Management: A bibliography for NASA managers

This bibliography lists 731 reports, articles and other documents introduced into the NASA Scientific and Technical Information System in 1990. Items are selected and grouped according to their usefulness to the manager as manager. Citations are grouped into ten subject categories: human factors and personnel issues; management theory and techniques; industrial management and manufacturing; robotics and expert systems; computers and information management; research and development; economics, costs and markets; logistics and operations management; reliability and quality control; and legality, legislation, and policy

Renewable Energy

Renewable Energy is energy generated from natural resources - such as sunlight, wind, rain, tides and geothermal heat - which are naturally replenished. In 2008, about 18% of global final energy consumption came from renewables, with 13% coming from traditional biomass, such as wood burning. Hydroelectricity was the next largest renewable source, providing 3% (15% of global electricity generation), followed by solar hot water/heating, which contributed with 1.3%. Modern technologies, such as geothermal energy, wind power, solar power, and ocean energy together provided some 0.8% of final energy consumption. The book provides a forum for dissemination and exchange of up - to - date scientific information on theoretical, generic and applied areas of knowledge. The topics deal with new devices and circuits for energy systems, photovoltaic and solar thermal, wind energy systems, tidal and wave energy, fuel cell systems, bio energy and geo-energy, sustainable energy resources and systems, energy storage systems, energy market management and economics, off-grid isolated energy systems, energy in transportation systems, energy resources for portable electronics, intelligent energy power transmission, distribution and inter - connectors, energy efficient utilization, environmental issues, energy harvesting, nanotechnology in energy, policy issues on renewable energy, building design, power electronics in energy conversion, new materials for energy resources, and RF and magnetic field energy devices

Bio-mimetic Spiking Neural Networks for unsupervised clustering of spatio-temporal data

Spiking neural networks aspire to mimic the brain more closely than traditional artificial neural networks. They are characterised by a spike-like activation function inspired by the shape of an action potential in biological neurons. Spiking networks remain a niche area of research, perform worse than the traditional artificial networks, and their real-world applications are limited. We hypothesised that neuroscience-inspired spiking neural networks with spike-timing-dependent plasticity demonstrate useful learning capabilities. Our objective was to identify features which play a vital role in information processing in the brain but are not commonly used in artificial networks, implement them in spiking networks without copying constraints that apply to living organisms, and to characterise their effect on data processing. The networks we created are not brain models; our approach can be labelled as artificial life. We performed a literature review and selected features such as local weight updates, neuronal sub-types, modularity, homeostasis and structural plasticity. We used the review as a guide for developing the consecutive iterations of the network, and eventually a whole evolutionary developmental system. We analysed the model’s performance on clustering of spatio-temporal data. Our results show that combining evolution and unsupervised learning leads to a faster convergence on the optimal solutions, better stability of fit solutions than each approach separately. The choice of fitness definition affects the network’s performance on fitness-related and unrelated tasks. We found that neuron type-specific weight homeostasis can be used to stabilise the networks, thus enabling longer training. We also demonstrated that networks with a rudimentary architecture can evolve developmental rules which improve their fitness. This interdisciplinary work provides contributions to three fields: it proposes novel artificial intelligence approaches, tests the possible role of the selected biological phenomena in information processing in the brain, and explores the evolution of learning in an artificial life system

Topical Workshop on Electronics for Particle Physics

The purpose of the workshop was to present results and original concepts for electronics research and development relevant to particle physics experiments as well as accelerator and beam instrumentation at future facilities; to review the status of electronics for the LHC experiments; to identify and encourage common efforts for the development of electronics; and to promote information exchange and collaboration in the relevant engineering and physics communities