Water quality data for Fall Creek,New York,USA: 1972–1995

The 33 086 ha mixed land use Fall Creek watershed in upstate New York is part of the Great Lakes drainage system. Results from more than 3500 water samples are available in a data set that compiles flow data and measurements of various water quality analytes collected between 1972 and 1995 in all seasons and under all flow regimes in Fall Creek and its tributaries. Data is freely accessible at https://ecommons.cornell.edu/handle/1813/8148 and includes measurements of suspended solids, pH, alkalinity, calcium, magnesium, potassium, sodium, chloride, nitrate nitrogen (NO3-N), sulphate sulphur (SO4-S), phosphorus (P) fractions molybdate reactive P (MRP) and total dissolved P (TDP), percent P in sediment, and ammonium nitrogen (NH4-N). Methods, sub-watershed areas, and coordinates for sampling sites are also included. The work represented in this data set has made important scientific contributions to understanding of hydrological and biogeochemical processes that influence loading in mixed use watersheds and that have an impact on algal productivity in receiving water bodies. In addition, the work has been foundational for important regulatory and management decisions in the region.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

Canonical theory of perturbations using eccentric anomaly as independent variable

It is shown in this paper how to build a canonical transformation of variables, so that the eccentric anomaly becomes the new independent variable. In the case of eccentric elliptical orbits it changes the equations of motion so, that they can be integrated analytically to any order of approximation comparatively easy.     

中国古生教授学会微体古生物学分会举行代表大会和学术年会

中国古生教授学会微体古生物学分会举行代表大会和学术年会中国古竽物学会微体古物学分会第五次会员代表大分暨第六次不术年会于１９９６年１月２４日到３０日在福州市召开。来自全国地质、石油、煤炭的和产、科研和大学的１００多位代表参加了这次大会和学术研讨。大会共...     

Pressure-induced incipient amorphization of α-quartz and transition to coesite in an eclogite from Antarctica: a first record and some consequences

Monocrystalline quartz inclusions in garnet and omphacite from various eclogite samples from the Lanterman Range (Northern Victoria Land, Antarctica) have been investigated by cathodoluminescence (CL), Raman spectroscopy and imaging, and in situ X‐ray (XR) microdiffraction using the synchrotron. A few inclusions, with a clear‐to‐opalescent lustre, show ‘anomalous’ Raman spectra characterized by weak α‐quartz modes, the broadening of the main α‐quartz peak at 465 cm^?1, and additional vibrations at 480–485, 520–523 and 608 cm^?1. CL and Raman imaging indicate that this ‘anomalous’α‐quartz occurs as relicts within ordinary α‐quartz, and that it was preserved in the internal parts of small quartz inclusions. XR diffraction circular patterns display irregular and broad α‐quartz spots, some of which show an anomalous d‐spacing tightening of ～2%. They also show some very weak, hazy clouds that have d‐spacing compatible with coesite but not with α‐quartz. Raman spectrometry and XR microdiffraction characterize the anomalies with respect to α‐quartz as (i) a pressure‐induced disordering and incipient amorphization, mainly revealed by the 480–485 and 608‐cm^?1 Raman bands, together with (ii) a lattice densification, evidenced by d‐spacing tightening; (iii) the cryptic development of coesite, 520–523 cm^?1 being the main Raman peak of coesite and (iv) Brazil micro‐twinning. This ‘anomalous’α‐quartz represents the first example of pressure‐induced incipient amorphization of a metastable phase in a crustal rock. This issue is really surprising because pressure‐induced amorphization of metastable α‐quartz, observed in impactites and known to occur between 15 and 32 GPa during ultrahigh‐pressure (UHP) experiments at room temperature, is in principle irrelevant under normal geological P–T conditions. A shock (due to a seism?) or a local overpressure at the inclusion scale (due to expansion mismatch between quartz and its host mineral) seem the only geological mechanisms that can produce such incipient amorphization in crustal rocks. This discovery throws new light on the modality of the quartz‐coesite transition and on the pressure regimes (non‐lithostatic v. lithostatic) during high‐pressure/UHP metamorphism. In particular, incipient amorphization of quartz could favour the quartz‐coesite transition, or allow the growth of metastable coesite, as already experimentally observed.