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The digester charges and discharges through pipes, allowing mass circulation during the digesting process. The pipes can be made of 100-mm-diameter PVC and placed in rifts in the digester well, xed on the bottom and fastened on the top of the wall. The charge tank can be used either as the prefermentation tank or for mixing the material. Therefore, its volume should be a little larger than the daily charge and a little bit above the liquid level of the digester. The discharge tank is constructed primarily to protect the output ow. The cladding used for the digester is of fundamental importance to its construction and operation. The cladding should be made of a 1 : 6 ratio of cement to ne sand and should be at least 10 mm thick. Finally, to guarantee the accuracy of the diameter measurements the use of a standard scale xed on the guide pipes of the plant so as to allow free rotation around it is recommended. 8.8.1 Sizing a Biodigester

Source: Maggie Law, The KM Conundrum, KMERA Corporation, 2004.

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3.1 My daughter Brenna enjoying being thrown into the air by our good friend Bob BBQ Kuhn. Direct top lighting separates them from the darker background. Even with a fast shutter speed to freeze Brenna and the water droplets in midair, Bob s legs are slightly blurred. Exposure: ISO 400, f/5.6, 1/800 second.

Focus for QoS design (routers) router latency ( t1) Application 01 R1 end-to-end delay Application 02 R2

2

PE1, which is in the network of Service Provider A, and PE2, which is in the network of Service Provider D. In the example, the pseudowire corresponds to the customer s ATM CBR circuit having a bandwidth of 30 Mbps. The individual pseudowire hops that constitute the multihop pseudowire are as follows: PE1 to ASBR1 ASBR1 to ASBR2 ASBR2 to ASBR3 ASBR3 to ASBR4 ASBR4 to PE2

call.setReturnType( XMLType.XSD_STRING );

shown in Fig. 5.8, where the update here is written in terms of the sigma point innovations, ek ,n . In particular, it can be shown that the average innovation ek can be removed from the cross-correlation sz (also notice the switched sign of these terms with respect to the expression in z used for the UKF). However, it is needed in the last formula in Fig. 5.8, to compute yk . The equivalence between this formulation and the original UKF has been demonstrated by Vercauteren and Wang [158], where

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typedef double Time; typedef double Rate; /********************************************************************************** General diffusion process classes This class describes a stochastic process governed by dx(t) = mu(t, x(t))dt + sigma(t, x(t))dz(t). **********************************************************************************/ class DiffusionProcess { public: DiffusionProcess(double x0) : x0_(x0) {} virtual DiffusionProcess() {} double x0() const { return x0_; } // returns the drift part of the equation, i.e. mu(t, x_t) virtual double drift(Time t, double x) const = 0; // returns the diffusion part of the equation, i.e. sigma(t,x_t) virtual double diffusion(Time t, double x) const = 0; // returns the expectation of the process after a time interval // returns E(x_{t_0 + delta t} | x_{t_0} = x_0) since it is Markov. // By default, it returns the Euler approximation de ned by // x_0 + mu(t_0, x_0) delta t. virtual double expectation(Time t0, double x0, Time dt) const { return x0 + drift(t0, x0)*dt; } // returns the variance of the process after a time interval // returns Var(x_{t_0 + Delta t} | x_{t_0} = x_0). // By default, it returns the Euler approximation de ned by // sigma(t_0, x_0)^2 \Delta t . virtual double variance(Time t0, double x0, Time dt) const { double sigma = diffusion(t0, x0); return sigma*sigma*dt; } private: double x0_; }; /********************************************************************************** Black-Scholes diffusion process class

If you re an automation buff, know that you can also simulate the Word Mail Merge function from Excel. The idea is fairly simple. You start with a template that contains bookmarks identifying where each element of contact information will go.

There are only a few things that are likely to go wrong with a Tomcat installation. First, you should know that Tomcat logs error messages to the file catalina.out in the CATALINA_BASE/logs directory. This is the first place to look for clues if Tomcat does not seem to be working. The most common installation problems for Tomcat are:

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2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 Monte Carlo Generating Sample Paths and Normal Deviates Generating Correlated Normal Random Variables Quasi-Random Sequences Variance Reduction and Control Variate Techniques Monte Carlo Implementation Hedge Control Variates Path-Dependent Valuation Brownian Bridge Technique Jump-Diffusion Process and Constant Elasticity of Variance Diffusion Model Object-Oriented Monte Carlo Approach