Regions of the target sequence that are not aligned to a template are modeled by loop modeling; they are the most susceptible to major modeling errors and occur with higher frequency when the target and template have low sequence identity. The coordinates of unmatched sections determined by loop modeling programs are generally much less accurate than those obtained from simply copying the coordinates of a known structure, particularly if the loop is longer than 10 residues. The first two sidechain dihedral angles (χ 1 and χ 2 ) can usually be estimated within 30° for an accurate backbone structure; however, the later dihedral angles found in longer side chains such as lysine and arginine are notoriously difficult to predict. Moreover, small errors in χ 1 (and, to a lesser extent, in χ 2 ) can cause relatively large errors in the positions of the atoms at the terminus of side chain; such atoms often have a functional importance, particularly when located near the active site.
Conference modeling is an approach where the goal often is of a more social kind, such as motivation, and change management. The idea is to involve a group of diversified people from the domain in question. Then the modeling process is developed in group participation during a fixed period of time.
Parametric modeling uses parameters to define a model (dimensions, for example). Examples of parameters are: dimensions used to create model features, material density, formulas to describe swept features, imported data (that describe a reference surface, for example). The parameter may be modified later, and the model will update to reflect the modification. Typically, there is a relationship between parts, assemblies, and drawings. A part consists of multiple features, and an assembly consists of multiple parts. Drawings can be made from either parts or assemblies.
Modeling can be performed by means of a dedicated program (e.g., Cinema 4D, Maya, 3ds Max, Blender, LightWave, Modo) or an application component (Shaper, Lofter in 3ds Max) or some scene description language (as in POV-Ray). In some cases, there is no strict distinction between these phases; in such cases modeling is just part of the scene creation process (this is the case, for example, with Caligari trueSpace and Realsoft 3D).
Related to parameters, but slightly different, are constraints. Constraints are relationships between entities that make up a particular shape. For a window, the sides might be defined as being parallel, and of the same length. Parametric modeling is obvious and intuitive. But for the first three decades of CAD this was not the case. Modification meant re-draw, or add a new cut or protrusion on top of old ones. Dimensions on engineering drawings were created, instead of shown. Parametric modeling is very powerful, but requires more skill in model creation. A complicated model for an injection molded part may have a thousand features, and modifying an early feature may cause later features to fail. Skillfully created parametric models are easier to maintain and modify. Parametric modeling also lends itself to data re-use. A whole family of capscrews can be contained in one model, for example.
Barbizon’s modeling curriculum follows the techniques used by professional models. Classes center around voice projection, self-confidence, print and runway modeling. The curriculum varies dependent upon the class category. Barbizon offers classes for female modeling, male modeling, pre-teen (kids) modeling, full-figure modeling, and advanced professional modeling.
Third, the System Dynamics method, unlike other modeling approaches, shows reciprocal feedback relationships between variables instead of simple one-way causality. Most statistical models are based on one- way causal relationship between a set of independent variables and a dependent variable. For example, component failures could be correlated with various conditions on the production line. System Dynamics models, such as those underlying LPM, include two way causality in which a variable “a” has a causal effect on variable “b” and “b” feeds back to affect “a”. For example, end-item failures reduce the number of planes’ available flying hours. A fleet’s fewer available flying hours increases the required number of hours flown per plane which increases end-item failures. The interaction between failures and hours flown creates a self-reinforcing relationship that is called a positive feedback loop. Positive feedback loops are also known as vicious or virtuous circles (or cycles).
The delimited real system is convicted with help of the IEM method in an abstract model. IEM is the construction of the two main positions "information model" and "business process model". The "information model" is made by the specification of the object classes to be modeled for "product", "order" and "resource" with the class structures as well as descriptive and relational features. By identification and description of functions, activities and its combination to processes the "business process model" is formed. As a general rule the construction of the "information model" follows first in which the modeling person can go back to available reference class structures. The reference classes which do not correspond to the real system or were not found to be relevant at the system delimitation are deleted. The missing relevant classes are inserted. After the object base is fixed, the activities and functions are joined together at the objects according to the "generic activity model" and with the help of combination elements to business processes. A model is made which can be analysed and changed if it's required. It often happens, that during the construction of the "business process model" new relevant object classes are identified so that the class trees getting completed. The construction of the two positions is therefore an iterative process.
