中国对虾精子超低温保存的研究

于1991年11月-1993年5月对中国对虾纳精囊中精子进行超低温保存研究。结果表明，以自然海水或人工海水作基础液添加10％DMSO（V／V）及5％－10％的甘油配制成抗冻稀释液，稀释中国对虾精子进行超低温保存最有效，存活率在60％以上；保存94-138d，解冻后作人工授精，受精率最高达59％；稀释液中Mg2+，K+，Ca2+为超低温保存所必需，但Ca2+浓度过高则不利于精子的保存。光镜及电镜观察表明。冷冻对中国对虾精子的损伤主要表现为“棘突”的折断，严重时须体脱落仅剩精核；冻伤的精子不发生正常项体反应，不形成项体丝。     

Experimental evidence of habitat provision by aggregations of Riftia pachyptila at hydrothermal vents on the East Pacific Rise

Habitat created or modified by the physical architecture of large or spatially dominant species plays an important role in structuring communities in a variety of terrestrial, aquatic, and marine habitats. At hydrothermal vents, the giant tubeworm Riftia pachyptila forms large and dense aggregations in a spatially and temporally variable environment. The density and diversity of smaller invertebrates is higher in association with aggregations of R. pachyptila than on the surrounding basalt rock seafloor. Artificial substrata designed to mimic R. pachyptila aggregations were deployed along a gradient of productivity to test the hypothesis that high local species diversity is maintained by the provision of complex physical structure in areas of diffuse hydrothermal flow. After 1 year, species assemblages were compared among artificial aggregations in low‐, intermediate‐, and high‐productivity zones and compared to natural aggregations of R. pachyptila from the same site. Hydrothermal vent fauna colonized every artificial aggregation, and both epifaunal density and species richness were highest in areas of high chemosynthetic primary production. The species richness was also similar between natural aggregations of R. pachyptila and artificial aggregations in intermediate‐ and high‐productivity zones, suggesting that complex physical structure alone can support local species diversity in areas of chemosynthetic primary production. Differences in the community composition between natural and artificial aggregations reflect the variability in microhabitat conditions and biological interactions associated with hydrothermal fluid flux at low‐temperature hydrothermal vents. Moreover, these local ecological factors may further contribute to the maintenance of regional species diversity in hydrothermal vent communities on the East Pacific Rise.     

An application of Boussinesq equations to Bragg reflection of irregular waves

A numerical model based on the second-order fully nonlinear Boussinesq equations of Wei et al. [1995. Journal of Waterway, Port, Coastal and Ocean Engineering 121 (5), 251-263] is developed to simulate the Bragg reflection of both regular and irregular surface waves scattered by submerged bars. Particularly for incident regular waves, the computed results are observed to agree very well with the existing experimental data as presented by Davies and Heathershaw [1984. Journal of Fluid Mechanics 144, 419-446] and Kirby and Anton [1990. Proceedings of the 22nd International Conference on Coastal Engineering, ASCE, New York, pp. 757–768). In the case of incident irregular waves, the simulated results reveal that the distribution of Bragg reflection from irregular waves becomes more flat than that of regular waves. Due to lack of experimental data, the numerical results for incident irregular waves are compared with those of the evolution equation of the mild-slope equation [Hsu et al., 2002 Proceedings of the 24th Ocean Engineering Conference in Taiwan, pp. 70–77 (in Chinese)]. In addition, several parameters such as the number of bars, the relative height of bars and the spacing of bars affecting Bragg reflection are also discussed.     

磁化水在栉孔扇贝人工育苗上的应用研究

本实验以栉孔扇贝为材料，对栉孔扇贝亲贝、幼虫，进行０．０５～０．９Ｔ不同磁场强度的磁化水培育试验．实验结果表明：磁化水对亲贝蓄养有明显促进性腺发育成熟的作用；磁化水组性腺指数比对照组提高３．５×１０－２．孵化率也大大提高，比对照组提高１６×１０－２．对扇贝幼虫生长发育的影响，在０．３～０．７Ｔ的磁化水范围内，幼虫生长较快，成活率最高，其中０．６Ｔ最好。然后又进行０．６Ｔ生产性应用试验，通过３ａ应用，磁化水组比对照组幼虫成活率可提高１８×１０－２，附着变态率比对照组提高２７×１０－２，单位水体出苗量比对照组提高５０×１０－２左右。另外，磁化水也有明显改善育苗木质的作用，从而保证了扇贝育苗稳产高产。     

