### Table 1: Order-up-to vs. Separating Plane Policies for

1999

"... In PAGE 6: ... For instance, us- ing mode CJBCCL and mode CJBEBCCL cuts the cost by more than a half! The experimental results also suggest that (a) it is bet- ter to couple two modes when their leadtime difference is substantial; (b) higher variance in demand allows more sav- ings. In Table1 , we list the performance of echelon order- up-to policies versus separating plane policies for demand profile C6B4BEBCBN BDBCBEB5. We note that separating plane policies performed better when the gap between the two leadtimes increases.... ..."

Cited by 5

### Table 1: Order-up-to vs. Separating Plane Policies for

1999

"... In PAGE 6: ... For instance, us- ing mode CJBCCL and mode CJBEBCCL cuts the cost by more than a half! The experimental results also suggest that (a) it is bet- ter to couple two modes when their leadtime difference is substantial; (b) higher variance in demand allows more sav- ings. In Table1 , we list the performance of echelon order- up-to policies versus separating plane policies for demand profile C6B4BEBCBN BDBC BE B5. We note that separating plane policies performed better when the gap between the two leadtimes increases.... ..."

Cited by 5

### Table 6: Maintaining an up-to-date system

"... In PAGE 9: ... More than a quarter of the participants do not use anti-virus at all. Most users also keep their OS, browser and anti-malware updated ( Table6 ). We also collected statistics on update mechanisms (auto/manual).... ..."

### Table I. Elevator Abstraction: Comparison between (up to) two runs of the classical model-checker and a run of Chek. CTL

2003

Cited by 34

### Table-driven routing protocols attempt to maintain consistent, up-to-date routing information from each node to every other node in the network. The routing information is kept in a number of different tables and they respond to changes in network topology by propagating updates throughout the network in order to maintain a consistent

### Table 1: Encoding of places for k-bounded Petri nets (with k = 3). 8 Weighted and Bounded Petri Nets This section will extend the already presented PN modeling and analysis techniques to weighted and bounded nets. First, bounded nets require sets of boolean variables to encode places. Once a encoding is selected it is necessary to build the corresponding transition functions. Finally, it will be necessary to verify that the boundedness assumptions hold, and extend the model to include inhibitor arcs. 8.1 Place Encoding A place p 2 P that may contain up to k tokens can be represented with a set of boolean variables, p1; : : :; pKp to encode the up-to-k possible number of tokens. The number of required variables depends on the type of encoding. Two di erent encoding strategies will be proposed: one-hot encoding and binary encoding. If an one-hot encoding is used, k variables are needed. For example, in a 3-bounded PN the number of tokens in place p could be represented by three variables (see Table 1). With a binary encoding dlog2(k +1)e variables would be required. Only two binary variables are required in a 3-bounded PN (see

"... In PAGE 21: ... With a binary encoding dlog2(k +1)e variables would be required. Only two binary variables are required in a 3-bounded PN (see Table1 ). The selected one-hot encoding will be equivalent to the binary encoding in case of safe PNs.... ..."

### Table 3: Data for the Relations Used in Example 7. The steps in the SYNCMAB strategy (see Fig. 7) presented above are for this example the following: 0. The view Customer-East0 is de ned as in Eq. (33) and its extent is initialized with the set f(John; 19), (Mary; 18)g given the extents of the relations Customer and FlightRes from Table 3. Note that this extent is not up-to-date given the de nition of the view Customer-East0. 1. The expression Customer? is f(John; 19)g (Fig. 7). 5. The expression Customer+ computes f(Gill; 16); (Bob; 17)g (Fig. 7). Note that the relation Customer+ contains a new tuple (Gill; 16) found only in the relation Accident-Ins plus a -common tuple (Bob; 17) of the relations Customer and Accident-Ins. The tuple (Bob; 17) was not part of the initial view Customer-East as it fails the select condition (C.State =0 MA0) that now was dropped from the rewriting Customer-East apos;.

1999

Cited by 22

### Table 1: Data sources included in Atlas.

2005

"... In PAGE 3: ... Currently, the data sources that fall into these categories are: apos;sequence apos;, Gen- Bank, RefSeq [11], and UniProt ; apos;molecular interactions apos;, HPRD, BIND, DIP, IntAct, and MINT; apos;gene related resources apos;, Online Mendelian Inheritance in Man (OMIM) [20], LocusLink [11,21], Entrez Gene [22], and HomoloGene [11,23]; and apos;ontology apos;, NCBI Taxonomy [11,24], and Gene Ontology [25,26]. Table1 lists each of the sources of data incorporated into Atlas, and provides URLs where those sources can be found. Note that Gen- Bank refers to the integrated records from the Interna- tional Nucleotide Sequence Database Collaboration (GenBank [11], DDBJ [27], and EMBL [28]).... ..."

### Table 9: # iterations using preconditioner based on sparse Schur built using inexact local solvers ILLT(t). 3.2.2 Elliptic problems in the solution of parabolic equations In Table 10, we report experimental results for the solution of elliptic problems involved in the so- lution of a parabolic equation for one time step. Here the operator L corresponds to an anisotropic equation with the anisotropy not necessarily aligned with the x or y axis, but making an angle . The time step and the mesh size are such that = 0:02, which gives raise to a well conditioned linear system (16) (independent of h for the classic heat equation) and consequently a well condi- tioned associated Schur complement. With this choice we note that the local preconditioners are numerically scalable with respect to the number of subdomains as it was already observed in an overlapping domain decomposition approach [18]. On those examples MV E is generally between 20 % up-to 40 % faster than ME, while both, as already noticed, have a similar computational complexity [7]. MS is still the most e cient but for those problems, the gap between this precondi- tioner and the other two decreases. In that case, MV E may be the most e cient alternative as the

1999

### TABLE 4-9:Fibric Acid Derivatives (Fibrates)

"... In PAGE 27: ... TABLE4 -7:HMG-CoA Reductase Inhibitors (Statins) Drug Dosage Adverse effects Patient information Monitoring Lovastatin (Mevacor)* (tab 20 mg, 400 Rials) Initially, 20 mg with evening meal. If serum cholesterol is gt;300 mg per dL, start with 40 mg daily.... In PAGE 28: ... TABLE4 -8:Niacin (Nicotinic Acid) Drug Dosage Adverse effects Patient information Monitoring Nonprescription niacin (tab 100 mg, 45 Rials, tab 500 mg 1450 Rials) 50 to 100 mg twice daily for the first week. Double the dosage every week to 1,000 to 1,500 mg daily, in 2 or 3 divided doses.... In PAGE 30: ... TABLE4 -10:Bile Acid Sequestrants Drug Dosage Adverse effects Patient information Monitoring Cholestyramine (LoCholest) powder (4 gr sachet, 1700 Rials) Initially, 4 g daily in 2 or 3 divided doses Increase dosage at 4- week intervals as tolerated. Maximum dosage: 24 g daily Constipation Take 1 hour before or 4 hours after other medications.... ..."