The impacts of international aid on the energy security of small island developing states (SIDS)

Tuvalu is a small island developing state (SID) with least developed country (LDC) status. The island has gained international attention due to the threat to its land territory as a result of climate change and subsequent sea-level rises. At the United Nations Climate Change Conference, held in Copenhagen in December 2009, Tuvalu was described as being at serious risk of becoming the first state to become uninhabitable due to the impacts of climate change. The majority of climate change scientists agree that a key driver of climate change is the burning of fossil fuels, predominantly for energy production. Energy security is multifaceted and connections can be drawn between the energy demands of the wealthier, industrialised states and the less developed states that are experiencing the detrimental impacts of the meeting of these demands. For Tuvalu, the lack of access to adequate, affordable, reliable, safe and environmentally benign energy is a severe development constraint. Currently, Tuvalu is close to being a totally oil dependent economy (83% of primary energy), whose energy security is dependent upon foreign aid to ensure its ability to pay international oil companies. Costs of all imported goods are exacerbated by its geographical isolation. This paper analyses the impact of international aid on energy security in Tuvalu and comments on the Tuvaluan Government’s commitment to 100% renewable energy – “being carbon neutral” – by 2020. Although this is a commendable aspiration it is clear that even if Tuvalu were to end reliance on fossil fuels it would still be at risk of disastrous inundation unless the industrialised states radically reduce their own dependency on such fuels and dramatically reduce the global levels of greenhouse gas emissions

Combining case based reasoning with neural networks

This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and CBR are instance-based learning techniques, although neural nets work with numerical data and CBR systems work with symbolic data. This paper discusses how the application scope of both paradigms could be enhanced by the use of hybrid concepts. To make the use of neural networks possible, the problem's situation and solution features are transformed into continuous features, using techniques similar to CBR's definition of similarity metrics. Radial Basis Function (RBF) neural nets are used to create a multivariable, continuous input-output mapping. As the mapping is continuous, this technique also provides generalisation between cases, replacing the domain specific solution adaptation techniques required by conventional CBR. This continuous representation also allows, as in fuzzy logic, an associated membership measure to be output with each symbolic feature, aiding the prioritisation of various possible solutions. A further advantage is that, as the RBF neurons are only active in a limited area of the input space, the solution can be accompanied by local estimates of accuracy, based on the sufficiency of the cases present in that area as well as the results measured during testing. We describe how the application of this technique could be of benefit to the real world problem of sales advisory systems, among others

Unconditional bases and strictly convex dual renormings

We present equivalent conditions for a space $X$ with an unconditional basis to admit an equivalent norm with a strictly convex dual norm

Combining case based reasoning with neural networks

This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and CBR are instance-based learning techniques, although neural nets work with numerical data and CBR systems work with symbolic data. This paper discusses how the application scope of both paradigms could be enhanced by the use of hybrid concepts. To make the use of neural networks possible, the problem's situation and solution features are transformed into continuous features, using techniques similar to CBR's definition of similarity metrics. Radial Basis Function (RBF) neural nets are used to create a multivariable, continuous input-output mapping. As the mapping is continuous, this technique also provides generalisation between cases, replacing the domain specific solution adaptation techniques required by conventional CBR. This continuous representation also allows, as in fuzzy logic, an associated membership measure to be output with each symbolic feature, aiding the prioritisation of various possible solutions. A further advantage is that, as the RBF neurons are only active in a limited area of the input space, the solution can be accompanied by local estimates of accuracy, based on the sufficiency of the cases present in that area as well as the results measured during testing. We describe how the application of this technique could be of benefit to the real world problem of sales advisory systems, among others

BodySpace: inferring body pose for natural control of a music player

We describe the BodySpace system, which uses inertial sensing and pattern recognition to allow the gestural control of a music player by placing the device at different parts of the body. We demonstrate a new approach to the segmentation and recognition of gestures for this kind of application and show how simulated physical model-based techniques can shape gestural interaction

Haptic dancing: human performance at haptic decoding with a vocabulary

The inspiration for this study is the observation that swing dancing involves coordination of actions between two humans that can be accomplished by pure haptic signaling. This study implements a leader-follower dance to be executed between a human and a PHANToM haptic device. The data demonstrates that the participants' understanding of the motion as a random sequence of known moves informs their following, making this vocabulary-based interaction fundamentally different from closed loop pursuit tracking. This robot leader does not respond to the follower's movement other than to display error from a nominal path. This work is the first step in an investigation of the successful haptic coordination between dancers, which will inform a subsequent design of a truly interactive robot leader

Using evidence to inform health policy: case study

No abstract available

Interpretive computer simulator for the NASA Standard Spacecraft Computer-2 (NSSC-2)

An Interpretive Computer Simulator (ICS) for the NASA Standard Spacecraft Computer-II (NSSC-II) was developed as a code verification and testing tool for the Annular Suspension and Pointing System (ASPS) project. The simulator is written in the higher level language PASCAL and implented on the CDC CYBER series computer system. It is supported by a metal assembler, a linkage loader for the NSSC-II, and a utility library to meet the application requirements. The architectural design of the NSSC-II is that of an IBM System/360 (S/360) and supports all but four instructions of the S/360 standard instruction set. The structural design of the ICS is described with emphasis on the design differences between it and the NSSC-II hardware. The program flow is diagrammed, with the function of each procedure being defined; the instruction implementation is discussed in broad terms; and the instruction timings used in the ICS are listed. An example of the steps required to process an assembly level language program on the ICS is included. The example illustrates the control cards necessary to assemble, load, and execute assembly language code; the sample program to to be executed; the executable load module produced by the loader; and the resulting output produced by the ICS

Severe storm initiation and development from satellite infrared imagery and Rawinsonde data

The geographical distribution of potential temperatures, mixing ratio, and streamlines of flow patterns at 850, 700, and 500 mb heights are used to understand the prestorm convection and the horizontal convergence of moisture. From the analysis of 21 tornadoes the following conclusions are reached: (1) Strong horizontal convergence of moisture appeared at the 850, 700, and 500 mb levels in the area 12 hours before the storm formation; (2) An abundantly moist atmosphere below 3 km (700 mb) becomes convectively unstable during the time period between 12 and 24 hours before the initiation of the severe storms; (3) Strong winds veering with height with direction parallel to the movement of a dryline, surface fronts, etc; (4) During a 36-hour period, a tropopause height in the areas of interest is lowest at the time of tornadic cloud formation; (5) A train of gravity waves is detected before and during the cloud formation period. Rapid-scan infrared imagery provides near real-time information on the life cycle of the storm which can be summarized as follows: (1) Enhanced convection produced an overshooting cloud top penetrating above the tropopause, making the mass density of the overshooting cloud much greater than the mass density of the surrounding air; (2) The overshooting cloud top collapsed at the end of the mature stage of the cloud development; (3) The tornado touchdown followed the collapse of the overshooting cloud top