Author: claimsbot

1. A catalyst supporting honeycomb comprising:
a pillar-shaped honeycomb structure having a plurality of cells formed in parallel with one another in a longitudinal direction with a cell wall interposed therebetween; and
catalyst particles supported on the honeycomb structure,
said honeycomb structure having an integral honeycomb structure comprising a single member obtained by extrusion-molding a mixture comprising inorganic fibers and an inorganic material, said inorganic material melting at a temperature of a heat-resistant temperature of the inorganic fibers or lower, a porosity of said cell wall being about 70% or more,
said catalyst particles being configured by an oxide catalyst having an average particle diameter of at least about 0.05 \u03bcm and at most about 1.00 \u03bcm,
wherein said inorganic fibers are fixed to each other through the inorganic material at intersection portions of the inorganic fibers, and
wherein said inorganic material is present locally at said intersection portions of the inorganic fibers.
2. The catalyst supporting honeycomb according to claim 1,
wherein
either of two end portions of each of said cells is sealed.
3. The catalyst supporting honeycomb according to claim 1,
wherein
said honeycomb structure is formed by a plurality of lamination members laminated with one another in a longitudinal direction, and
said lamination members are laminated so that the cells of each lamination member are aligned with the cells of the other lamination members.
4. The catalyst supporting honeycomb according to claim 1,
wherein
said oxide catalyst is at least one member selected from the group consisting of CeO2, ZrO2, FeO2, Fe2O3, CuO, CuO2, Mn2O3, MnO, K2O, and a composite oxide represented by a composition formula AnB1-nCO3 in which A represents La, Nd, Sm, Eu, Gd or Y; B represents an alkali metal or an alkali earth metal; and C represents Mn, Co, Fe or Ni.
5. A catalyst supporting honeycomb comprising:
a pillar-shaped honeycomb structure having a plurality of cells formed in parallel with one another in a longitudinal direction with a cell wall interposed therebetween, said honeycomb structure having an integral honeycomb structure comprising a single member obtained by extrusion-molding a mixture comprising inorganic fibers and an inorganic material, said cell wall comprising inorganic fibers and an inorganic material, said inorganic material melting at a temperature of a heat-resistant temperature of the inorganic fibers or lower, a porosity of said cell wall being about 70% or more, and
oxide catalyst particles supported on the cell wall, said oxide catalyst particles being supported by flowing a gas containing a dispersed solution of a precursor of the oxide catalyst into the honeycomb structure, wherein said oxide catalyst particles have an average particle diameter of at least about 0.05 \u03bcm and at most about 1.00 \u03bcm,
wherein said inorganic fibers are fixed to each other through the inorganic material at intersection portions of the inorganic fibers, and
wherein said inorganic material is present locally at said intersection portions of the inorganic fibers.
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 process for resurfacing a monocrystalline or directionally solidified metallic piece having a length, a width and a thickness (Ws), wherein said thickness, when measured across a side wall of said metallic piece, varies between 0.2 and 2 mm along said width, said process comprising:
applying coaxially a laser beam and a flux of metallic powder to said side wall of the metallic piece and along said width, wherein the metallic powder is of a same nature as that of the metallic piece, to produce at least one layer of metal, monocrystalline or directionally solidified, on said side wall and along said width of the metallic piece,
emitting the laser beam at a power \u201cP\u201d and moving said laser beam along the metallic piece at a speed \u201cv\u201d, and
adapting a Pv ratio as a function of said thickness (Ws) of said metallic piece as said laser beam moves along said side wall of said metallic piece at said speed, wherein said adapting of said Pv ratio is performed as follows:
for a thickness Ws between 0.2 and 0.6 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 0.12 to 0.58 Wmm-1, the other taking values ranging from 0.25 to 0.84 Wmm-1;
for a thickness Ws between 0.6 and 0.8 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 0.58 to 0.83 Wmm-1, the other taking values ranging from 0.84 to 1.42 Wmm-1;
for a thickness Ws between 0.8 and 1 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 0.83 to 1.08 Wmm-1, the other taking values ranging from 1.42 to 2.05 Wmm-1;
for a thickness Ws between 1 and 1.2 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 1.08 to 1.27 Wmm-1, the other taking values ranging from 2.05 to 2.34 Wmm-1;
for a thickness Ws between 1.2 and 1.4 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 1.27 to 1.33 Wmm-1, the other taking values ranging from 2.34 to 2.48 Wmm-1;
for a thickness Ws between 1.4 and 2 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, a constant one equal to 1.33 Wmm-1, the other taking values ranging from 2.48 to 2.75 Wmm-1,
wherein the laser beam and the metallic powder are applied by a projection nozzle, and wherein said adapting of said Pv ratio is performed automatically via a program that includes predetermined information about an evolution of said thickness (Ws) along said side wall and that automatically controls said Pv ratio as a function of a position of said nozzle along said side wall of the metallic piece.
