Recent observations support an emerging paradigm that climate variability dominates nutrient enrichment in costal eco-systems, which can explain seasonal and inter-annual variability of phytoplankton community composi...Recent observations support an emerging paradigm that climate variability dominates nutrient enrichment in costal eco-systems, which can explain seasonal and inter-annual variability of phytoplankton community composition, biomass (Chl-a), and primary production (PP). In this paper, we combined observation and modeling to investigate the regulation of phytoplankton dynamics in Chesapeake Bay. The year we chose is 1996 that has high river runoff and is usually called a 'wet year'. A 3-D physical-biogeochemical model based on ROMS was developed to simulate the seasonal cycle and the regional distributions of phytoplankton biomass and primary production in Chesapeake Bay. Based on the model results, NO3 presents a strong contrast to the river nitrate load during spring and the highest concentration in the bay reaches around 80 mmol Nm-3 . Compared with the normal year, phytoplankton bloom in spring of 1996 appears in lower latitudes with a higher concentration. Quantitative comparison between the modeled and observed seasonal averaged dissolved inorganic nitrogen concentrations shows that the model produces reliable results. The correlation coefficient r2 for all quantities exceeds 0.95, and the skill parameter for the four seasons is all above 0.95.展开更多
Drum level sloshing is the latest discovery in the application of heat recovery steam generator (HRSG) in combined cycle, and shows certain negative influence on drum level controlling. In order to improve drum level ...Drum level sloshing is the latest discovery in the application of heat recovery steam generator (HRSG) in combined cycle, and shows certain negative influence on drum level controlling. In order to improve drum level controlling, influence factors on the drum level sloshing were investigated. Firstly, drum sub-modules were developed using the method of modularization modeling, and then the model of drum level sloshing was set up as well. Experiments were carried out on the experimental rig, and the model was validated using the obtained experimental results. Dynamic simulation was made based on the model to get a 3-D graph of drum level sloshing, which shows a vivid procedure of drum level sloshing. The effect of feed-water flow rate, main-steam flow rate and heating quantity on the drum level sloshing was analyzed. The simulation results indicate that the signals with frequency higher than 0.05 Hz are that of drum level sloshing, the signals with frequency of 0.0-0.05 Hz are that of drum level trendy and "false water level", and variation of the feed-water flow rates, main-steam flow rates and heating quantities can change the frequency of drum level sloshing, i.e., the frequency of sloshing increases with the increase of feed-water flow rate, or the decrease of the main-steam flow rate and the heating quantity. This research work is fundamental to improve signal-to-noise ratio of drum level signal and precise controlling of drum level.展开更多
Several of the most celebrated examples of visual mimicry, like mimetic eggs laid by avian brood parasites and pala-table insects mimicking distasteful ones, involve signals directed at the eyes of birds. Despite this...Several of the most celebrated examples of visual mimicry, like mimetic eggs laid by avian brood parasites and pala-table insects mimicking distasteful ones, involve signals directed at the eyes of birds. Despite this, studies of mimicry from the avian visual perspective have been rare, particularly with regard to defensive mimicry and masquerade. Defensive visual mimicry, which includes Batesian and Mtillerian mimicry, occurs when organisms share a visual signal that functions to deter predators. Masquerade occurs when an organism mimics an inedible or uninteresting object, such as a leaf, stick, or pebble. In this paper, I present five case studies covering diverse examples of defensive mimicry and masquerade as seen by birds. The best-known cases of defensive visual mimicry typically come from insect prey, but birds themselves can exhibit defensive visual mimicry in an at- tempt to escape mobbing or dissuade avian predators. Using examples of defensive visual mimicry by both insects and birds, I show how quantitative models of avian color, luminance, and pattern vision can be used to enhance our understanding of mimicry in many systems and produce new hypotheses about the evolution and diversity of signals. Overall, I investigate examples of Batesian mimicry （1 and 2）, Miillerian mimicry （3 and 4）, and masquerade （5） as follows： 1） Polymorphic mimicry in African mocker swallowtail butterflies; 2） Cuckoos mimicking sparrowhawks; 3） Mimicry rings in Neotropical butterflies; 4） Plumage mimicry in toxic pitohuis; and 5） Dead leaf-mimicking butterflies and mantids.展开更多