小竹山岛人工鱼礁区大型底栖动物群落结构的初步研究

为探究小竹山岛海域投放人工鱼礁后大型底栖动物群落结构的变化, 于2017—2020年进行了8个航次的调查, 研究鱼礁区、邻近区与对照区的大型底栖动物种类组成、优势种、生物多样性、群落结构及群落稳定性。结果显示, 共鉴定出大型底栖动物72种, 其中多毛类45种, 甲壳类16种, 软体动物8种, 棘皮动物3种, 投礁后第4年(2020年)比投礁后第1年(2017年)种类数增加了15种, 并且鱼礁区的种类数多于邻近区与对照区。优势种组成以多毛类为主。调查海域大型底栖动物生物量和丰度呈逐年上升趋势, 年份间差异显著, 2017—2020年年均生物量从2.11 g/m²上升到4.38 g/m², 年均丰度从289.31 ind./m²上升到764.14 ind./m², 区域间无显著差异。多样性指数、丰富度指数和均匀度指数在不同年份和区域之间变化不显著。聚类分析结果显示, 大型底栖动物群落结构分布格局呈现年际变化; 双因素相似性分析结果表明, 调查海域大型底栖动物群落结构的年份差异显著(P<0.01), 区域间无显著差异(P>0.05); 相似性百分比分析结果表明, 深沟毛虫(Sigambra bassi)、长叶索沙蚕(Lumbrineris longiforlia)、中蚓虫(Mediomastus californiensis)和寡鳃齿吻沙蚕(Micronephthys oligobranchia)等是造成群落年际差异的主要贡献种。丰度/生物量曲线结果表明, 2017—2020年大型底栖动物群落均未受到扰动, 随着鱼礁建设时间的推移, 大型底栖动物群落更趋稳定。     

Forecasting gob gas venthole production performances using intelligent computing methods for optimum methane control in longwall coal mines

Gob gas ventholes (GGV) are used to control methane inflows into a longwall operation by capturing it within the overlying fractured strata before it enters the work environment. Thus, it is important to understand the effects of various factors, such as drilling parameters, location of borehole, applied vacuum by exhausters and mining/panel parameters in order to be able to evaluate the performance of GGVs and to predict their effectiveness in controlling methane emissions. However, a practical model for this purpose currently does not exist.In this paper, we analyzed the total gas flow rates and methane percentages from 10 GGVs located on three adjacent panels operated in Pittsburgh coalbed in Southwestern Pennsylvania section of Northern Appalachian basin. The ventholes were drilled from different surface elevations and were located at varying distances from the start-up ends of the panels and from the tailgate entries. Exhauster pressures, casing diameters, location of longwall face and mining rates and production data were also recorded. These data were incorporated into a multilayer-perceptron (MLP) type artificial neural network (ANN) to model venthole production. The results showed that the two-hidden layer model predicted total production and the methane content of the GGVs with more than 90% accuracy. The ANN model was further used to conduct sensitivity analyses about the mean of the input variables to determine the effect of each input variable on the predicted production performance of GGVs.     

Parameter identification method for determination of elastic modulus of rock based on adjoint equation and blasting wave measurements

The purpose of this paper is to present a parameter identification method to determine the force of a blast and the elastic modulus of the ground using the measurements of a dynamic elastic wave, the adjoint equation method of optimal control theory, and the finite element method. Before the excavation of rocky ground, it is important to estimate the ground properties. In this paper, the elastic modulus is determined as the performance function is minimized using a technique based on the first‐order adjoint method. The performance function is a square sum of the discrepancies between the computed and the observed values of the velocities. After the determination of the magnitude of the blasting force, we can determine the elastic modulus of the rock. As the basic equation to calculate the velocities of dynamic elastic body, elastic equilibrium equations with linear viscosity are employed. The adjoint equation method has been utilized in order to calculate the gradient of the performance function with respect to the parameters. The gradient of the performance function is calculated using the first‐order adjoint equation. The weighted gradient method is applied for minimization. In order to solve the state equations in space and time, the finite element method and the Newmark $\frac{1}{4}$ method are used. In this paper, we tested the practical application of our proposed method for determination of the elastic modulus of rock at the Ikawa tunnel located in the Tokushima prefecture, Japan. Copyright © 2009 John Wiley & Sons, Ltd.     