2. The process as claimed in claim 1, wherein the portions of Pv vs Ws curves are portions of straight lines.
3. The process as claimed in claim 1, further comprising pre-heating the piece prior to applying the flux of powder.
4. The process as claimed in claim 3, wherein the pre-heating is carried out by laser beam.
5. The process as claimed in claim 1, wherein applying the flux of powder is carried out without pre-heating of the piece.
6. The process as claimed in claim 1, further comprising fabricating successively several layers of metal on top of each other, wherein the energy applied along the metallic piece to all the layers is the same.
7. The process as claimed in claim 6, wherein a same Pv ratio is maintained for the layers to which the same energy is applied.
8. The process as claimed in claim 1, further comprising producing successively several layers of metal on top of each other, wherein the energy of a first layer is less than the energy of subsequent layers.
9. The process as claimed in claim 8, wherein a same Pv ratio is maintained for the layers to which the same energy is applied.
10. The process as claimed in claim 1, wherein the metallic piece and the metallic powder comprise a monocrystalline alloy known as AM1.
11. The process as claimed in claim 1, wherein the laser beam is a YAG laser beam.
12. The process as claimed in claim 1, wherein the laser beam and the metallic powder are applied by a projection nozzle, comprising a truncated end portion which comprises a central bore for passage of the laser beam and channels, extending in the generation wall of its truncated end, for supplying the powder.
13. The process as claimed in claim 1, wherein the metallic piece is a gas turbine motor blade.
14. The process as claimed in claim 1, wherein said adapting of said Pv ratio is performed as follows:
for a thickness Ws between 0.2 and 0.6 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 0.125 to 0.58 Wmm-1, the other taking values ranging from 0.25 to 0.833 Wmm-1,
for a thickness Ws between 0.6 and 0.8 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 0.583 to 0.83 Wmm-1, the other taking values ranging from 0.84 to 1.417 Wmm-1,
for a thickness Ws between 0.8 and 1 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 0.833, to 1.08 Wmm-1, the other taking values ranging from 1.42, to 2.042 Wmm-1;
for a thickness Ws between 1 and 1.2 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 1.083, to 1.27 Wmm-1, the other taking values ranging from 2.05 to 2.333 Wmm-1,
for a thickness Ws between 1.2 and 1.4 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 1.271 to 1.33 Wmm-1, the other taking values ranging from 2.34 to 2.479 Wmm-1,
for a thickness Ws between 1.4 and 2 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, a constant one equal to 1.333 Wmm-1, the other taking values ranging from 2.48 to 2.75 Wmm-1.
