TLFeBOOK in .NET

Draw EAN13 in .NET TLFeBOOK

Extension of Eq. (2.5.3) to the three dimensions is accomplished by multiplying this equation by 4pe0, by replacing the term gijn1 by the free-space Green s function for the nth image at a distance h zj zi 2nT from the eld point, and by replacing the term gijn2 by the free-space Green s function for the nth image at a distance h zj zi 2 n 1 T from the eld point. The new expressions for gijn1 and gijn2 will become & ! 1 c A2 d B2 gijn1 xj xi ln 4pe0 d C2 c D2   ! xj d B2 d C2 ln 2 c D2 c A2 ! a A2 b B2 yj yi ln b D2 a C2   ! yj b B2 b D2 ln 2 a C2 a A2    ! ac bd 2 nT atan atan 2nTA2 2nTB2    !' ad bc 2 nT atan atan 2:5:5 2nTC2 2nTD2 where q A2 a2 c2 2nT 2 q C2 a2 d 2 2nT 2 q B2 b2 d 2 2nT 2 q D2 b2 c2 2nT 2

Were this not bad enough, urban America Baltimore, Chicago, Detroit, Miami, Newark, and Washington, D.C. experienced major riots, and significant parts of some of these cities went up in flames. Police control was tested, with the National Guard and even regular Army units being called up to assist. Serious commentators were debating the proper role of the military in assisting the police in the discharge of their duties and the Constitutional issues this would raise.4 Thomas Repetto, the head of the New York City Crime Commission, has commented on the state of crime in that city during the era. In 1961, the NYPD reported 390 murders; by 1964, the rate was 637, an increase of two-thirds in just three years. By 1972, the rate was close to 1,700, quadrupling over a single decade. The number of robberies reported had risen from 23,000 in 1966, the first year statistics for such crimes had been kept accurately, to nearly 90,000 at the beginning of the 1970s.5 New York City was hardly alone in its crime problems. Meltzer observes that other cities had their issues as well:

  ! yj b B3 b D3 a C2 a A2 ln T4 2 a C3 a A3 b B2 b D2  T5 2 n 1 T atan  T6 2 n 1 T atan ac 2 n 1 TA3  atan  atan  bd 2 n 1 TB3 ! !

12.1 How is acquiring data from a human user different from automatically acquiring data from some instrument 12.2 What are the nodes in a decision tree 12.3 In a depth- rst search of a decision tree, what happens if a hypothesis is accepted 12.4 What is a rule con ict algorithm What is its relevance to a depth- rst search of a decision tree 12.5 Under what circumstance does backtracking take place 12.6 What happens during backtracking 12.7 What two different concepts of knowledge representation can be used when writing rules to conduct a depth- rst search or a decision tree 12.8 What are the advantages and disadvantages of placing knowledge in rules or in databases 12.9 How can we determine whether a problem is best solved by a depth- rst (sequential rule- ring) or breadth- rst (parallel rule- ring) search of possibilities