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Related Concept Videos

Schemata01:17

Schemata

A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
Two types of schemata are:
Impact of Schemas01:30

Impact of Schemas

Schemas are cognitive structures that provide a framework for interpreting and organizing social information. They help individuals navigate complex environments by offering expectations about people, events, and behaviors. Schemas influence attention, encoding, and retrieval processes, thereby shaping the entire trajectory of information processing in social contexts.Attention and Cognitive LoadDuring initial attention, schemas function as filters that prioritize schema-consistent information,...
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Schemas01:42

Schemas

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
SBAR I: Understanding the Concept01:29

SBAR I: Understanding the Concept

Effective communication among healthcare professionals during hand-off reporting is essential to delivering safe and continuous patient care. Common professional interactions include reports to healthcare team members, hand-off, and transfer reports. Nurses routinely report information to other healthcare team members and also urgently contact healthcare providers to report changes in patient status.
Standardized methods of communication have been developed to ensure that information is...
SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...

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Related Experiment Videos

Bio-jETI: a framework for semantics-based service composition.

Anna-Lena Lamprecht1, Tiziana Margaria, Bernhard Steffen

  • 1Chair for Programming Systems, Dortmund University of Technology, Dortmund, D-44227, Germany. anna-lena.lamprecht@cs.tu-dortmund.de

BMC Bioinformatics
|October 3, 2009
PubMed
Summary
This summary is machine-generated.

Semantic Web technologies and domain modeling simplify bioinformatics service integration. Bio-jETI automates interface and type matching, enabling complex workflow construction without manual intervention.

Related Experiment Videos

Area of Science:

  • Bioinformatics
  • Semantic Web Technologies
  • Computational Biology

Background:

  • Bioinformatics services are highly distributed, hindering complex analysis process development.
  • Current integration methods require manual handling of service interfaces, semantic annotations, and type incompatibilities.
  • Existing Semantic Web and workflow systems still necessitate manual intervention for service composition.

Purpose of the Study:

  • To demonstrate how Semantic Web technology and domain modeling can eliminate manual handling of interfaces, types, and inconsistencies in bioinformatics service composition.
  • To present Bio-jETI as a system that facilitates graphical combination of bioinformatics services.
  • To reduce the burden on in silico researchers when building complex, specialized analysis processes.

Main Methods:

  • Utilizing Semantic Web technology for machine-processable meta-information.
  • Implementing workflow systems for service composition.
  • Employing Bio-jETI's model checking and synthesis features for semantic-level issue resolution.
  • Developing and semantically annotating mediators to bridge type gaps.

Main Results:

  • Bio-jETI enables graphical composition of bioinformatics services, abstracting away interface details and type mismatches.
  • Automated model checking and synthesis resolve type incompatibilities or graphically indicate incorrect service combinations.
  • Users can modify compositions with semantically similar services or develop mediators for type gaps.

Conclusions:

  • Semantic annotations in a well-modeled domain empower users to orchestrate complex processes from heterogeneous services.
  • Model checking and synthesis methods eliminate concerns about interfaces and type consistency.
  • Success hinges on careful semantic annotation and its exploitation for analysis, validation, and synthesis, which are expected to become standard.