15. A process for resurfacing a monocrystalline or directionally solidified metallic piece having a length, a width and a thickness (Ws), wherein said thickness, when measured across a side wall of said metallic piece, varies between 0.2 and 2 mm along said width, said process comprising:
applying coaxially a laser beam and a flux of metallic powder to said side wall of the metallic piece and along said width, wherein the metallic powder is of a same nature as that of the metallic piece, to produce at least one layer of metal, monocrystalline or directionally solidified, on said side wall and along said width of the metallic piece,
emitting the laser beam at a power \u201cP\u201d and moving said laser beam along the metallic piece at a speed \u201cv\u201d, and
adapting a Pv ratio as a function of said thickness (Ws) of said metallic piece as said laser beam moves along said side wall of said metallic piece at said speed, wherein said adapting of said Pv ratio is performed as follows:
for a thickness Ws between 0.2 and 0.6 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 0.12 to 0.58 Wmm-1, the other taking values ranging from 0.25 to 0.84 Wmm-1;
for a thickness Ws between 0.6 and 0.8 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 0.58 to 0.83 Wmm-1, the other taking values ranging from 0.84 to 1.42 Wmm-1;
for a thickness Ws between 0.8 and 1 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 0.83 to 1.08 Wmm-1, the other taking values ranging from 1.42 to 2.05 Wmm-1;
for a thickness Ws between 1 and 1.2 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 1.08 to 1.27 Wmm-1, the other taking values ranging from 2.05 to 2.34 Wmm-1;
for a thickness Ws between 1.2 and 1.4 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, one taking values ranging from 1.27 to 1.33 Wmm-1, the other taking values ranging from 2.34 to 2.48 Wmm-1;
for a thickness Ws between 1.4 and 2 mm, maintaining said Pv ratio between two portions of increasing Pv vs Ws curves, a constant one equal to 1.33 Wmm-1, the other taking values ranging from 2.48 to 2.75 Wmm-1,
wherein said adapting of said Pv ratio is performed by an auto-regulator that controls said Pv ratio in real time as a function of data obtained by measuring in real time a property that depends on said thickness (Ws) at any given point along said side wall of said metallic piece.
16. The process as claimed in claim 15, wherein said property is luminosity and said measuring is performed by a photodiode connected to said auto-regulator.

1. A method comprising:
extracting, by a computer processor of a computing system from a plurality of documents, content, wherein said extracting said content comprises:
performing, by said computer processor, a text analytics process with respect to said plurality of documents;
presenting, by said computer processor, a first screen interface comprising a metrics search menu, wherein said first screen interface presents a menu indicating a hierarchal order for various metrics;
enabling, by said first screen interface, a metrics search;
identifying, by said computer processor based on results of said text analytics process and said metrics search, extraction patterns associated with said content, wherein said extraction patterns comprise metrics and business rules comprising key content types that require sharing among peers;
presenting, by said computer processor, a second screen interface comprising a concept search menu, wherein said second screen interface presents a menu indicating a hierarchal order for business processes, concepts, metrics, and rules;
identifying, by said computer processor based on results of said text analytics process and a concept search enabled by said second screen interface, concepts from glossaries;
extracting, by said computer processor, said concepts from said content in accordance with said extraction patterns; and
determining, by said computer processor based on results of comparing said concepts to a plurality of specified concepts, new concepts of said concepts;

presenting, by said computer processor, a third screen interface enabling a process for exporting, importing, and merging metrics, wherein said third screen interface presents a menu indicating identified content sources and content details associated with said content;
enabling, by said third screen interface, said process for exporting, importing, and merging metrics;
publishing, by said computer processor based on said enabling said process for exporting, importing, and merging metrics, said content in external glossaries;
publishing, by said computer processor, said content arranged in a business content hierarchy in a specified format;
enabling, by said computer processor, said business content hierarchy in a plurality of business related projects; and
arranging, by said computer processor, updated content associated with said content and said business content hierarchy in said updated business content hierarchy.
2. The method of claim 1, wherein said publishing said business content hierarchy comprises:
translating, by said computer processor, an XML version of said business content hierarchy into a DHTML format using an XSL process; and
displaying, by said computer processor, said content as a tree structure or an alphabetical structure.
