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/** Fetch photo data from Flickr for a given tag. */ function fetchFlickrTag($tag) { $fapi = new phpFlickr(FLICKR_API_KEY); $fapi->enableCache( fs , CACHE_DIR); $results = $fapi->photos_search( array( tags =>$tag) ); $photos = array(); foreach ($results[ photo ] as $result) { $photo = $result; $photo[ sizes ] = $fapi->photos_getSizes($result[ id ]); array_push($photos, $photo); } return $photos; }
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Tundra soils, gelisols, entisols, inceptisols, and associated histosols Podosol (spodosols and associated histosols) Gray-brown podosols (spodosols, al sols) Gray-brown podosols (al sols) Red and yellow podzolic (ultisols)
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Narada headers Interaction ID JXTA interaction type Peer group id Peer id JXTA event payload Narada event distribution traces Present when targeted to a specific peer Request/ response
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Value: <color>{1,4} Initial: The value of the color property Applies to: All elements
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Scholarly inquiry into the intrinsic properties of soil (pedology), separate from their impacts on plant growth (edaphology), developed signi cantly in Europe, Russia, and the United States in the nineteenth century (Brady and Weil, 1999). At least two complementary approaches to soil science have progressed simultaneously since then: eld approaches to natural history and soil genesis; and laboratory approaches (chemical, biological, mineralogical, and physical determinations) applied to soil samples. Despite advancements in both approaches throughout the twentieth century, McBride (1994) has written, much of soil science is empirical rather than theoretical in practice. This fact is a result of the extreme complexity and heterogeneity of soils, which are impossible to fully describe or quantify by simple chemical or physical models. Soils are natural bodies, whose lateral and vertical boundaries usually occur as gradients between mixtures of materials of atmospheric, geologic, aquatic, and/or biotic origin. Soils are open systems subject to uxes in energy (e.g., sunlight, wind) and materials (e.g., aqueous precipitation, erosion, deposition, inputs of organic compounds from activities of plants, human beings, and other animals). Furthermore, the intrinsic complexity of soil stems from its nature as an assemblage of solid, liquid, gaseous, organic, inorganic, and biological constituents whose chemical composition and random three-dimensional structure have not been completely characterized. In addition to physical complexity, the microbial (Bacteria, Archaea, fungi, algae, protozoa, viruses; see 5) physiological processes in soil and their multitude of interactions are dauntingly complicated. Compounding the challenge of understanding soil processes is the fact that abiotic reactions (e.g., precipitation, dilution, hydrolysis)
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Remote invocation is always necessary for some demanding applications that cannot (exclusively) be run locally on the computer of a user because they depend on a set of resources scattered over multiple remote domains. Examples include computationally demanding gene sequencing, business forecasting, climate change simulation, and astronomical sky surveying as well as data-intensive HEP analysis sweeping over terabytes of data. Such applications can reasonably only be run on a remote supercomputer or several large computing clusters with massive CPU, network, disk and tape capacities, as well as an appropriate software environment matching minimum standards. The most straightforward but also most in exible con guration approach is to hard wire the location, interface, behavior, and other properties of remote services into the local application. Loosely coupled decentralized systems call for solutions that are more exible and can seamlessly adapt to changing conditions. For example, if a user turns out to be less than happy with the perceived quality of a word processor s remote spell checker, he/she may want to plug in another spell checker. Such dynamic plug-ability may become feasible if service implementations adhere to some common interfaces and network protocols, and if it is possible to match services against an interface and network protocol speci cation. An interesting question then is: What infrastructure is necessary to enable a program to have the capability to search the Internet for alternative but similar services and dynamically substitute these Web Services: As communication protocols and message formats are standardized on the Internet, it becomes increasingly possible and important to be able to describe communication mechanisms in some structured way. A service description language addresses this need by de ning a grammar for describing Web services as collections of service interfaces capable of executing operations over network protocols to end points. Service descriptions provide documentation for distributed systems and serve as a recipe for automating the
Table 4.12
Solver is an add-in developed by the company Frontline Systems ( A basic version of Solver has been bundled with Excel since 1991. More advanced versions are offered by Frontline Systems. The idea of finding the optimal value is to find the coefficients that minimize or maximize some objective function. For our purposes, the objective function simply means the value in the target cell, such as the example in the text for the total error for the weighted best-fit line. The objective function can be quite complicated, because it can depend directly on the input cells or there could be many intermediate calculations, using other cells in the spreadsheet. The weighted best-fit line is a particularly simple type of problem to solve, because it is in a class called convex conic quadratics. The simplest example of this, a parabola, has a single minimum value, and by analyzing information at any point along the curve, it is possible to determine whether the minimum is to the left or right of that point. Solver guesses the solution and then refines the guess, getting closer and closer each time. Making even small changes to the spreadsheet model can change the structure of the problem. So, changing the objective function to something more complicated could have a big impact on the efficiency of the algorithm. A small change could result in Solver taking much more time to find the optimal solution. The Solver software is quite powerful. It can detect when a problem is easy to solve and solve it using the appropriate methods. More complicated problems have more complicated methods, which can take longer to solve. Finding the coefficients for a best-fit line is only a taste of what Solver can do. One interesting class of problems is resource allocation. This occurs when there are many constraints and the goal is to maximize profit. An example of this is dividing the marketing budget to bring in new customers in various channels. Different channels have different costs for acquiring customers. The customers who come in may behave differently, and different times of year may have better response or different mixes, and each channel has a maximum or minimum capacity. It is possible to set up a spreadsheet that, given a mix of customers, is able to calculate the profit. Then, the overall profit can be maximized using Solver. Of course, the result is only as good as the assumptions going into the worksheet model, and these assumptions are only estimates about what might happen in the future. This type of resource allocation problem is called a linear programming problem (for technical reasons; it is not related to linear regression), and Solver knows how to solve such problems quite efficiently.
For more on paths, see 4. For more on selecting and editing paths, see 6.
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