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Radio Frequency Identification (RFID) is a quickly growing area of research, development, and implementation, in part because of the need of many communities, private and commercial, to track large numbers of items within a defined environmental context. RFID technology is based on small, often extremely small, transponders, which can be used as embedded tags to identify objects through low-power radio frequency signals. One issue that has been a challenge to RFID developers is that multiple standards organizations are involved with developing RFID architecture, and multiple regulatory bodies have some oversight of RFID implementation activities. Various types of RFIDs exist today, and others are being created, designed to meet the requirements of a growing number of applications and services. Passive RFIDs are not internally powered. When certain types of external radio frequency signals are detected by the tag s antenna, an internal electrical current is generated within its integrated electronic circuit. This activation mode powers the interactive communication required for the sending of information, or for the interactive exchange of information. Active RFIDs contain their own power source, usually small batteries. RFIDs can operate across a broad range of frequencies, e.g., 135 kHz (and less, 30 kHz) on the low end, to almost 6 GHz on the high end, and many frequencies in between. A popular range for many RFID devices is 13.5 MHz, which is used by product labels and smart cards. The high side of the mid-range is around 30 MHz.

The other view is that, since speculative grade borrowers have a greater probability of default, a higher LEQ should be assigned to lower grade borrowers Araten and Jacobs also noted that the other important factor in estimating LEQ is the tenor of the commitment With longer time to maturity, there is a greater opportunity for drawdown as there is more time available (higher volatility) for a credit downturn to occur, raising its associated credit risk Consequently, Araten and Jacobs focused on the relation of the estimated LEQs to (1) the facility risk grade and (2) time-to-default The data set for revolving credits included 834 facility-years and 309 facilities (ie, two to three years of LEQ measurements prior to default per facility) Exhibit 328 contains the LEQs observed8 (in boldface type) and predicted (in italics) by Araten/Jacobs The average LEQ was 43% (with a standard deviation of 41%).

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A number of different privacy rights have been carved out of the First Amendment concept of liberty, Fifth Amendment protections against self-incrimination, Ninth Amendment reservation of rights to the people, and the Fourth Amendment prohibition against unreasonable search and seizure (Hummel, Talbutt, & Alexander, 1985) In a case that received considerable attention from legal scholars, a federal district court ruled that parents of school children have a right to be free from the invasion of family privacy by the school (Merriken v Cressman, 1973; see 10) This right to privacy was recognized only for the parents or family unit; the courts generally have not recognized an independent student right to privacy in the schools However, federal education law (eg, the Family Educational Rights and Privacy Act discussed later in this chapter) now provides some guidance regarding protection of the privacy rights of pupils and their parents..

An indispensable tool in the war against crime is the ability to identify with precision when and where crime takes place, what form it takes, and the characteristics of its victims and perpetrators. Armed with such information, law enforcement can better make its case to acquire the resources it needs to fight crime and, after obtaining those resources, use them in the most efficient and effective manner. NIBRS provides law enforcement with that tool because it is capable of producing more detailed, accurate, and meaningful data than those produced by the traditional UCR Program.8

The observed LEQs (the numbers in boldface type in Exhibit 328) suggest that LEQ declines with decreasing credit quality This is most evident in shorter time-to-default categories (years 1 and 2) LEQ increases as time-to-default increases To ll in the missing LEQs and to smooth out the LEQs in the table, Araten/Jacobs used a regression analysis While they considered many different combinations of factors, the regression equation that best t the data (ie, had the most explanatory power) was LEQ = 4836 349 (Facility Rating) + 1087(Time-to-Default) where the facility rating was on a scale of 1 8 and time-to-default was in years Other variables (lending organization, domicile of borrower, indus-.

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