Aspects of the upwelling regime between Cape Point and Cape Agulhas,South Africa     

《African Journal of Marine Science》2013,35(1):91-101

A progressive increase in mean ovary mass (standardized by the cube offish length) over time was observed in specified length classes of pilchard Sardinops ocellatus sampled from purse-seine catches off the west coast of South Africa. This, in earlier investigations, was considered to have been a density-dependent response following the collapse of the stock in the mid 1960s, and it appeared to result mainly from the increasing frequency of occurrence of pilchard entering the batch-spawning cycle, particularly among fish of 16–20 cm L_c . The possibility that the observed trend was an artifact of changing distribution of fishing or shifting seasonality of spawning relative to the three-month period of sample collection was rejected following analysis of variance on spatially disaggregated data and inspection of seasonal patterns in ovary mass. Other causes of the increase in mean ovary mass could have been density-dependence, declining age structure, changes in the environment and selection for early maturity under prolonged high rates of mortality. The data are critically examined to evaluate the likelihood of each of these explanations.  相似文献   

Intelligent prediction of the stress–strain response of intact and jointed rocks     

Arunakumari Garaga  Gali Madhavi Latha 《Computers and Geotechnics》2010

An application of Artificial Neural Networks for predicting the stress–strain response of jointed rocks under different confining pressures is presented in this paper. Rocks of different compressive strength with different joint properties (frequency, orientation and strength of joints) are considered in this study. The database for training the neural network is formed from the results of triaxial compression tests on different intact and jointed rocks with different joint properties tested at different confining pressures reported by various researchers in the literature. The network was trained using a three-layered network with the feed-forward back propagation algorithm. About 85% of the data was used for training and the remaining 15% was used for testing the network. Results from the analyses demonstrated that the neural network approach is effective in capturing the stress–strain behaviour of intact rocks and the complex stress–strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress–strain response of different jointed rocks, whose intact strength varies from 11.32 MPa to 123 MPa, spacing of joints varies from 10 cm to 100 cm, and confining pressures range from 0 to 13.8 MPa.  相似文献   

巨型山东龙的系统分类、生活习性与生态环境研究          下载免费PDF全文

季燕南 《地学前缘》2010,17(1):378

简要回顾了我国山东省诸城市吕标乡库沟村龙骨涧晚白垩世早期辛格庄组中产出的巨型山东龙的研究历史,并讨论了巨型山东龙与"巨大诸城龙"的系统分类关系。对比两者的产出状况、骨骼结构和个体发育特征,作者认为两者为同物异名。依据古生物命名法规的优先律,巨型山东龙命名在先,合法有效;"巨大诸城龙"命名较晚,是一无效名称,应予取消。还讨论了巨型山东龙的生活习性和生态环境,认为巨型山东龙属陆栖性、群居性、植食性恐龙,生活在气候温暖、雨水充沛、植被繁茂的低山或丘陵地区。此外研究了巨型山东龙赋存地层的沉积特征,认为是一套辫状河流相或冲积扇相沉积,而不是湖相沉积。恐龙化石的密集产出是非正常集群死亡于突发性灾害事件的结果,而与国际上的K/T事件无关。  相似文献   

Three-dimensional quantitative analysis on granular particle shape using convolutional neural network     

Pin Zhang  Zhen-Yu Yin  Yin-Fu Jin 《国际地质力学数值与分析法杂志》2022,46(1):187-204

To identify all desired shape parameters of granular particles with less computational cost, this study proposes a three-dimensional convolutional neural network (3D-CNN) based model. Datasets are made of 100 ballast and 100 Fujian sand particles, and the shape parameters (i.e., aspect ratio, roundness, sphericity, and convexity) obtained by conventional methods are used to label all particles. For the model training, by feeding the slice images of particles into the model, the contour of particles is automatically extracted, thereby the shape parameters can be learned by the model. Thereafter, the model is applied to predict shape parameters of new particles as model testing. All results indicate the model trained based on slice images cut from three orthogonal planes presents the highest prediction accuracy with an error of less than 10%. Meanwhile, the accuracy for concave and angular particles can be guaranteed. The rotation-equivariant of the model is confirmed, in which the predicted values of shape parameters are roughly independent of orientations of the particle when cutting slice images. Superior to conventional methods, all desirable shape parameters can be obtained by one unified 3D-CNN model and its prediction is independent of particle complexity and the number of triangular facets, thus saving computation cost.  相似文献