An Investigation of Learning Curves and Their Use in Simulation

In 1995, the C-17 Factory Simulation Model (FSM) was developed to enable analysts to address \u27what-if\u27 questions about the resources required to build future aircraft, and is based on learning curve models that are used to both portray and simulate future aircraft production. In this thesis, we examine and develop alternate learning curve models that also utilize a small amount of initial production data to portray the relationship between the number of aircraft built and the resources required to build them. The goal is to identify a model which not only provides a good fit and forecast based on a small amount of data but is also intuitive and reasonably simple to apply. We also propose and evaluate the use of Autoregressive Moving Average (ARMA) models for modeling the effects of learning. These models are exercised in fitting simulated log-linear data, as well as in fitting and forecasting historical F-102 manufacturing data and notional C-17 manufacturing data. The results are somewhat inconclusive since they do not identify any one model as the best. They do, however, suggest that ARMA models are a promising alternative to the standard log-linear learning curve. The thesis concludes with an examination of the effects of explicitly accounting for uncertainty in parameter estimation when simulating future performance based on the traditional log-linear learning curve model. The results show that the approach employed in the FSM is viable even though it does not directly account for this uncertainty

Multi-level mapping: Real-time dense monocular SLAM

We present a method for Simultaneous Localization and Mapping (SLAM) using a monocular camera that is capable of reconstructing dense 3D geometry online without the aid of a graphics processing unit (GPU). Our key contribution is a multi-resolution depth estimation and spatial smoothing process that exploits the correlation between low-texture image regions and simple planar structure to adaptively scale the complexity of the generated keyframe depthmaps to the texture of the input imagery. High-texture image regions are represented at higher resolutions to capture fine detail, while low-texture regions are represented at coarser resolutions for smooth surfaces. The computational savings enabled by this approach allow for significantly increased reconstruction density and quality when compared to the state-of-the-art. The increased depthmap density also improves tracking performance as more constraints can contribute to the pose estimation. A video of experimental results is available at http://groups.csail.mit.edu/rrg/multi_level_mapping.Charles Stark Draper Laboratory (Research Fellowship

Protein O-Mannosylation in the Murine Brain: Occurrence of Mono-O-Mannosyl Glycans and Identification of New Substrates

Protein O-mannosylation is a post-translational modification essential for correct development of mammals. In humans, deficient O-mannosylation results in severe congenital muscular dystrophies often associated with impaired brain and eye development. Although various O-mannosylated proteins have been identified in the recent years, the distribution of O-mannosyl glycans in the mammalian brain and target proteins are still not well defined. In the present study, rabbit monoclonal antibodies directed against the O-mannosylated peptide YAT(α1-Man)AV were generated. Detailed characterization of clone RKU-1-3-5 revealed that this monoclonal antibody recognizes O-linked mannose also in different peptide and protein contexts. Using this tool, we observed that mono-O-mannosyl glycans occur ubiquitously throughout the murine brain but are especially enriched at inhibitory GABAergic neurons and at the perineural nets. Using a mass spectrometry-based approach, we further identified glycoproteins from the murine brain that bear single O-mannose residues. Among the candidates identified are members of the cadherin and plexin superfamilies and the perineural net protein neurocan. In addition, we identified neurexin 3, a cell adhesion protein involved in synaptic plasticity, and inter-alpha-trypsin inhibitor 5, a protease inhibitor important in stabilizing the extracellular matrix, as new O-mannosylated glycoproteins

Refinement of Copper(II) Azide with 1‐Alkyl‐5H‐tetrazoles: Adaptable Energetic Complexes

A concept for stabilizing highly sensitive and explosive copper(II) azide with 1‐N‐substituted tetrazoles is described. It was possible to stabilize the system by the use of highly endothermic, nitrogen‐rich ligands. The sensitivities of the resulting energetic copper coordination compounds can be tuned further by variation of the alkyl chain of the ligands and by phlegmatization of the complexes with classical additives during the synthesis. It is demonstrated, using the compound based on 1‐methyl‐5H‐tetrazole ([Cu(N3)2(MTZ)], 1) that this class of complexes can be applied as a potential replacement for both lead azide (LA) and lead styphnate (LS). The complex was extensively investigated according to its chemical (elemental analysis, single‐crystal and powder X‐ray diffraction, IR spectroscopy, scanning electron microscopy) and physico‐chemical properties (differential thermal analysis, sensitivities towards impact, friction, and electrostatic discharge) compared to pure copper(II) azide

1-(Azidomethyl)-5H-Tetrazole: A Powerful New Ligand for Highly Energetic Coordination Compounds

Highly energetic 1-(azidomethyl)-5H-tetrazole (AzMT, 3) has been synthesized and characterized. This completes the series of 1-(azidoalkyl)-5H-tetrazoles represented by 1-(azidoethyl)-5H-tetrazole (AET) and 1-(azidopropyl)-5H-tetrazole (APT). AzMT was thoroughly analyzed by single-crystal X-ray diffraction experiments, elemental analysis, IR spectroscopy and multinuclear (H-1, C-13, N-14, N-15) NMR measurements. Several energetic coordination compounds (ECCs) of 3d metals (Mn, Fe, Cu, Zn) and silver in combination with anions such as (per)chlorate, mono- and dihydroxy-trinitrophenolate were prepared, giving insight into the coordination behavior of AzMT as a ligand. The synthesized ECCs were also analyzed by X-ray diffraction experiments, elemental analysis, and IR spectroscopy. Differential thermal analysis for all compounds was conducted, and the sensitivity towards external stimuli (impact, friction, and ESD) was measured. Due to the high enthalpy of formation of AzMT (+654.5 kJ mol(-1)), some of the resulting coordination compounds are extremely sensitive, yet are able to undergo deflagration-to-detonation transition (DDT) and initiate pentaerythritol tetranitrate (PETN). Therefore, they are to be ranked as primary explosives

Salts of Picramic Acid – Nearly Forgotten Temperature‐Resistant Energetic Materials

Thermally stable explosives are becoming more and more important nowadays due to their important role in the oil and mining industry. The requirements of these explosives are constantly changing. Picramate‐based compounds are poorly investigated towards their energetic properties as well as sensitivities. In this work, 13 different salts of picramic acid were synthesized as potential energetic materials with high thermal stability in a simple one‐step reaction and compared with commercially used lead picramate. The obtained compounds were extensively characterized by e. g. XRD, IR, EA, DTA, and TGA. In addition, the sensitivities towards impact and friction were determined with the BAM drop hammer and the BAM friction tester. Also, the electrostatic discharge sensitivity was explored. Calculations of the energetic performance of selected compounds were carried out with the current version of EXPLO5 code. Therefore, heats of formation were computed and X‐ray densities were converted to room temperature. Some of the synthesized salts show promising characteristics with high exothermic decomposition temperatures. Especially, the water‐free rubidium, cesium, and barium salts 5 , 6 and 10 with decomposition temperatures of almost 300 °C could be promising candidates for future applications

Emergence of qualia from brain activity or from an interaction of proto-consciousness with the brain: which one is the weirder? Available evidence and a research agenda

This contribution to the science of consciousness aims at comparing how two different theories can explain the emergence of different qualia experiences, meta-awareness, meta-cognition, the placebo effect, out-of-body experiences, cognitive therapy and meditation-induced brain changes, etc. The first theory postulates that qualia experiences derive from specific neural patterns, the second one, that qualia experiences derive from the interaction of a proto-consciousness with the brain\u2019s neural activity. From this comparison it will be possible to judge which one seems to better explain the different qualia experiences and to offer a more promising research agenda