A New Current?Voltage Relation for Duct Precipitators Valid for Low and High Current Densities

A closed-form analytic current-voltage formula for duct electrostatic precipitators is presented. A short discussion of previous theoretical and numerical solutions is given, followed by an explanation of the theoretical formula derived here. A comparison with experimental data is then given, showing that the present formula is accurate over a wide range of conditions, including wide plate spacing.     

A photolabile oligodeoxyribonucleotide probe of the decoding site in the small subunit of the Escherichia coli ribosome: identification of neighboring ribosomal components

In this work we report the synthesis of a radioactive, photolabile oligodeoxyribonucleotide probe complementary to 16S rRNA nucleotides 1397-1405 and its exploitation in identifying 30S ribosomal subunit components neighboring its target site in 16S rRNA. Nucleotides 1397-1405 lie within a single-stranded sequence that has been linked to the decoding region of Escherichia coli ribosomes. On photolysis in the presence of activated 30S subunits, the photolabile oligodeoxyribonucleotide probe site-specifically incorporates into proteins S1, S7, S18, and S21 (identified by SDS-PAGE, RP-HPLC, and antibody affinity chromatography) and into three separate 16S rRNA regions, specifically, nucleotides A-1396, G-1405-A-1408, and A-1492 and A-1493. These results provide clear evidence that G-1405 in 16S rRNA is within 24 A (the distance between G-1405 and the photogenerated nitrene) of proteins S1, S7, S18, and S21 and each of the other nucleotides mentioned above, consistent with other studies of 30S internal structure. Although the probe binds to inactive 30S subunits about as well as to activated 30S subunits, photolysis of the inactive 30S.probe complex leads to a very different pattern of protein labeling, providing strong evidence, at the protein level, that the inactive to activated transition is accompanied by conformational change in the 1400 region of 16S rRNA.     

Mining Frequent Generalized Itemsets and Generalized Association Rules Without Redundancy

This paper presents some new algorithms to efficiently mine max frequent generalized itemsets （g-itemsets） and essential generalized association rules （g-rules）. These are compact and general representations for all frequent patterns and all strong association rules in the generalized environment. Our results fill an important gap among algorithms for frequent patterns and association rules by combining two concepts. First, generalized itemsets employ a taxonomy of items, rather than a flat list of items. This produces more natural frequent itemsets and associations such as （meat, milk） instead of （beef, milk）, （chicken, milk）, etc. Second, compact representations of frequent itemsets and strong rules, whose result size is exponentially smaller, can solve a standard dilemma in mining patterns： with small threshold values for support and confidence, the user is overwhelmed by the extraordinary number of identified patterns and associations; but with large threshold values, some interesting patterns and associations fail to be identified. Our algorithms can also expand those max frequent g-itemsets and essential g-rules into the much larger set of ordinary frequent g-itemsets and strong g-rules. While that expansion is not recommended in most practical cases, we do so in order to present a comparison with existing algorithms that only handle ordinary frequent g-itemsets. In this case, the new algorithm is shown to be thousands, and in some cases millions, of the time faster than previous algorithms. Further, the new algorithm succeeds in analyzing deeper taxonomies, with the depths of seven or more. Experimental results for previous algorithms limited themselves to taxonomies with depth at most three or four. In each of the two problems, a straightforward lattice-based approach is briefly discussed and then a classificationbased algorithm is developed. In particular, the two classification-based algorithms are MFGI_class for mining max frequent g-itemsets and EGR_class for mining essential g-rules. The classification-based algorithms are featured with conceptual classification trees and dynamic generation and pruning algorithms.