Provenance history of a Late Triassic-Jurassic Gondwana margin forearc basin, Murihiku Terrane, North Island, New Zealand: petrographic and geochemical constraints

The Murihiku Terrane in the North Island was a forearc basin adjacent to a volcanic arc along the eastern margin of Gondwana during the Mesozoic. The rocks that infill the basin are mainly volcaniclastic sandstones and mudstones, often turbiditic, with sparse shellbeds, rhyolitic tuffs, carbonaceous sandstones, plant beds, concretionary horizons, and rare thick granitoid-rich conglomerates. Petrographic studies of the rock fragments in the sandstones show that andesites are the dominant lithic type, but there is a wide range of other lithologies, including dacites, rhyolites, ignimbrites, granitoids, quartzofeldspathic mica schists, rare amphibolites, and reworked mudstones and sandstones. The sandstones are texturally and mineralogically immature and suggest deposition relatively close to a source of high relief, undergoing physical rather than chemical weathering in cool- to cold-temperate conditions. Geochemical analyses of 67 whole-rock volcaniclastic sandstones and siltstones indicate that they were derived from an active and dissected volcanic arc in a convergent margin setting built upon relatively thin continental crust. Modal petrographic data and whole-rock geochemistry both confirm that there were systematic variations with time in the composition of clastic material being supplied to the basin. From the Late Triassic to Middle Jurassic, there was a decrease in silicic volcanic material, plutonics, and metamorphics, and an increase in the supply of andesitic detritus. This was followed in the Late Jurassic by a broader range of volcanic detritus, varying from basaltic andesite to rhyolite, which may have been caused by progressive extension of the volcanic arc and thinning of the crust, a precursor to the breakup of Gondwana in the Early-Middle Cretaceous. Comparison with the Southland segment of the Murihiku Terrane in the South Island suggests that there were significant along-arc source variations, with relatively less silicic but greater andesitic and continental crust contributions in the North Island than in Southland. This may be analogous to the modern Taupo-Kermadec arc where there is a south-north along-arc transition from a continental to an oceanic arc

Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches

Abstract—Semantics-preserving dimensionality reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition, and signal processing. This has found successful application in tasks that involve data sets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and Web content classification. One of the many successful applications of rough set theory has been to this feature selection area. This paper reviews those techniques that preserve the underlying semantics of the data, using crisp and fuzzy rough set-based methodologies. Several approaches to feature selection based on rough set theory are experimentally compared. Additionally, a new area in feature selection, feature grouping, is highlighted and a rough set-based feature grouping technique is detailed. Index Terms—Dimensionality reduction, feature selection, feature transformation, rough selection, fuzzy-rough selection.

The mechanical response of graphite

A theory of rate Independent material response is developed In conjunction with a functional Integral approximation. The linear term of this approximation Is used to represent the mechanical behavior of reactor grade polycrystalline graphite. The constitutive relation ls then applied to the case of one dimensional cyclic straining. A general three dimensional rate independent constitutive theory for the mechanical behavior of graphite is also developed. This theory Is then analyzed with respect to the available cyclic loading data obtained from uniaxial experiments

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

Geology and metamorphic history of the Kanmantoo copper deposit, South Australia

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