Distributed data mining (DDM) is the semi-automatic pattern extraction of distributed data sources. The next generation of the data mining studies will be distributed data mining for many reasons. First of all, most of the current used data mining techniques require all data to be resident in memory, i.e., the mining process must be done at the data source site. This is not feasible for the exp...

Recent research suggests that incubation is superior to metacognition in tasks involving many decision alternatives (Dijksterhuis et al., 2006). One explanation for these findings is the limited information processing capacity of working memory. The current study further investigates this topic by using more complex tasks as many errors in Dynamic Decision-Making (DDM) are thought to be related...

Distributed Data Mining (DDM) has been proposed as a means to deal with the analysis of distributed data, where DDM discovers patterns and implements prediction based on multiple data sources. However, faces several problems in terms autonomy, privacy, performance implementation. requires homogeneity regarding environment, control, administration classification algorithm(s), such that requireme...

Data Distribution Management (DDM) is one of the High-level Architecture (HLA) services that reduce message traffic over the network. The major purpose of DDM is to filter and route the exchange of data between federates during a federation. However, this traffic reduction usually results in a significant computational overhead, which is caused by calculating the intersection between update reg...

Data Distribution Management (DDM) deals with two basic problems: how to distribute data generated at the application layer among underlying nodes in a distributed system and how to retrieve data back whenever it is necessary. This thesis explores DDM in two different network environments: peer-to-peer (P2P) overlay networks and cluster-based network environments. DDM in P2P overlay networks is...

liquid protein formulations are prone to form aggregates. the effect of nonionic surfactants such as polysorbate 20 (ps 20) and n-dodecyl β-d-maltoside (ddm) on the prevention of aggregation and conformational changes of recombinant human ifnβ-1b (rhifn β_1b) was explored. polysorbate has been used in formulations of protein pharmaceuticals. there have been concerns about using ps 20 due to its...

Liquid protein formulations are prone to form aggregates. The effect of nonionic surfactants such as Polysorbate 20 (PS 20) and n-Dodecyl β-D-maltoside (DDM) on the prevention of aggregation and conformational changes of recombinant human IFNβ-1b (rhIFN β_1b) was explored. Polysorbate has been used in formulations of protein pharmaceuticals. There have been concerns about using PS 20 due to its...