3. The method of claim 1, wherein said publishing said business content hierarchy comprises:
translating, by said computer processor, an XML version of said business content hierarchy into a DHTML format using an XSL process; and
generating, by said computer processor, a document comprising said content presented as a tree structure or an alphabetical structure.
4. The method of claim 1, wherein said publishing said enabling said business content hierarchy in said plurality of projects comprises:
performing a read only operation, by said computer processor, associated with an XML version of said business content hierarchy; and
retrieving, by said computer processor, details associated with said content.
5. The method of claim 4, wherein said read only operation comprises an operation selected from the group consisting of searching, zooming, and highlighting.
6. The method of claim 1, wherein said updating said content and said business content hierarchy comprises:
loading, by said computer processor, a selected file associated with said content;
translating, by said computer processor, an XML version of said selected file into a DHTML format using an XSL process; and
importing, by said computer processor, additional content associated with said selected file.
7. The method of claim 1, wherein said updating said content and said business content hierarchy comprises:
loading, by said computer processor, selected node content associated with said content;
applying, by said computer processor, pagination to said selected node content;
presenting, by said computer processor via an explorer export window, business content of said selected node content;
receiving, by said computer processor from a user, a selection for specific content of said business content;
exporting, by said computer processor to an external location, said specific content.
8. The method of claim 1, wherein said updating said content and said business content hierarchy comprises:
loading, by said computer processor, a selected file associated with said content;
translating, by said computer processor, an XML version of said selected file into a DHTML format using an XSL process; and
adding, by said computer processor, additional content associated with said selected file.
9. The method of claim 1, further comprising:
providing a process for supporting a computer infrastructure, said process comprising providing at least one support service for at least one of creating, integrating, hosting, maintaining, and deploying computer-readable code in the computing system, wherein the code in combination with the computing system performs the method of claim 1.
10. A computing system comprising a computer processor coupled to a computer-readable memory unit, said memory unit comprising instructions that when executed by the computer processor implements a method comprising:
extracting, by said computer processor from a plurality of documents, content, wherein said extracting said content comprises:
performing, by said computer processor, a text analytics process with respect to said plurality of documents;
presenting, by said computer processor, a first screen interface comprising a metrics search menu, wherein said first screen interface presents a menu indicating a hierarchal order for various metrics;
enabling, by said first screen interface, a metrics search;
identifying, by said computer processor based on results of said text analytics process and said metrics search, extraction patterns associated with said content, wherein said extraction patterns comprise metrics and business rules comprising key content types that require sharing among peers;
presenting, by said computer processor, a second screen interface comprising a concept search menu, wherein said second screen interface presents a menu indicating a hierarchal order for business processes, concepts, metrics, and rules;
identifying, by said computer processor based on results of said text analytics process and a concept search enabled by said second screen interface, concepts from glossaries;
extracting, by said computer processor, said concepts from said content in accordance with said extraction patterns; and
determining, by said computer processor based on results of comparing said concepts to a plurality of specified concepts, new concepts of said concepts;

presenting, by said computer processor, a third screen interface enabling a process for exporting, importing, and merging metrics, wherein said third screen interface presents a menu indicating identified content sources and content details associated with said content;
enabling, by said third screen interface, said process for exporting, importing, and merging metrics;
publishing, by said computer processor based on said enabling said process for exporting, importing, and merging metrics, said content in external glossaries;
publishing, by said computer processor, said content arranged in a business content hierarchy in a specified format;
enabling, by said computer processor, said business content hierarchy in a plurality of business related projects; and
arranging, by said computer processor, updated content associated with said content and said business content hierarchy in said updated business content hierarchy.
11. The computing system of claim 10, wherein said publishing said business content hierarchy comprises:
translating, by said computer processor, an XML version of said business content hierarchy into a DHTML format using an XSL process; and
displaying, by said computer processor, said content as a tree structure or an alphabetical structure.
12. The computing system of claim 10, wherein said publishing said business content hierarchy comprises:
translating, by said computer processor, an XML version of said business content hierarchy into a DHTML format using an XSL process; and
generating, by said computer processor, a document comprising said content presented as a tree structure or an alphabetical structure.