The system dynamics perspective has important implications for the type of detail included in the model. Other modeling methods, for example discrete event simulation, usually involve many complex details (e.g. specific airplanes), where LPM focuses on a relatively few major components that are responsible for most end item failures, yet includes a great deal of dynamic complexity by modeling the interactions between multiple subsystems. Dynamic and detail complexity are both important to understand but are usually best approached through different modeling methods.
In the context of business process integration (see figure), data modeling complements business process modeling, and ultimately results in database generation.
The physics and modeling of devices in integrated circuits is dominated by MOS and bipolar transistor modeling. However, other devices are important, such as memory devices, that have rather different modeling requirements. There are of course also issues of reliability engineering—for example, electro-static discharge (ESD) protection circuits and devices—where substrate and parasitic devices are of pivotal importance. These effects and modeling are not considered by most device modeling programs; the interested reader is referred to several excellent monographs in the area of ESD and I/O modeling.
The functional modeling approach concentrates on describing the dynamic process. The main concept in this modeling perspective is the process, this could be a function, transformation, activity, action, task etc. A well-known example of a modeling language employing this perspective is data flow diagrams.
This approach concentrates on describing the static structure. The main concept in this modeling perspective is the entity, this could be an object, phenomena, concept, thing etc.
The structuring of the enterprise processes in Integrated Enterprise Modeling (IEM) is reached by its hierarchical subdivision with the help of the decomposition. Decomposition means the reduction of a system in a partial system which respectively contains components which are in a logical cohesion. The process modeling is a partitioning of processes into its threads. Every thread describes a task completed into itself. The decomposition of single processes can be carried out long enough until the threads are manageable, i.e. appropriately small. They may turn out also not too rudimentary because a high number of detailed processes increases the complexity of a business process model. A process modeling person therefore have to find a balance between the effort complexity degree of the model and possible detailed description of the enterprise processes. A model depth generally recommends itself with at most three to four decomposition levels (model levels).
Second, the method takes a broad view of the factors that cause changes in MC as opposed to a more detailed microscopic view. One way to analyze MC is to focus on the detailed technological factors that cause components to fail. This would involve root cause analysis and perhaps the design of experiments. A complementary LPM/system dynamics perspective considers how the major subsystems of a supply chain interact to affect MC. The broad view is important for anticipating otherwise unanticipated side effects. Often, the benefits of productive investments in one part of a system can be nullified by unanticipated negative reactions, or 'rogue outcomes', to those investments in another part of the system. System dynamics modeling has been shown to provide some early warning of 'unintended consequences'.
There are three elements of the system dynamics method that differentiate it from other modeling methods. First, is to explain why a system changes over time as opposed to why a system is in a particular state at a point in time. For example, statistical analysis could be very useful for understanding the factors that were correlated with mission capability in 2005. System Dynamics could be useful in understanding relationships that caused mission capability (MC) to change over the last 5 years.
Being originally invented as an advanced training to teach Object-Oriented Analysis and Design with UML to students, the Speechless Modeling, in essence, is a restriction on using communication means directly or indirectly involving a natural language. In this way, a team of designers is forced to use the modeling language as the only language available for communication during a design session.
The overall goal of semantic data models is to capture more meaning of data by integrating relational concepts with more powerful abstraction concepts known from the Artificial Intelligence field. The idea is to provide high level modeling primitives as integral part of a data model in order to facilitate the representation of real world situations.
The logical data structure of a DBMS, whether hierarchical, network, or relational, cannot totally satisfy the requirements for a conceptual definition of data because it is limited in scope and biased toward the implementation strategy employed by the DBMS. That is unless the semantic data model is implemented in the database on purpose, a choice which may slightly impact performance but generally vastly improves productivity. Therefore, the need to define data from a conceptual view has led to the development of semantic data modeling techniques. That is, techniques to define the meaning of data within the context of its interrelationships with other data. As illustrated in the figure the real world, in terms of resources, ideas, events, etc., are symbolically defined within physical data stores. A semantic data model is an abstraction which defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world.
Recently, an effort to create and disseminate open multi-hazard cat risk modeling tools was initiated by the Alliance for Global Open Risk Analysis (AGORA).