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Research-Based Principles of Vocational Rehabilitation and Supported Employment
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section one:
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environment using output methods to update the user on the status of the search or the end results. What makes the agent intelligent is its ability to make a rational decision when given a choice. In other words, given a goal, it will make decisions to follow the course of actions that would lead it to that goal in a timely manner. An agent can usually generate all of the possible outcomes of an event, but then it will need to search through those outcomes to nd the desired goal and execute the path (sequence of steps) starting at the initial or current state, to get to the desired goal state. In the case of the intelligent Web search agent, it will need to utilize a search to navigate through the Web to reach its goal. Building an intelligent Web search agent requires mechanisms for multiple and combinational keyword searches, exclusion handling, and the ability to self-seed when it exhausts a search space. Given a target, the Web search agent should proceed to look for it through as many paths as are necessary. This agent will be keyword based. The method advocated is to start from a seed location (user provided) and nd all other locations linked in a tree fashion to the root (seed location) that contains the target. The search agent needs to know the target (i.e., keyword or phrase), where to start, how many iterations of the target to nd how long to look (time constraint), and what methods should determine criteria for choosing paths (search methods). These issues are addressed in the software. Implementation requires some knowledge of general programming, working with sockets, the HTTP, HTML, sorting, and searches. There are many languages with Web-based utilities, advanced application programming interfaces (APIs), and superior text parsing capabilities that can be used to write a Web search agent. Using a more advanced, ef cient sorting algorithm will help improve the performance of the Web search agent. The Web search agent design consists of four main phases: initialization, perception, action, and effect. In the initialization phase, the Web search agent should set up all variables, structures, and arrays. It should also get the base information it will need to conduct the hunt for the target, the goal, a place to start, and the method of searching. The perception phase is centered on using the knowledge provided to contact a site and retrieve the information from that location. It should identify if the target is present and should identify paths to other Universal Resource Locator (URL) locations. The action phase takes all of the information that the system knows and determines if the goal has been met (the target has been found and the hunt is over). If the hunt is still active it must make the decision on where to go next. This is the intelligence of the agent, and the method of search dictates how smart the Web agent will be. If a match is found, the hunt is complete, and it provides output to the user. The Web search agent moves from the initialize phase to a loop consisting of the perception, action, and effect phases until the goal is achieved or cannot be achieved.
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and it is easy to see how traditional ROI calculations can become daunting or even unrealistic in certain cases. The value of an SOA initiative can be easily expressed in terms of soft bene ts like exibility and customer satisfaction, as well as hard bene ts such as reduced total cost of ownership (TCO) and increased revenue. Most discussions of SOA ROI focus on the soft bene ts of SOA as these are easier (and safer) to discuss. It is the hard, quanti able SOA bene ts that are more elusive and, consequently, the most important to explore. The quanti able bene ts of SOA can be grouped into three categories: 1. Short-term tactical ROI. Easily quanti able on a project-speci c basis by measuring the reduction in integration expenses required. 2. Medium-term operational ROI. Tougher to quantify; savings is derived through reuse of enterprise assets. 3. Long-term strategic ROI. Dif cult to quantify; savings and increased revenue are based on agility afforded through service infrastructure and business alignment. We will explore each of these categories in turn. In some cases, formal ROI calculation models exist; in other cases, there are simply some solid guidelines and best practices to employ.
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minimum required for taking advantage of the lower long-term capital gains tax rate of 20 percent. Franklin I and II, by the way, is the same apartment complex discussed in the previous chapter the one the broker recommended selling at a price of $1.8 million, claiming I would be lucky to get $1.65 million for it. As you may recall, my own analysis which suggested an exit price of $2 million to $2.1 million was ultimately corroborated by two other brokerage firms and an appraiser, and the property eventually sold for a price of $1,995,000. Take a minute to refer back to the Estimated Exit Price YR 1 line in Exhibit 8.7. My original estimate of $1,950,000 proved to be quite accurate and slightly on the conservative side. You can see firsthand from this example the value of having a powerful and dynamic model, such as the one I developed, to help you with your analysis. By simply adjusting the variables to reflect realistic projections based on your research and analysis, within minutes you can determine the latent potential of any multifamily property before you buy it.
Peak Periods
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