13. The computing system of claim 10, wherein said publishing said enabling said business content hierarchy in said plurality of projects comprises:
performing a read only operation, by said computer processor, associated with an XML version of said business content hierarchy; and
retrieving, by said computer processor, details associated with said content.
14. The computing system of claim 13, wherein said read only operation comprises an operation selected from the group consisting of searching, zooming, and highlighting.
15. The computing system of claim 10, wherein said updating said content and said business content hierarchy comprises:
loading, by said computer processor, a selected file associated with said content;
translating, by said computer processor, an XML version of said selected file into a DHTML format using an XSL process; and
importing, by said computer processor, additional content associated with said selected file.
16. The computing system of claim 10, wherein said updating said content and said business content hierarchy comprises:
loading, by said computer processor, selected node content associated with said content;
applying, by said computer processor, pagination to said selected node content;
presenting, by said computer processor via an explorer export window, business content of said selected node content;
receiving, by said computer processor from a user, a selection for specific content of said business content;
exporting, by said computer processor to an external location, said specific content.
17. The computing system of claim 10, wherein said updating said content and said business content hierarchy comprises:
loading, by said computer processor, a selected file associated with said content;
translating, by said computer processor, an XML version of said selected file into a DHTML format using an XSL process; and
adding, by said computer processor, additional content associated with said selected file.
18. A computer program product, comprising a computer readable hardware storage device storing a computer readable program code, said computer readable program code comprising an algorithm that when executed by a computer processor of a computing system implements a method, said method comprising:
extracting, by said computer processor from a plurality of documents, content, wherein said extracting said content comprises:
performing, by said computer processor, a text analytics process with respect to said plurality of documents;
presenting, by said computer processor, a first screen interface comprising a metrics search menu, wherein said first screen interface presents a menu indicating a hierarchal order for various metrics;
enabling, by said first screen interface, a metrics search;
identifying, by said computer processor based on results of said text analytics process and said metrics search, extraction patterns associated with said content, wherein said extraction patterns comprise metrics and business rules comprising key content types that require sharing among peers;
presenting, by said computer processor, a second screen interface comprising a concept search menu, wherein said second screen interface presents a menu indicating a hierarchal order for business processes, concepts, metrics, and rules;
identifying, by said computer processor based on results of said text analytics process and a concept search enabled by said second screen interface, concepts from glossaries;
extracting, by said computer processor, said concepts from said content in accordance with said extraction patterns; and
determining, by said computer processor based on results of comparing said concepts to a plurality of specified concepts, new concepts of said concepts;

presenting, by said computer processor, a third screen interface enabling a process for exporting, importing, and merging metrics, wherein said third screen interface presents a menu indicating identified content sources and content details associated with said content;
enabling, by said third screen interface, said process for exporting, importing, and merging metrics;
publishing, by said computer processor based on said enabling said process for exporting, importing, and merging metrics, said content in external glossaries;
publishing, by said computer processor, said content arranged in a business content hierarchy in a specified format;
enabling, by said computer processor, said business content hierarchy in a plurality of business related projects; and
arranging, by said computer processor, updated content associated with said content and said business content hierarchy in said updated business content hierarchy.
19. The computer program product of claim 18, wherein said publishing said business content hierarchy comprises:
translating, by said computer processor, an XML version of said business content hierarchy into a DHTML format using an XSL process; and
displaying, by said computer processor, said content as a tree structure or an alphabetical structure.
20. The computer program product of claim 18, wherein said publishing said business content hierarchy comprises:
translating, by said computer processor, an XML version of said business content hierarchy into a DHTML format using an XSL process; and
generating, by said computer processor, a document comprising said content presented as a tree structure or an alphabetical structure.
