1. A process for increasing toughness of glass particulates, the process comprising:
providing a glass particulate, the glass particulate generally in the form of a sphere;
heating the glass particulate to a temperature greater than 600\xb0 C. for a predetermined time; and
cooling the glass particulate to ambient temperature;
wherein heating the glass particulate alters the failure mechanism of the glass particulate from a high energy failure that produces generally fine powder to a lower energy failure that produces generally large fragments.
2. The process of claim 1, wherein the glass particulate is provided by fusing and quenching an aluminosilicate mineral.
3. The process of claim 2, wherein the aluminosilicate mineral is selected from the group consisting of rhyolite, theoliite, olivine, basalt, andesite and combinations thereof.
4. The process of claim 1, wherein the glass particulate is an amorphous glass particulate before heating the glass particulate to a temperature greater than 600\xb0 C. for a predetermined time.
5. The process of claim 1, wherein the step of heating the glass particulate is heating to a temperature between 700\xb0 C. and 1050\xb0 C., inclusive.
6. The process of claim 1, wherein heating the glass particulate nucleates and grows a crystalline phase within an amorphous glass matrix of the glass particulate.
7. The process of claim 6, wherein the glass particulate contains between 0.5-4.5 weight percent (wt %) Fe2O3, 3-13 wt % FeO and the crystalline phase is an iron oxide.
8. The process of claim 7, wherein the iron oxide is magnetite.
9. The process of claim 1, wherein the step of heating the glass particulate produces a glass particulate with a fracture toughness at least 10% greater than a glass particulate that has not been heated.
10. The process of claim 1, wherein the step of heating the glass particulate produces a glass particulate with a fracture toughness at least 25% greater than a glass particulate that has not been heated.
11. The process of claim 1, wherein the step of heating the glass particulate produces a glass particulate with a fracture toughness at least 50% greater than a glass particulate that has not been heated.
12. A process for producing proppants, the process comprising:
providing an amorphous glass particulate derived by fusing and quenching an aluminosilicate mineral, the amorphous glass particulate generally in the form of a sphere and the aluminosilicate mineral selected from the group consisting of rhyolite, theoliite, olivine, basalt, andesite and combinations thereof;
heating the glass particulate to a temperature greater than 600\xb0 C. for a predetermined time such that a crystalline phase nucleates and grows within the amorphous glass particulate and produces a devitrified glass particulate; and
cooling the devitrified glass particulate to ambient temperature;
wherein heating the devitrified glass particulate has an altered failure mechanism compared to the amorphous glass particulate, the altered failure mechanism providing a low energy failure of that produces generally large fragments from the devitrified glass particulate compared to a higher energy failure that produces generally fine powder from the amorphous glass particulate.
13. The process of claim 12, wherein the step of heating the glass particulate is heating to a temperature between 700\xb0 C. and 1050\xb0 C., inclusive.
14. The process of claim 12, wherein the crystalline phase that nucleates and grows within the amorphous glass particulate and produces a devitrified glass particulate is an iron oxide.
15. The process of claim 14, wherein the amorphous glass particulate contains between 0.5-4.5 weight percent (wt %) Fe2O3, 3-13 wt % FeO and the iron oxide is magnetite.
16. The process of claim 12, wherein the devitrified glass particulate has a fracture toughness at least 10% greater than a fracture toughness of the amorphous glass particulate.
17. The process of claim 12, wherein the devitrified glass particulate has a fracture toughness at least 25% greater than a fracture toughness of the amorphous glass particulate.
18. The process of claim 12, wherein the devitrified glass particulate has a fracture toughness at least 50% greater than a fracture toughness of the amorphous glass particulate.
19. A proppant made from glass and having improved failure properties, said proppant comprising:
a glass particulate having a generally spherical shape with a within the range of 250 to 5000 microns;
said glass particulate having a Vickers indentation fracture resistance (VIFR) greater than 1.2 MPa\xb7\u221a{square root over (m)};
wherein said VIFR is determined by the expression:
VIFR=0.1706(H\xb7\u221a{square root over (a)})\xb7Log(4.5ac)
where:
H is a Vickers hardness value of said glass particulate;
a is a diagonal length of an indentation produced from a Vickers hardness test; and
c is a crack length extending from the indentation produced from the Vickers hardness test.
