1. A structure comprising:
a layer of silicon oxide;
a plurality of synthesis sites, each of the synthesis sites including a seed pad and a spacer peripherally encircling the seed pad, the seed pad composed of a first material that supports carbon nanotube growth and the spacer composed of a second material that does not support the carbon nanotube growth; and
a plurality of carbon nanotubes, each of said plurality of carbon nanotubes including a first end embedded in said layer of silicon oxide and a second end in a contacting relationship with said seed pad of a respective one of said plurality of synthesis sites.
2. The structure of claim 1 wherein said seed pad of each of the plurality of synthesis sites is sized to support synthesis of one of said plurality of carbon nanotubes.
3. The structure of claim 1 wherein each of said plurality of carbon nanotubes is carried on said seed pad of a corresponding one of the plurality of synthesis sites.
4. The structure of claim 1 wherein said plurality of carbon nanotubes are multi-wall carbon nanotubes.
5. The structure of claim 1 wherein said plurality of carbon nanotubes have a substantially uniform length between said first and second ends.
6. The structure of claim 1 wherein said first material is selected from the group consisting of iron, nickel, cobalt, and compounds and alloys thereof.
7. The structure of claim 6 wherein said spacer is configured to prevent synthesis of said plurality of carbon nanotubes in a direction other than between said layer of silicon oxide and said seed pad.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A non-transitory computer program product, tangibly embodied in a machine readable storage device, comprising one or more instructions that, when executed, cause at least one processor to perform knowledge based search operations comprising:
aggregate, prepare and store a plurality of data, a plurality of definitions and two relevance criteria where said data and definitions comprise at least one entity, one entity function and one or more entity function measures where said data and definitions are stored in one or more categories,
transform said data into an entity knowledge by learning from the data,
use the relevance criteria and the entity knowledge to identify one or more of the categories as a source of data for any query,
receive a search request from the subject entity, and
provide a plurality of search results from the identified categories in response to said request by using two relevance measures to identify and prioritize the results where the entity knowledge comprises a graph.
2. The computer program product of claim 1, wherein the search results comprise a plurality of health related data.
3. The computer program product of claim 1, wherein the entity is selected from the group consisting of team, group, department, division, company, organization or multi-entity organization.
4. The computer program product of claim 1, wherein the entity is selected from the group consisting of patient or patient-entity system.
5. The computer program product of claim 1, wherein the search request is received from a browser.
6. The computer program product of claim 1, wherein the two relevance criteria consist of a node depth and an impact cutoff and the two relevance measures are selected from the group consisting of ontology alignment measures, semantic alignment measures, cover density rankings, vector space model measurements, okapi similarity measurements, three level relevance scores and hypertext induced topic selection algorithm scores.
7. The computer program product of claim 1, wherein the search request comprises one or more keywords or a question where said search request is received from a natural language interface or an anticipated need for data automatically initiates the search request.
8. The computer program product of claim 1, wherein the at least one processor comprises at least one processor in a computer, at least one processor in a mobile access device or a combination thereof.
9. A knowledge based search system, comprising:
a computer with one or more processors having circuitry to execute instructions; a storage device available to said processor with sequences of instructions stored therein, which when executed cause the one or more processors to:
aggregate, prepare and store a plurality of data, a plurality of definitions and two relevance criteria where said data and definitions comprise at least one entity, one entity function and one or more entity function measures where said data and definitions are stored in one or more categories,
transform said data into an entity knowledge by learning from the data,
use the relevance criteria and the entity knowledge to identify one or more of the categories as a source of data for any query,
receive a search request from the subject entity, and
provide a plurality of results from the identified categories in response to said query by using two relevance measures to identify and prioritize the results where the entity knowledge comprises a graph.
10. The system of claim 9, wherein the search results comprise a plurality of health related data.
11. The system of claim 9, wherein the entity is selected from the group consisting of team, group, department, division, company, organization or multi-entity organization.
12. The system of claim 9, wherein the entity is selected from the group consisting of patient or patient-entity system.
13. The system of claim 9, wherein the search request is received from a browser.
14. The system of claim 9, wherein the two relevance criteria consist of a node depth and an impact cutoff and the two relevance measures are selected from the group consisting of ontology alignment measures, semantic alignment measures, cover density rankings, vector space model measurements, okapi similarity measurements, three level relevance scores and hypertext induced topic selection algorithm scores.
15. The system of claim 9, wherein the search request comprises one or more keywords or a question where said search request is received from a natural language interface or an anticipated need for data automatically initiates the search request.
16. The system of claim 9, wherein the at least one processor comprises at least one processor in a computer, at least one processor in a mobile access device or a combination thereof.
17. A knowledge based search method, comprising:
using a computer to:
aggregate, prepare and store a plurality of data, a plurality of definitions and two relevance criteria where said data and definitions comprise at least one entity, one entity function and one or more entity function measures where said data and definitions are stored in one or more categories,
transform said data into an entity knowledge by learning from the data,
use the relevance criteria and the entity knowledge to identify one or more of the categories as a source of data for any query,
receive a search request from the subject entity, and
provide a plurality of results from the identified categories in response to said query by using two relevance measures to identify and prioritize the results where the entity knowledge comprises a graph.
18. The system of claim 17, wherein the entity is selected from the group consisting of team, group, department, division, company, organization or multi-entity organization.
19. The system of claim 17, wherein the entity is selected from the group consisting of patient or patient-entity system.
20. The system of claim 17, wherein the two relevance criteria consist of a node depth and an impact cutoff and the two relevance measures are selected from the group consisting of ontology alignment measures, semantic alignment measures, cover density rankings, vector space model measurements, okapi similarity measurements, three level relevance scores and hypertext induced topic selection algorithm scores.