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Like HTML, XML documents are composed primarily of elements and attributes Each element may optionally contain one or more child elements; in the example document just shown there are two item elements inside the root stockList element, and each item element itself contains three child elements An element may also contain plain text rather than child elements (such as the text apple inside the first name element in the example document) It s also possible for an XML element to contain both child elements and plain text, though this usage isn t that common in practice Each element can optionally have one or more attributes; these are specified in the format name= value inside the element s opening tag (such as <item type= fruit > in the example document).

A' = llAt11' = (At1)'

Anyone can write XML documents, and many folks also design applications to handle XML documents both reading existing documents and composing new ones The XML specification is free for anyone to use; the World Wide Web Consortium at wwww3org authored and maintains the latest versions of the spec Although you can write XML documents just by creating arbitrary elements and attributes as shown in the stockList example earlier often you want to formally specify the elements and attributes that are allowed in a document, as well as their meaning and structure This is so that, when you exchange data with another person or application, both parties to the transaction know exactly what the element and attribute names mean To do this, you use either a document type definition (DTD) or an XML Schema definition (XSD); DTDs are discussed in detail a little later in this chapter.

in the practical shunting problem case of the Netherlands Railways (s 13, 14, 15, 16, and 17) Note that the AI approaches here differ from the ones discussed in the section about planning for yourself in AI and cognitive science, where actors make plans for themselves rather than for organizational processes Quantitative models are usually based on an analysis of the objects (entities) the domain that are scheduled For example, to make an algorithm for a planning problem in a ow shop, one has to know the capacities of machines, the setup and cleaning times, the number and sizes of orders, the processing characteristics, and so on All these characteristics can be used to determine the best way to navigate through the problem space of possible solutions An example of how such knowledge can be used in an algorithm is to start to plan the bottleneck rst.

Frequently when you create XML documents, you ll either use an existing publicly available DTD (or XSD) or use one you ve written yourself Once you write a DTD, you can publish it on the Web That means anyone who needs to read or write an XML document compatible with your system has the capability to access the published DTD to make sure the document is valid..

This is often the most sensible thing to do in order to avoid problems in a later stage of the planning process Most techniques are somehow limited in the kinds of characteristics they can handle For example, a linear programming model cannot deal with nonlinear constraints, and temporal reasoning is dif cult to implement in many mathematical techniques Therefore, the domain analysis must somehow be translated into the quantitative model, and the solution must be translated back to the application domain The second mainstream approach to plan generating techniques focuses on imitating human problem-solving processes in so-called rule-based, expert, or knowledge systems, also called the transfer view (Schreiber et al, 1993, 2000), because the knowledge to solve planning problems is extracted from a human and transferred into a computer program For this approach, the problem-solving approach of the human scheduler must be analyzed.

Applications of Plasmonics Applications of the new nanostructured materials include [340] (i) plasmonic bandgap materials, (ii) light-gated optical transmitters that allow one to control photons with photons and to develop photonic nanocircuits, (iii) plasmon-enhanced QED control of spontaneous emission for surface-enhanced coherent spectroscopies, (iv) plasmon-enhanced nonlinear photodetectors and other optoelectronic devices, and (v) novel left-handed plasmonic nanomaterials that have a negative refractive index and appear to o er untapped potential for photonics (eg, superlenses ) At the practical level, the concept of surface plasmons is applicable to thin lms, nanowires, and clusters Photonic and plasmonic applications range from energy guiding and storage to imaging and sensing Applications include but are not limited to chemical sensing where chemicals in quantities as small as a single molecule can be detected; this has applicability, for example, to homeland security and medicine.