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Does the abstraction provide adequate primitives to de ne, detect and react to real-life events Resource constraints Is the abstraction able to cope with hardware, computing and power constraints Scalability Is the set of provided programming primitives suitable for growing network size and/or density Deployment and maintenance Is it possible to perform resource-ef cient debugging at application level In section 532, we provided a survey on the past and present work on programming models for COs The surveyed approaches were sorted in different categories and for each category we presented a concrete implementation example We thus discussed along the way the most known and successful programming abstractions speci cally targeted to COs, and provided an overview on existing running systems such as TinyDB, DSWare, Mat , MagnetOS, SensorWare, generic role assignment, Hood and abstract regions, to name just a few.

Execution consists of a set of event invocations that may be performed concurrently The MagnetOS system provides the image of a virtual Java machine Regular Java applications are partitioned into distributable components that communicate via events by a static partitioning service Therefore, applications are rewritten at bytecode level For example, object creations are replaced by calls to the MagnetOS run-time, which.

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It is at least as easy to tell evolutionary stories around such a dual mechanism as it is around a simpler model The notion that there is a mechanism that regulates body weight around a set point may be contrasted with the idea that any apparent stability of body weight re ects a settling point which is the net result of two or more mechanisms that may have quite different functions The implication for intervention may well be different, perhaps especially for the treatment of obesity where the idea of a set point that is defended even.

Most of the proposed approaches were designed for a speci c application scenario or were tailored to some speci c design goals, and thus appear able to comply with only a subset of the above-listed requirements Even if most of them (eg database approach, agent-based approach, event-based approach, virtual markets) are not new, they required signi cant adaptation to be used for COs and/or sensor networks These approaches differ with respect to ease-of-use, expressiveness, scalability, overhead, etc, as we will now outline by means of three representative examples, namely, TinyDB, SensorWare and DSWare TinyDB provides the user with a declarative query system which is very easy to use The database approach hides distribution issues from the user, and rather than programming individual objects, the network can be programmed as a single (virtual).

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A property appraisal report prepared by a licensed property appraiser is generally defined as: an opinion or estimate of a property s value based on a thorough analysis of all the available data that s been derived from one or all three of the property appraisal valuation methods that are commonly used to appraise property. To avoid being snookered by dishonest property owners who use bogus appraisal reports to help substantiate the asking price for their property, you must know what to look for when reading a residential or commercial property appraisal report. A sham appraisal report is not that difficult to spot when you know what to look for. The best way to check the validity of an appraisal report is to use an appraisal report checklist, such as the sample on page 134.

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We have seen how to use Boost.Python to call C++ code from Python. In this section we are going to discuss how to call Python code from C++. The firs step is to embed the Python interpreter into the C++ code. To do this, we simply #include<boost/python.hpp> and call Py Initialize() to start the interpreter and create the main module. Note that at the time of writing you must not call Py Finalize() to stop the interpreter. This may change in future versions of Boost.Python. Although objects in Python are automatically reference-counted, the Python C API requires reference counting to be handled manually. So Boost.Python provides the handle and object class templates to automate the process. The handle template class is beyond the scope of this short primer.

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