Study of onboard expert systems to augment space shuttle and space station autonomy

The feasibility of onboard crew activity planning was examined. The use of expert systems technology to aid crewmembers in locating stowed equipment was also investigated. The crew activity planning problem, along with a summary of past and current research efforts, was discussed in detail. The requirements and specifications used to develop the crew activity planning system was also defined. The guidelines used to create, develop, and operate the MFIVE Crew Scheduler and Logistics Clerk were discussed. Also discussed is the mathematical algorithm, used by the MFIVE Scheduler, which was developed to aid in optimal crew activity planning

Some Notes on the Past and Future of Lisp-Stat

Lisp-Stat was originally developed as a framework for experimenting with dynamic graphics in statistics. To support this use, it evolved into a platform for more general statistical computing. The choice of the Lisp language as the basis of the system was in part coincidence and in part a very deliberate decision. This paper describes the background behind the choice of Lisp, as well as the advantages and disadvantages of this choice. The paper then discusses some lessons that can be drawn from experience with Lisp-Stat and with the R language to guide future development of Lisp-Stat, R, and similar systems.

21st Century City Temperature Analogs in the United States

Decades of research now show that the planet is slowly warming and that this trend will affect life on Earth over the long term. Water supply, weather patterns, and disease are examples of the many ways in which climate change will directly affect humans. Mitigation planning efforts will require new ways of thinking about, visualizing, and analyzing the massive amounts of forecast data now available from a multitude of climate models. Various temperature-forecast models and datasets exist to help analyze climate change effects. This project converted one of those into a spatial database, extracted yearly averages for a selected set of United States cities, and used them to create lists of which cities’ temperatures are forecast to be most analogous to which others at various forecast years. A web application built with ESRI’s ArcGIS Server and Flex API visually linked these analogs to compare and contrast disparate geographic locations in new ways. A disciplined use of accepted design practices will allow this example to be easily adapted and extended in future analyses

The role of malignant tissue on the thermal distribution of cancerous breast

The present work focuses on the integration of analytical and numerical strategies to investigate the thermal distribution of cancerous breasts. Coupled stationary bioheat transfer equations are considered for the glandular and heterogeneous tumor regions, which are characterized by different thermophysical properties. The cross-section of the cancerous breast is identified by a homogeneous glandular tissue that surrounds the heterogeneous tumor tissue, which is assumed to be a two-phase periodic composite with non-overlapping circular inclusions and a square lattice distribution, wherein the constituents exhibit isotropic thermal conductivity behavior. Asymptotic periodic homogenization method is used to find the effective properties in the heterogeneous region. The tissue effective thermal conductivities are computed analytically and then used in the homogenized model, which is solved numerically. Results are compared with appropriate experimental data reported in the literature. In particular, the tissue scale temperature profile agrees with experimental observations. Moreover, as a novelty result we find that the tumor volume fraction in the heterogeneous zone influences the breast surface temperature

fastnet: An R Package for Fast Simulation and Analysis of Large-Scale Social Networks

Traditional tools and software for social network analysis are seldom scalable and/or fast. This paper provides an overview of an R package called fastnet, a tool for scaling and speeding up the simulation and analysis of large-scale social networks. fastnet uses multi-core processing and sub-graph sampling algorithms to achieve the desired scale-up and speed-up. Simple examples, usages, and comparisons of scale-up and speed-up as compared to other R packages, i.e., igraph and statnet, are presented

Designing and testing a molecularly targeted glioblastoma theranostic: experimental and computational studies

With an extremely poor patient prognosis glioblastoma multiforme (GBM) is one of the most aggressive forms of brain tumor with a median patient survival of less than 15 months. While new diagnostic and therapeutic approaches continue to emerge, the progress to reduce the mortality associated with the disease is insufficient. Thus, developing new methods having the potential to overcome problems that limit effective imaging and therapeutic efficacy in GBM is still a critical need. The overall goal of this research was therefore to develop targeted glioblastoma theranostics capable of imaging disease progression and simultaneously killing cancer cells. To achieve this, the state of the art of liposome based cancer theranostics are reviewed in detail and potential glioblastoma biomarkers for theranostic delivery are identified by querying different databases and by reviewing the literature. Then tumor targeting liposomes loaded with Gd3N@C80 and doxorubicin (DXR) are developed and tested in vitro. Finally, the stability of these formulations in different physiological salt solutions is evaluated using computational techniques including area per lipid, lipid interdigitaion, carbon-deuterium order parameter, radial distribution of ions as well as steered molecular dynamic simulations. In conclusion the experimental and computational studies of this dissertation demonstrated that DXR and Gd3N@C80-OH loaded and lactoferrin & transferrin dual-tagged, PEGylated liposomes might be potential drug and imaging agent delivery systems for GBM treatment

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

Program on application of communications satellites to educational development. Computer-based instruction: A background paper on its status, cost/effectiveness and telecommunications requirements

Status, cost/effectiveness, and telecommunication/satellite requirements of computer-based instructio