Mechanical properties of molybdenum neutron-irradiated at a high temperature

Molybdenum specimens prepared by two processes, powder-metallurgy (PM) and electron-beam melting (EB), were irradiated to a fast neutron fluence of 2.74 × 10²⁴n/m² (E_n? 1 MeV) at about 600°C (873 K), and their mechanical properties were studied in detail. It was shown that the degree of irradiation embrittlement in EB-Mo was smaller than that in PM-Mo, which might be caused by stronger grain-boundaries and probably smaller irradiation-hardening in the former. From the relation between the recovery of ductility and microstructural changes in post-irradiation annealed PM-Mo at 800 (1073 K), 1000 (1273 K) and 1200°C (1473 K), it was concluded that the recovery resulted from a decrease of irradiation hardening due to a rearrangement and a disappearance of depleted-zones, dislocation-loops and voids in order with increasing annealing temperature. An anomalous mode of fracture was observed in as-irradiated specimens, which consisted of inhomogeneous deformation, then brittle fracture not at the center but at the root of the deformation neck. This mode was observed in a narrow temperature range near the DBTT. A possible mechanism is discussed.     

Photoinduced superhydrophilic properties of Ti-B binary oxide thin films and their photocatalytic reactivity for the decomposition of NO

Characterizations of Ti-B binary oxide thin films by means of various spectroscopic measurements have shown that Ti-B binary oxide thin films are formed by ultra fine TiO2 nanoparticles. A dramatic decrease in the contact angle of water droplets to 0 degree under ultraviolet (UV) light irradiation and a return to only half of its initial value under dark conditions were observed for the Ti-B binary oxide thin films. Moreover, ultraviolet irradiation of these thin film photocatalysts in the presence of NO led to photocatalytic decomposition of NO into N2 and O2. Especially when the Ti content was low, the size of the primary nanoparticles of the thin films was smaller and the photocatalytic reaction proceeded efficiently with a high selectivity for the formation of N2 and O2 from NO.     

Mapping of boreal vegetation of a temperate mountain in China by multitemporal Landsat TM imagery

The Changbai Mountain Natural Reserve (2000 km 2 ), north-east China, is a very important ecosystem representing the temperate biosphere. The cover types were derived by using multitemporal Landsat TM imagery, which was modified with DEM data on the relationship between vegetation distribution and elevation. It was classified into 20 groups by supervised classification. By comparing the results of the classification of different band combinations, bands 4 and 5 of an image from 18 July 1997 and band 3 of an image from 22 October 1997 were used to make a false colour image for the final output, a vegetation map, which showed the best in terms of classification accuracy. The overall accuracy by individual images was less than 70%, while that of the multitemporal classification was higher than 80%. Further, on the basis of the relationship of vegetation distribution and elevation, the accuracy of multitemporal classification was raised from 85.8 to 89.5% by using DEM. Bands 4 and 5 showed a high ability for discriminating cover types. Images acquired in late spring and mid-summer were recognized better than other seasons for cover type identification. NDVI and band ratio of B4/B3 proved useful for cover type discrimination, but were not superior to the original spectral bands. Other band ratios like B5/B4 and B7/B5 were less important for improving classification accuracy. The changes of spectral reflectance and NDVI with season were also analysed with 10 images ranging from 1984 to 1997. Seperability of images in terms of classification accuracy was high in late spring and summer, and decreased towards winter. There were five vegetation zones on the mountain, from the base to the peak: deciduous forest zone, mixed forest zone, conifer forest zone, birch forest zone and tundra zone. Spruce-fir conifer dominated forest was the most dominant vegetation (33%), followed by mixed forest (26%), Korean pine forest (8%) and mountain birch forest (5%).     

Decorrelation of SAR data by urban damages caused by the 1995 Hyogoken-nanbu earthquake

Large part of urban area of around Kobe city were damaged by the 1995 Hyogoken-nanbu earthquake. For detecting the damaged area, the authors computed correlation coefficients from single-look complex ERS-1/SAR data. Two types of correlations, intensity correlation and coherence were computed. Decorrelation was found in the damaged urban built-up area in the data pair of which interval included the earthquake occurrence. The similarity of the decorrelation between the intensity correlation and the coherence indicated that major factor of the decorrelation is closely related to interferometric processes, which results in the change of speckle patterns in the single-look intensity images. A normalized difference was calculated from the correlation coefficients between the data pair including the earthquake occurrence and the pair before the earthquake. The distribution patterns of the pixels for which normalized difference was higher than a threshold showed good correspondence with the result of the ground survey. The result of this study indicates the possibility of detecting urban disasters using the decorrelation of SAR data.     

Selective vapor phase hydroformylation of ethylene over cluster-derived cobalt catalyst

Vapor phase hydroformylation of ethylene was studied with silica-supported metal catalysts. A cobalt metal catalyst derived from Co₂(CO)₈ gave propanal and its derivatives in as high selectivity of about 36% as Rh/SiO₂ catalyst under the reaction conditions of 1.1 MPa of a gas-mixture of ArCOC₂H₄H₂ = 1333 at 423–503 K. On the other hand, conventional cobalt catalysts derived from cobalt nitrate, chloride, or acetate, and other noble metal catalysts (Pd/SiO₂ and Ir/SiO₂) produced mainly ethane.     

On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms

Outlier detection is a fundamental issue in data mining, specifically in fraud detection, network intrusion detection, network monitoring, etc. SmartSifter is an outlier detection engine addressing this problem from the viewpoint of statistical learning theory. This paper provides a theoretical basis for SmartSifter and empirically demonstrates its effectiveness. SmartSifter detects outliers in an on-line process through the on-line unsupervised learning of a probabilistic model (using a finite mixture model) of the information source. Each time a datum is input SmartSifter employs an on-line discounting learning algorithm to learn the probabilistic model. A score is given to the datum based on the learned model with a high score indicating a high possibility of being a statistical outlier. The novel features of SmartSifter are: (1) it is adaptive to non-stationary sources of data; (2) a score has a clear statistical/information-theoretic meaning; (3) it is computationally inexpensive; and (4) it can handle both categorical and continuous variables. An experimental application to network intrusion detection shows that SmartSifter was able to identify data with high scores that corresponded to attacks, with low computational costs. Further experimental application has identified a number of meaningful rare cases in actual health insurance pathology data from Australia's Health Insurance Commission.     

Clinical application of single-shot echo-planar diffusion-weighted imaging with compressed SENSE in prostate MRI at 3T: preliminary experience

Magnetic Resonance Materials in Physics, Biology and Medicine - Image quality (IQ) of diffusion-weighted imaging (DWI) with single-shot echo-planar imaging (ssEPI) suffers from low signal-to-noise...