Analysis of 2D seismic data over 4 500 km in length from the Madura Strait Basin in the East Java Sea reveals seismic reflection characteristics of reefs and associated sedimentary bodies, including asymmetrical or symmetrical dome reflections, slope progradational reflections, chaotic reflections and discontinuous strong reflections inside the reef, which onlap the flank of the reef. It is concluded that the developmental paleo-environment of most reefs is mainly conducive to shallow marine carbonate platform facies and platform margin facies, based on well core data, variations in seismic facies and strata thickness. The formation and evolution of all reefs are primarily influenced by the tectonic framework of the Madura Strait Basin. Platform margin reefs are principally controlled by two types of structures: one is a series of E-W trending Paleogene normal faults, and the other is an E-W trending Neogene inversion structures. In addition, wave actions, tidal currents and other ocean currents play an accelerated role in sorting, rounding and redeposition for the accumulation and evolution of reefs. Tertiary reefs in the MSB can be divided into four types: 1) an open platform coral reef of Late Oligocene to Early Miocene, 2) a platform margin coral reef controlled by normal faults in Late Oligocene to Early Miocene, 3) a platform margin Globigerina moundreef controlled by a “hidden” inversion structure in Early Pliocene, and 4) a platform margin Globigerina mound-reef controlled by thrust faults in the early Pliocene. Patterns of the formation and evolution of reefs are also suggested.
In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation. 相似文献
Nannochloropsis oculata CS179, a unicellular marine microalga, is rich in long-chain polyunsaturated fatty acids (LCPUFAs). Elongase and desaturase
play a key role in the biosynthesis of PUFAs. A new elongase gene, which encodes 322 amino acids, was identified via RT-PCR and 5′ and 3′ RACE. The sequence of the elongase gene was blast-searched in the NCBI GenBank and showed a similarity
to those of the cryptosporidium. But the NJ-tree revealed that the N. oculata CS179 elongase clustered with those of the microalgae Phaeodactylum tricornutum, Ostreococcus tauri and Thalassiosira pseudonana. 相似文献