南北地震带地震活动强度变化特征及其神经网络模型

南北地震带是中国大陆的一条主要活动地震带, 20世纪以来有很多大地震集中发生在这条地震带上。为了进一步探讨南北地震带地震活动强度变化的规律并对其进行中期预测,在研究了南北地震带逐年最大地震强度演化特征及其机理的基础上,建立了南北地震带地震强度序列变化的一种模式,建模中采用了人工神经网络技术,并提出了一种简易实用的能够获得较隹预测效果的确定神经网络输入窗口大小的方法。结果表明: 南北地震带的地震活动具有强弱分期轮回的特征; 用人工神经网络建模的预测结果与实际资料的对比检验中误差较小,因而该模型可作为南北地震带地震活动强度变化的预测模型。     

Mapping of hard rock aquifer system and artificial recharge zonation through remote sensing and GIS approach in parts of Perambalur District of Tamil Nadu,India

Mapping of hard rock aquifer system and artificial recharge zonation were carried out in an area of 325 km^2 in parts of the Perambalur District,Tamil Nadu,India.This district has been declared as one of the over-exploited regions in Tamil Nadu by the Central Groundwater Board.To raise the groundwater level,suitable recharge zones were identified and artificial recharge structures are suggested using geomatics technology in the present study.To this end,various thematic maps concerning lithology,soil,geomorphology,land use,land cover,slope,lineament,lineament density,drainage,drainage density and groundwater depth level were prepared.Fissile hornblende gneiss(244 km^2)covered most of the study area followed by charnockites(68 km^2).Structural hills and rocky pediments characterize the major geomorphological features in the targeted area,and are followed by deep moderated pediments.The area is mostly used as crop and fallow land,followed by scrub land and deciduous forest.In the study area,the slopes are predominantly very gentle(142 km^2)and nearly level(66 km^2)ones.Besides,Groundwater level data of 58 wells have been generated,in which the minimum and maximum depth were 3 and 28 m respectively.Integration under the GIS environment has been carried out using all the thematic layers to identify the groundwater prospect zone through the introduction of weight and rank methods.Integrated output performances were classified into very poor,poor,moderate,good and excellent categories.All these classes were further divided into two groups as suitable and non-suitable area for the selection of recharge sites.Hard rock fractures were mapped as lineaments from satellite images,and besides that,rose diagram was also generated to find out the trend of the fracture.Furthermore,fracture data of 146 numbers have been collected using Brunton compass to generate rose diagram and were correlated with the rose diagram derived from lineaments.The present study significantly brought up a few areas such as Ammapalayam,Melapuliyur,Senjeri and around Siruvachur for artificial recharge.     

Artificial neural networks and cluster analysis in landslide susceptibility zonation

A landslide susceptibility analysis is performed by means of Artificial Neural Network (ANN) and Cluster Analysis (CA). This kind of analysis is aimed at using ANNs to model the complex non linear relationships between mass movements and conditioning factors for susceptibility zonation, in order to identify unstable areas. The proposed method adopts CA to improve the selection of training, validation, and test records from data, managed within a Geographic Information System (GIS). In particular, we introduce a domain-specific distance measure in cluster formation. Clustering is used in data pre-processing to select non landslide records and is performed on the whole dataset, excluding the test set landslides. Susceptibility analysis is carried out by means of ANNs on the so-generated data and compared with the common strategy to select random non-landslide samples from pixels without landslides. The proposed method has been applied in the Brembilla Municipality, a landslide-prone area in the Southern Alps, Italy. The results show significant differences between the two sampling methods: the classification of the test set, previously separated and excluded from the training data, is always better when the non-landslide patterns are obtained using the proposed cluster sampling. The case study validates that, by means of a domain-specific distance measure in cluster formation, it is possible to introduce expert knowledge into the black-box modelling method, implemented by ANNs, to improve the predictive capability and the robustness of the models obtained.