Compound Object

The goal of object design is to support the above workflow. As the Compound object is in the center of this process, it makes sense to further analyze and design the Compound object first. 

The above workflow can very well be represented by a finite state machine—the compound object goes through a series of state transitions in the compound registration process. A finite state machine can be modeled using the UML State Diagram. Figure 12.4 is the UML State Diagram of a compound object. 

When a compound object is created, its structure may have multiple fragments. The system should first decompose the structure into individual fragments and let the chemist specify their roles—parent, salt, and their coefficients. This is done at the Created State, which transitions to the Fragmented State when the fragmentation finishes. Next, the system must make sure the structure is fully compliant with structure conventions. This is tlie structure QC process. There are three possibilities as a result of this process ... 

Figure 12.4 The state transition diagram of the Compound object.

Now look at the Compound class. The Compound class has all its states as its instance variables. These variables are instantiated when the compound object is created (see its constructor), not the first time when they are needed. 

The other instance variables in the Compound class include a Molstructure object, a list of salt structures, a list of parent structures, a String array that contains information about what has been changed or what the problems are in the structure as a result of the chemistry rules checking, and a Compounds tate object representing the current state of the Compound object. The process() method in the Compound object simply delegates the call to its current state object, which knows how to process the Compound object depending on its current state as described above. 

Suppose the compound state transition only involves structure changes and undo only needs to recover the structure and the compound state. Figure 12.6 is the class diagram of the Memento Pattern for the Compound object. 

Figure 12.6 The class diagram of the Memento Pattern for the Compound object.

The createMemento() method creates and returns a CompoundMemento object that is a snapshot of the current state of the Compound object. The setMemento() method restores the state of the Compound object to the state that was saved in the CompoundMemento object that is passed in. The undo() method is called by the client of Compound object to restore the state. 

Notice how the process() method is implemented—it calls the process() method on the Compound object, which is a member variable of the ChemicalSample object. As described at the beginning of this chapter, the Compound object is a finite state machine. Its process() method is implemented based on what state the Compound object is in and is responsible for the state transition of the Compound object. 

Every time the chemist edits the data and submits the form, the Front Controller receives a request and updates the ChemicalSample objects in the session. Depending on the type of request and the states of the Compound objects, it retrieves a Command object from the ApplicationController. If this is in the middle of structure QC or salt handling process, the Application Controller returns a ChemistryProcessCommand object. The FrontController... 

The execute() method simply calls the ChemicalLibrary object s process() method. If you remember how the process() method in ChemicalLibrary is implemented, all it does is iterate through all its ChemicalSample objects and invoke their process() methods, which depends on what state the Compound object is in, and either decompose the compound s structure into fragments, QC the structure, or attach salt to the parent structure. 

Now we are ready to develop the data mapper for the Compound object—the CompoundMapper class. The following class diagram describes Compound Mapper s relationship with the two interfaces (Figure 15.1). 

The CompoundMapper class—the data mapper for the compound object—is as follows ... 

The CompoundMapper defines specific data access logic for the Compound object. Its findStatementPrimaryO method defines how to query the compound table using its primary key. The findByCorpIdPrimary() defines how to query the compound table using its corporate id and so on. 

For N given (compound) object points, the sum of the squared distances of the corresponding points Oj to M is constant. 

Table 4.1 Water Solubility of Ionic Compounds Objective 9...

Classify each of the following compounds as either (1) a binary ionic compound. Objective 12... 

This calculation also illustrates a general result in diffraction theory the scattering intensity for any compound object, A+B, can be written as ... 

In other words, the scattering intensity from a compound object, A + B, is the incoherent... 

Benzene, the simplest aromatic compound, and other members of the aromatic class contain a six-membered ring with three double bonds. This aromatic ring is often drawn as a hexagon containing a circle, which represents the six electrons of the double bonds that move freely around the ring. All organic compounds that do not contain an aromatic ring are called aliphatic compounds. Objective 8, Exercise 2.48... 

Rule induction is an artificial intelligence method that has been applied to the analysis of a number of chemical data sets. As the name implies, rule induction aims to extract rules from a set of data (descriptor variables) so as to classify the samples (compounds, objects) into two or more categories. The input to a rule induction algorithm is a number of test cases, a test set, and the output is a tree-structured series of rules, also known as a class probability tree. A popular rule induction algorithm is... 

Task taxonomies have been proposed for many kinds of visualizations. Amar et al. presented a list of 10 basic tasks on multivariate data visualizations [4] Plaisant et al. adapted it to graphs [5]. Unlike multivariate data tables where the objects of interest are records with uniform attribute sets, networks have attributes associated with edges as well as vertices, and a more complex topology including compound objects such as paths or sub-graphs. Thus the task set is different. However, their tasks are generic and need extension for social networks. 

In its simplest, and in chemistry the most used, form, PLS is a method for relating two data matrices X and Y to each other by a linear multivariate model. PLS stands for projection to latent structures by means of partial least squares. It derives its usefulness from its ability to analyze data with many noisy, collinear, and even incomplete variables in both X and Y. PLS has the desirable property that for the parameters regarding the observations (samples, compounds, objects, items), the precision improves with the increasing number of relevant X variables. This corresponds to the intuitive belief of most chemists that many variables provide more information about the observations than do just a few variables. 

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