基金supported by the National Science Foundation project of M. Li (OCE-082543)
文摘Recent observations support an emerging paradigm that climate variability dominates nutrient enrichment in costal eco-systems, which can explain seasonal and inter-annual variability of phytoplankton community composition, biomass (Chl-a), and primary production (PP). In this paper, we combined observation and modeling to investigate the regulation of phytoplankton dynamics in Chesapeake Bay. The year we chose is 1996 that has high river runoff and is usually called a 'wet year'. A 3-D physical-biogeochemical model based on ROMS was developed to simulate the seasonal cycle and the regional distributions of phytoplankton biomass and primary production in Chesapeake Bay. Based on the model results, NO3 presents a strong contrast to the river nitrate load during spring and the highest concentration in the bay reaches around 80 mmol Nm-3 . Compared with the normal year, phytoplankton bloom in spring of 1996 appears in lower latitudes with a higher concentration. Quantitative comparison between the modeled and observed seasonal averaged dissolved inorganic nitrogen concentrations shows that the model produces reliable results. The correlation coefficient r2 for all quantities exceeds 0.95, and the skill parameter for the four seasons is all above 0.95.
基金Project(51276023) supported by the National Natural Science Foundation of ChinaProject(09k069) supported by the Open Project Funded by Universities Innovation Platform, Hunan Province, ChinaProject(2011GK311) supported by the Office of Science and Technology of Hunan Province, China
文摘Drum level sloshing is the latest discovery in the application of heat recovery steam generator (HRSG) in combined cycle, and shows certain negative influence on drum level controlling. In order to improve drum level controlling, influence factors on the drum level sloshing were investigated. Firstly, drum sub-modules were developed using the method of modularization modeling, and then the model of drum level sloshing was set up as well. Experiments were carried out on the experimental rig, and the model was validated using the obtained experimental results. Dynamic simulation was made based on the model to get a 3-D graph of drum level sloshing, which shows a vivid procedure of drum level sloshing. The effect of feed-water flow rate, main-steam flow rate and heating quantity on the drum level sloshing was analyzed. The simulation results indicate that the signals with frequency higher than 0.05 Hz are that of drum level sloshing, the signals with frequency of 0.0-0.05 Hz are that of drum level trendy and "false water level", and variation of the feed-water flow rates, main-steam flow rates and heating quantities can change the frequency of drum level sloshing, i.e., the frequency of sloshing increases with the increase of feed-water flow rate, or the decrease of the main-steam flow rate and the heating quantity. This research work is fundamental to improve signal-to-noise ratio of drum level signal and precise controlling of drum level.
文摘Several of the most celebrated examples of visual mimicry, like mimetic eggs laid by avian brood parasites and pala-table insects mimicking distasteful ones, involve signals directed at the eyes of birds. Despite this, studies of mimicry from the avian visual perspective have been rare, particularly with regard to defensive mimicry and masquerade. Defensive visual mimicry, which includes Batesian and Mtillerian mimicry, occurs when organisms share a visual signal that functions to deter predators. Masquerade occurs when an organism mimics an inedible or uninteresting object, such as a leaf, stick, or pebble. In this paper, I present five case studies covering diverse examples of defensive mimicry and masquerade as seen by birds. The best-known cases of defensive visual mimicry typically come from insect prey, but birds themselves can exhibit defensive visual mimicry in an at- tempt to escape mobbing or dissuade avian predators. Using examples of defensive visual mimicry by both insects and birds, I show how quantitative models of avian color, luminance, and pattern vision can be used to enhance our understanding of mimicry in many systems and produce new hypotheses about the evolution and diversity of signals. Overall, I investigate examples of Batesian mimicry （1 and 2）, Miillerian mimicry （3 and 4）, and masquerade （5） as follows： 1） Polymorphic mimicry in African mocker swallowtail butterflies; 2） Cuckoos mimicking sparrowhawks; 3） Mimicry rings in Neotropical butterflies; 4） Plumage mimicry in toxic pitohuis; and 5） Dead leaf-mimicking butterflies and mantids.