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 method comprising:
starting an automatic speech recognition session for a phone call initiated from a communication device;
determining that the communication device is associated with a plurality of users;
identifying a group of selected speech recognition models comprising a speaker independent model and a plurality of speaker dependent models;
recognizing an utterance received from a particular user in the plurality of users using each model in the group of selected speech recognition models in parallel, to yield a group of recognition results;
selecting a dominant speech model from the group of selected speech recognition models using a heuristic search algorithm, to yield a selected dominant speech model, wherein the dominant speech model is an efficient model in the group of selected speech recognition models; and
continuously using the selected dominant speech model to recognize speech received from the particular user for a remainder of the automatic speech recognition session.
2. The method of claim 1, further comprising dropping a speech model from the group of selected speech recognition models when recognition accuracy is below a threshold.
3. The method of claim 1, further comprising selecting the plurality of speaker dependent models based on the communication device.
4. The method of claim 3, further comprising selecting the plurality of speaker dependent models based on the plurality of users associated with the communication device.
5. The method of claim 3, further comprising receiving additional utterances from the communication device and clustering the additional utterances to generate a new speaker dependent model.
6. The method of claim 1, further comprising iteratively generating the group of selected models, recognizing the utterance, and selecting the dominant speech model, each time a new automatic speech recognition session is initiated.
7. A system comprising:
a processor;
a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising:
starting an automatic speech recognition session for a phone call initiated from a communication device;
determining that the communication device is associated with a plurality of users;
identifying a group of selected speech recognition models comprising a speaker independent model and a plurality of speaker dependent models;
recognizing an utterance received from a particular user in the plurality of users using each model in the group of selected speech recognition models in parallel, to yield a group of recognition results;
selecting a dominant speech model from the group of selected speech recognition models using a heuristic search algorithm, to yield a selected dominant speech model, wherein the dominant speech model is an efficient model in the group of selected speech recognition models; and
continuously using the selected dominant speech model to recognize speech received from the particular user for a remainder of the automatic speech recognition session.
8. The system of claim 7, the computer-readable storage medium having additional instructions stored which, when executed by the processor, result in operations comprising dropping a speech model from the group of selected speech recognition models when recognition accuracy is below a threshold.
9. The system of claim 7, the computer-readable storage medium having additional instructions which, when executed by the processor, result in operations comprising selecting the plurality of speaker dependent models based on the communication device.
10. The system of claim 9, the computer-readable storage medium having additional instructions which, when executed by the processor, result in operations comprising selecting the plurality of speaker dependent models based on the plurality of users associated with the communication device.
11. The system of claim 9, the computer-readable storage medium having additional instructions which, when executed by the processor, result in operations comprising:
receiving utterances from the communication device; and
clustering the utterances to generate a new speaker dependent model.
12. The system of claim 7, the computer-readable storage medium storing additional instructions which, when executed by the processor, result in operations comprising iteratively generating the group of selected speech recognition models, recognizing the utterance, and selecting the dominant speech model, each time a new automatic speech recognition session is initiated.
13. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising:
starting an automatic speech recognition session for a phone call initiated from a communication device;
determining that the communication device is associated with a plurality of users;
identifying a group of selected speech recognition models comprising a speaker independent model and a plurality of speaker dependent models;
recognizing an utterance received from a particular user in the plurality of users using each model in the group of selected speech recognition models in parallel, to yield a group of recognition results;
selecting a dominant speech model from the group of selected speech recognition models using a heuristic search algorithm, to yield a selected dominant speech model, wherein the dominant speech model is an efficient model in the group of selected speech recognition models; and
continuously using the selected dominant speech model to recognize speech received from the particular user for a remainder of the automatic speech recognition session.
14. The computer-readable storage device of claim 13, having additional instructions stored which, when executed by the computing device, result in operations comprising dropping a speech model from the group of selected speech recognition models when recognition accuracy is below a threshold.
15. The computer-readable storage device of claim 13, having additional instructions stored which, when executed by the computing device, result in operations comprising:
selecting the plurality of speaker dependent models based on the plurality of users associated with the communication device.