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Effects of lanthanum (La) loading on the structural, optical, and electrical properties of tin monoxide (SnO) films were examined as a p-type semiconducting layer. La loading up to 1.9 atom % caused the texturing of the tetragonal SnO phase with a preferential orientation of (101), which was accompanied by the smoother surface morphology. Simultaneously, the incorporated La cation suppressed the formation of n-type SnO2 in the La-doped SnO film and widened its optical band gap. These variations allowed the 1.9 atom % La-loaded SnO film to have a high hole mobility and carrier density, compared with the La-free control SnO film. The superior semiconducting property was reflected in the p-type thin-film transistor (TFT). The control SnO TFTs exhibited the field-effect mobility (μSAT) and I ON/OFF ratio of 0.29 cm2 V–1 s–1 and 5.4 × 102, respectively. Enhancement in the μSAT value and I ON/OFF ratio was observed for the TFTs with the 1.9 atom % La-loaded SnO channel layer: they were improved to 1.2 cm2 V–1 s–1 and 7.3 × 103, respectively. The reason for this superior performance was discussed on the basis of smoother morphology, suppression of disproportionation conversion from Sn2+ to Sn + Sn4+, and reduced gap-state density.

Results from experiments performed to characterize plastic capsules containing foam layers are presented. A compact X-pinch pulser with a footprint <1m2 having a peak current of 80kA and a rise time of 50ns was used. Various wire materials including tungsten, molybdenum, and aluminum were employed. Results with plastic capsules (1mm diameter, 20μm thick wall with 80μm foam inside the capsule) show phase contrast effects at the edges of the wall due to the foam, which mimics the ice inside the shell. The sharpness of the image reveals a source less than 2μm in size and x-ray diodes show a pulse length of ∼10ns. The small source size allows high-resolution phase contrast imaging of capsules. The x-ray pulse from an X-pinch is sufficiently short to avoid the motional blurring due to cryogenic system vibrations, which is not possible with low flux sources.

The current capsule target design for the first ignition experiments at the NIF Facility beginning in 2009 will be a copper-doped beryllium capsule, roughly 2 mm in diameter with 160-µm walls. The capsule will have a 75-µm layer of solid DT on the inside surface, and the capsule will driven with x-rays generated from a gold/uranium cocktail hohlraum. The design specifications are extremely rigorous, particularly with respect to interfaces, which must be very smooth to inhibit Rayleigh-Taylor instability growth. This paper outlines the current design, and focuses on the challenges and advances in capsule fabrication and characterization; hohlraum fabrication, and D-T layering and characterization.2

Network traffic describes the characteristics and users' behaviors of communication networks. It is a crucial input parameter of network management and network traffic engineering. This paper proposes a new prediction algorithm to network traffic in the large-scale communication network. First, we use signal analysis theory to transform network traffic from time domain to timefrequency domain. In the time-frequency domain, the network traffic signal is decomposed into the low-frequency and high-frequency components. Second, the gray model is exploited to model the low-frequency component of network traffic. The white Gaussian noise model is utilized to describe its high-frequency component. This is reasonable because the low-frequency and highfrequency components, respectively, represent the trend and fluctuation properties of network traffic, while the gray model and white Gaussian noise model can well capture the characteristics. Third, the prediction models of low-frequency and high-frequency components are built. The hybrid prediction algorithm is proposed to overcome the problem of network traffic prediction in the communication network. Finally, network traffic data from the real network is used to validate our approach. Simulation results indicate that our algorithm holds much lower prediction error than previous methods.