Nest survival rate of Reeves＇s pheasant(Syrmaticus reevesii) based on artificial nest experiments

To explore the nest survival rate of Reeves＇ s pheasant（Syrmaticus reevesii） and the nest-site factors that affect it, we conducted artificial nest experiments with reference to natural nests at Dongzhai National Nature Reserve（DNNR）, Henan Province and Pingjingguan, Hubei Province from April to June 2014 simulating the situation in its early and later breeding season. We also determined distance characteristics of the nest sites by Arc GIS 10.0. Nest survival models were constructed in Program MARK for data analysis. Results indicated that in the early breeding season, the apparent survival rate（ASR） in DNNR（52.4%） was significantly greater than that in Pingjingguan（13.5%）, and the ASR in the later breeding season in DNNR（26.7%） was not indistinctively correlated with Pingjingguan（3.2%）. The daily survival rate（DSR） in the later breeding season was 93.8% in DNNR and 92.0% in Pingjingguan, respectively. The DSRs were both negatively correlated with nest distance to forest edges and settlements. The DSR in Pingjingguan was positively correlated with nest distance to paths and negatively correlated with nest distance to water sources. However, the DSR in DNNR was negatively correlated with nest distance to paths but positively correlated with nest distance to water sources.

Radiation of lamp and optimized experiment using artificial light in the Arctic Ocean

A winter optical experiment by an artificial lamp was conducted in the Amundsen Bay of Arctic Ocean from November of 2007 to January of 2008. The radiation field emitted from an artificial lamp was measured and is introduced in this paper, and the optimized experiment project is discussed. It is demonstrated that the minimum size allowed of the lamp is determined by both the field of view （ FOV ） of optical instrument and the measuring distance from the lamp. Some problems that might influence on the experiment result often occur for a simple fluorescent lamp, such as instability, spatial nonuniformity, light divergence, effect of lamp temperature, etc. By the analysis of the light radiation, three kind of measures are proposed to control the quality of the experiment, i.e. keeping consistency of lamp size with FOV of instrument, calibrating in situ downwind, and conducting measurement in effective range. Among them, the downwind calibration is the key step to overcome most problems arose by the lamp. The experiment indicated that the reliable results can be obtained only when the optical measurement is coordinated with the radiation field of artificial lamp. The measured radiation property of the lamp was used to advise the field experiment to minimize measuring error. As the experiment by artificial lamp was the first attempt in the Arctic Ocean, the experience given by this paper is a valuable reference to the correlative studies.

Model Analysis of Radar Echo Split Observed in an Artificial Cloud Seeding Experiment

An artificial cloud seeding experiment was performed over the Japan Sea in winter to show how massive seeding could be effective to mitigate heavy snowfall damage.The results showed that 20 min after cloud seeding,a portion of the radar echo beneath the seeding track was weakened to divide the radar echo into two parts.In order to analyze the results,a numerical simulation was conducted by using the Weather Research and Forecasting model verion 3.5.1.In this simulation,the seeding effects were represented as phenomena capable of changing rain particles by accreting cloud ice and snow to form graupel particles and by changing cloud liquid water to snow particles.The graupel particles fell rapidly,thus temporarily intensifying the rainfall,which subsequently decreased.Therefore,the weakened radar echo in the field experiment is deemed to have been caused by the increase in rapidly falling graupel particles.

Modeling, analysis, and screening of cyclic pressure pulsing with nitrogen in hydraulically fractured wells

Cyclic pressure pulsing with nitrogen is studied for hydraulically fractured wells in depleted reservoirs.A compositional simulation model is constructed to represent the hydraulic fractures through local-grid refinement.The process is analyzed from both operational and reservoir/hydraulic-fracture perspectives.Key sensitivity parameters for the operational component are chosen as the injection rate,lengths of injection and soaking periods and the economic rate limit to shut-in the well.For the reservoir/hydraulic fracturing components,reservoir permeability,hydraulic fracture permeability,effective thickness and half-length are used.These parameters are varied at five levels.A full-factorial experimental design is utilized to run 1250 cases.The study shows that within the ranges studied,the gas-injection process is applied successfully for a 20-year project period with net present values based on the incremental recoveries greater than zero.It is observed that the cycle rate limit,injection and soaking periods must be optimized to maximize the efficiency.The simulation results are used to develop a neural network based proxy model that can be used as a screening tool for the process.The proxy model is validated with blind-cases with a correlation coefficient of 0.96.

Thermo-mechanical coupling analysis of APSE using submodels and neural networks

The ,Aspoe Pillar Stability Experiment （APSE） is an in situ experiment for investigating the spalling mechanism under mechanical and thermal loading conditions in a crystalline rock. In this study, the thermo-mechanical behaviors in the APSE were investigated with three models： （1） a Full model with rough meshes for calculating the influence of tunnel excavation; （2） a Submodel with fine meshes for predicting the thermo-mechanical behavior in the pillar during the borehole drilling, heating, and cool- ing phases; and （3） a Thin model for modeling the effect of slot cutting for de-stressing around the pillar. In order to import the stresses calculated from the Full model to the Submodel and to define the complex thermal boundary conditions, artificial neural networks （NNs） were utilized. From this study, it was pos- sible to conclude that the stepwise approach with the application of NNs was useful for predicting the complex response of the pillar under severe thermo-mechanical loading conditions.