and semiarid areas, where the vegetation growth is associated with variations in recipitation and temperature. However, conversions between farmland and grassland or forest had a significant effect on the change in the NDVI trend. Therefore, although climate conditions can affect vegetation growth, human activities are more important in vegetation changes, and appropriate human activities would contribute to its continual improvement. Hence, we recommend establishing an assessment and scientific management mechanism for eco-risks in the design and management of ecosystem restoration programs.

Description: Author(s): Juanjuan Liu, Jinchen Wang, Jieming Sheng, Feng Ye, Keith M. Taddei, J. A. Fernandez-Baca, Wei Luo, Guang-Ai Sun, Zhi-Cheng Wang, Hao Jiang, Guang-Han Cao, and Wei Bao   Sr 2 Cr 3 As 2 O 2 is composed of alternating square-lattice CrO 2 and Cr 2 As 2 stacking layers, where CrO 2 is isostructural to the CuO 2 building-block of cuprate high- T c superconductors and Cr 2 As 2 to Fe 2 As 2 of Fe-based superconductors. Current interest in this material is raised by theoretic prediction of p... [Phys. Rev. B 98, 134416] Published Wed Oct 10, 2018

Description: Sensors, Vol. 18, Pages 2591: A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks Sensors doi: 10.3390/s18082591 Authors: Yixiong Feng Yong Wang Hao Zheng Shanghua Mi Jianrong Tan Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.