THE FORESTRY RECLAMATION APPROACH: MEASURING SEDIMENT MASS ACCUMULATION RATES IN RECLAIMED MINE LANDS AND NATURALLY REGENERATED LOGGED FORESTS OF EASTERN KENTUCKY

The spread of surface coal mining has resulted in loss of forests in the Appalachian region. The Forestry Reclamation Approach (FRA) was developed to provide guidance for restoring forests on reclaimed mined land. This study hypothesizes that the FRA will result in larger magnitude of sediment accumulation rates in reclaimed mine sites compared to those reclaimed using grassland reclamation. Three sediment cores and six trenches were sampled within four reclaimed mined and three previously logged sites in eastern Kentucky. Samples were processed for radionuclides, grain-size, stable isotopes (δ13C), and POC. LIDAR data were used to identify valley fills, while historical aerial photography was used to identify changes in vegetative cover from 1994 to 2016. Radionuclide dating was used to determine sediment accumulation rates over the previous 100 years. Results from logged sites are inconclusive. δ13C data for all sites fall within the range expected for forested landscapes (C3), and do not show any transitions from grassland to forests. POC data indicates that inventories and fluxes were the same for mined and logged sites. Sediment accumulation rates for reclaimed mined lands show elevated values after the implementation of the FRA, compared to grassland reclamation, thus supporting the hypothesis for previously mined sites

Functional Reasoning and Functional Modelling

A car that will not start on a cold winter day and one that will not start on a hot summer day usually indicate two very different situations. When pressed to explain the difference, we would give a winter account- Oil is more viscous in cold conditions, and that causes . . .\u27\u27 -and a summer story- Vapor lock is a possibility in hot weather and is usually caused by . . .\u27\u27 How do we build such explanations? One possibility is that understanding how the car works as a device gives us a basis for generating the explanations. But that raises another question: how do people understand devices? Model-based reasoning is a subfield of artificial intelligence focusing on device understanding issues. In any model-based-reasoning approach, the goal is to model\u27\u27 a device in the world as a computer program. Unfortunately, model\u27\u27 is a loaded term-different listeners understand the word to mean very different concepts. By extrapolation, model-based reasoning\u27\u27 can suggest several different approaches, depending on the embedded meaning of model.\u27\u2

Functional Representation and Reasoning About the F/A-18 Aircraft Fuel System

Functional reasoning, a subfield of model-based reasoning, is discussed. This approach uses abstractions of a device\u27s purpose to index behaviors that achieve that purpose. Functional modeling, a variation on this method, also uses simulation as a core reasoning strategy. The complex causal knowledge of a device along functional lines is decomposed, then a causal story of how the device will operate in a particular situation given stated boundary conditions is composed. The application of the functional approach to modeling the fuel system of a F/A-18 aircraft is described. The representation of the F/A-18 fuel system includes 89 component devices, 92 functions, 118 behaviors, and 181 state variables

SeMi-Supervised Adaptive Resonance Theory (SMART2)

Adaptive resonance theory (ART) algorithms represent a class of neural network architectures which self-organize stable recognition categories in response to arbitrary sequences of input patterns. The authors discuss incorporation of supervision into one of these architectures, ART2. Results of numerical experiments indicate that this new semi-supervised version of ART2 (SMART2) outperformed ART for classification problems. The results and analysis of runs on several data sets by SMART2, ART2, and backpropagation are analyzed. The test accuracy of SMART2 was similar to that of backpropagation. However, SMART2 network structures are easier to interpret than the corresponding structures produced by backpropagation

Using Neural Networks for Aerodynamic Parameter Modeling

Neural networks are being developed at McDonnell Douglas Corporation to provide an onboard model of an aircraft\u27s aerodynamics to support advanced flight control systems. These flight control systems, constructed using neural networks and advanced controllers, have the potential to reduce flight control development costs and to improve inflight performance. Neural networks are useful in this situation because they can compactly represent the data and operate in real-tim

Determining Customary International Law Relative to the Conduct of Hostilities in Non-international Armed Conflicts

In 1987, the 6th annual American Red Cross-Washington College of Law Conference on International Humanitarian Law convened to discuss the 1977 Protocols Additional to the 1949 Geneva Conventions. This article outlines the proceedings of the various workshops, serving as a richly detailed scholarly source for a significant historical event

Immunocytochemical localization of small-conductance, calcium-dependent potassium channels in astrocytes of the rat supraoptic nucleus

Supraoptic nucleus (SON) neurons possess a prominent afterhyperpolarization (AHP) that contributes to spike patterning. This AHP is probably underlain by a small-conductance, CA2+-dependent, K+ type 3 (SK3) channel. To determine the distribution of SK3 channels within the SON, we used immunocytochemistry in rats and in transgenic mice with a regulatory cassette on the SK3 gene, allowing regulated expression with dietary doxycycline (DOX). In rats and wild-type mice, SK3 immunostaining revealed an intense lacy network surrounding SON neurons, with weak staining in neuronal somata and dendrites. In untreated, conditional SK3 knockout mice, SK3 was overexpressed, but the pericellular pattern in the SON was similar to that of rats. DOX-treated transgenic mice exhibited no SK3 staining in the SON. Double staining for oxytocin or vasopressin neurons revealed weak co-localization with SK3 but strong staining surrounding each neuron type. Electron microscopy showed that SK3-like immunoreactivity was intense between neuronal somata and dendrites, in apparent glial processes, but weak in neurons. This was confirmed by using confocal microscopy and double staining for glial fibrillary acidic protein (GFAP) and SK3: many GFAP-positive processes in the SON, and in the ventral dendritic/glial lamina, were shown to contain SK3-like immunoreactivity. These studies suggest a prominent role of SK3 channels in astrocytes. Given the marked plasticity in glial/neuronal relationships, as well as studies suggesting that astrocytes in the central nervous system can generate prominent CA2+ transients to various stimuli, a CA2+-dependent K+ channel may help SON astrocytes with K+ buffering whenever astrocyte intracellular CA2+ is increased. © 2005 Wiley-Liss, Inc

Pattern Recognition for Nondestructive Evaluation

The issues involved in automating nondestructive evaluation (NDE) techniques are outlined. Attention is given to research focused on the application of machine learning techniques to the construction and maintenance of knowledge-based systems which are capable of evaluating the readings from nondestructive tests that have been performed on aircraft components. Preliminary results obtained from this research are described. In particular, the authors discuss the application of a symbolic machine learning algorithm, ID3, to the NDE problem. ID3 has been used by Douglas Aircraft to classify defects in sets of standard NDE reference blocks. Based on the preliminary results, a need for an improved method of distinguishing features in the test waveforms is identified. The authors also outline a feature extraction approach from pattern recognition, called scale-space filtering, which can be used to preprocess data for input into a classification algorithm such as ID3