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Most developers are familiar with the EJB 1.x specification and its reliance on EJB interfaces that extend java.rmi.Remote. Thus, they may continue to make blanket use of remote interfaces, even when working in an EJB 2.x environment. With proper education, developers can understand the choices that EJB 2.x offers, and the considerable benefits associated with using local interfaces where appropriate. The term local interfaces actually refers to a set of interfaces called javax.ejb.EJBLocalObject and javax.ejb.EJBLocalHome. These interfaces allow their implementers to respectively perform the roles of EJB objects and EJB homes without incurring the overhead of their remotable javax.ejb.EJBObject and javax.ejb.EJBHome counterparts. Unless a Session or Entity Bean will be accessed remotely, there is no need for it to support a remote interface. Using local interfaces not only improves the performance of an application, but can also simplify its overall design. For example, EJB 1.x applications typically used domain data transfer objects (DTOs) to convey Entity Bean attributes en masse. This practice was followed in order to reduce the overhead associated with making fine-grained invocations against Entity Bean interfaces. Although DTOs were helpful, they were also problematic because they often exploded in number and had to be synchronized

Just as NMR spectroscopy is simplest when a nuclear spin state of 1/2 is involved (1 H, 13 C, 19 F, 29 Si, 31 P, etc), EPR spectroscopy is simplest when a single electron spin ( 1/2) is involved The energies of the two states get further apart as the magnetic eld is increased, as shown by Eq 333, with the 1/2 spin state considered as the ground state Transition metal complexes with d1 or d9 electronic con gurations and one-electron free radicals of both organic and inorganic species are most often studied, although a wide range of transition metal species with other dn electronic con gurations have been studied (46, 48, 49) Samples of amorphous polymers can be studied in either the solid state as powders, in solution, or in low-temperature glasses.

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A 3-D plane wave solution of this equation is given by u r; t A k; o cos k r ot ; where the wave vector k is given by k kx ky ey kz ez and the phase velocity v is related to k and o by v A phase front is de ned by k r ot constant 3:5-13 o jkj 3:5-12 3:5-11 3:5-10

Aqueous and alcoholic solutions and glasses give poorer results than solvents and glasses with lower dielectric constants Better resolution is obtained at lower temperatures, and most EPR studies are conducted at 77 K or lower However, room-temperature EPR is often quite useful for kinetics and mechanism studies in solution The measurements are normally made at a constant frequency, and the external magnetic eld is varied through the resonance region X-band EPR, the most common, uses frequencies near D 95 109 Hz (95 GHz) and magnetic eld strengths of about 3400 gauss (for g D 2; see below) Although the sensitivity is expected to increase with the square of the frequency, the higher-frequency Q-band (35 GHz) instruments require magnetic eld strengths of about 12,500 gauss, for which homogeneity of eld strength is more dif cult to obtain This drives up the cost of Q-band instrumentation Thus many experts recommend.

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Using Proposition 1.20, with probability 1, X ( ) has no tangencies at level 0; thus, an upper bound for this expectation follows from Kac s formula:

But there is another aspect of Bayes theorem that is even more important for our understanding of how the brain works. The theorem has two critical components: p(A |X ) and p(X |A). p(A|X ) tell us how much we have to change our belief about the world (A) given the new evidence (X ). p(X |A) tells us what evidence (X ) we should expect given our belief about the world (A). We can look at these two components as devices that make predictions and detect prediction errors. Now, on the basis of its belief about the world, my brain can predict the pattern of activity that should be detected by my eyes, ears, and other senses: p(X |A). So what happens if there is an error in this prediction These errors are very important because my brain can use them to update its belief about the world and create a better belief: p(A |X ). Once this update has occurred, my brain has a new belief about the world and it can repeat the process. It makes another prediction about the patterns of activity that should be detected by my senses. Each time my brain goes round this loop the prediction error will get smaller. Once the error is suf ciently small, my brain knows what is out there. And this all happens so rapidly that I have no awareness of this complex process. Knowing what is out there in the world may seem easy to me, but my brain never rests from this endless round of prediction and updating.

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