20. The proppant of claim 18, wherein said VIFR is greater than 1.5 MPa\xb7\u221a{square root over (m)}.
21. The proppant of claim 19, wherein said VIFR is greater than 1.8 MPa\xb7\u221a{square root over (m)}.
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:
receiving two or more variations of an underlying piece of content, each variation including metadata;
using a text alignment technique to correlate the metadata of the two or more variations; and
merging multiple sets of the metadata into one multi-track set from the correlation.
2. The method of claim 1 wherein the content includes one or more of digital text, digital audio and digital video.
3. The method of claim 1 wherein the text alignment technique is a dynamic programming process optimizing a metric.
4. The method of claim 3 wherein the metric is a metric that minimizes a number of word substitutions, insertions and deletions.
5. The method of claim 3 wherein the metric is a metric that weights different words differently.
6. The method of claim 3 wherein the metric assigns different penalties to different errors and minimizes a total weighted penalty.
7. The method of claim 3 wherein the metric is calculated in conjunction with natural language processing.
8. The method of claim 3 wherein the metric is calculated using a Viterbi dynamic programming process for finding the most likely sequence of hidden states.
9. The method of claim 1 wherein receiving two or more variations of the underlying piece of content further comprises applying pattern-based normalization on the two or more variations.
10. The method of claim 9 wherein applying pattern-based normalization comprises removing time stamps from closed-captioning.
11. The method of claim 1 wherein the one multi-track set includes external non-aligned metadata.
12. The method of claim 11 wherein the external non-aligned metadata is selected based on aligned metadata.
13. The method of claim 1 wherein the content is digital audio.
14. The method of claim 13 wherein speech-to-text is performed on the digital audio.
15. The method of claim 1 wherein the text alignment technique comprises text aligning to one or more time alignments to align the metadata of the two or more variations.
16. An apparatus comprising:
a local computing system linked to a network of interconnected computer systems, the local computing system comprising a processor, a memory and a storage device;
the memory comprising an operating system and a metadata alignment process, the metadata alignment process comprising:
receiving two or more variations of an underlying piece of content, each piece of content including metadata;
using a text alignment technique to correlate the metadata of the two or more variations; and
merging multiple sets of the metadata into one multi-track set from the correlation.
17. The apparatus of claim 16 wherein the content includes one or more of digital text, digital audio and digital video.
18. The apparatus of claim 16 wherein the text alignment technique is a dynamic programming process optimizing a metric.
19. The apparatus of claim 18 wherein the metric is a metric that minimizes a number of word substitutions, insertions and deletions.
20. The apparatus of claim 18 wherein the metric is a metric that weights different words differently.
21. The apparatus of claim 18 wherein the metric is calculated in conjunction with natural language processing.
22. The apparatus of claim 18 wherein the metric is calculated using a Viterbi dynamic programming process for finding the most likely sequence of hidden states.
23. The apparatus of claim 16 wherein receiving two variations of the underlying piece of content further comprises applying pattern-based normalization on the two variations.
24. The apparatus of claim 23 wherein applying pattern-based normalization comprises removing time stamps from closed-captioning.
25. The apparatus of claim 16 wherein the one multi-track set includes external non-aligned metadata.
26. The apparatus of claim 25 wherein the external non-aligned metadata is selected based on aligned metadata.
27. The apparatus of claim 16 wherein the content is digital audio.
28. The apparatus of claim 27 wherein speech-to-text is performed on the digital audio.
29. A method comprising:
receiving variations of an underlying piece of content, each piece of content including metadata;
using a text alignment technique to correlate the metadata of a first variation to a third variation, the correlated metadata including timestamps;
using the text alignment technique to correlate the metadata of a second variation to the third variation, the correlated metadata including timestamps; and
merging the correlated metadata into one multi-track set.
30. The method of claim 29 wherein the content includes one or more of digital text, digital audio and digital video.
31. The method of claim 29 wherein the text alignment technique is a dynamic programming process optimizing a metric.
32. The method of claim 29 wherein the content is digital audio.
33. The method of claim 32 wherein speech-to-text is performed on the digital audio.