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6.895 Computational Biology: Genomes, Networks, Evolution (MIT) 6.895 Computational Biology: Genomes, Networks, Evolution (MIT)

Description

This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution. This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution.

Subjects

Genomes: Biological sequence analysis | Genomes: Biological sequence analysis | hidden Markov models | hidden Markov models | gene finding | gene finding | RNA folding | RNA folding | sequence alignment | sequence alignment | genome assembly | genome assembly | Networks: Gene expression analysis | Networks: Gene expression analysis | regulatory motifs | regulatory motifs | graph algorithms | graph algorithms | scale-free networks | scale-free networks | network motifs | network motifs | network evolution | network evolution | Evolution: Comparative genomics | Evolution: Comparative genomics | phylogenetics | phylogenetics | genome duplication | genome duplication | genome rearrangements | genome rearrangements | evolutionary theory | evolutionary theory | rapid evolution | rapid evolution

License

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18.417 Introduction to Computational Molecular Biology (MIT) 18.417 Introduction to Computational Molecular Biology (MIT)

Description

This course introduces the basic computational methods used to understand the cell on a molecular level. It covers subjects such as the sequence alignment algorithms: dynamic programming, hashing, suffix trees, and Gibbs sampling. Furthermore, it focuses on computational approaches to: genetic and physical mapping; genome sequencing, assembly, and annotation; RNA expression and secondary structure; protein structure and folding; and molecular interactions and dynamics. This course introduces the basic computational methods used to understand the cell on a molecular level. It covers subjects such as the sequence alignment algorithms: dynamic programming, hashing, suffix trees, and Gibbs sampling. Furthermore, it focuses on computational approaches to: genetic and physical mapping; genome sequencing, assembly, and annotation; RNA expression and secondary structure; protein structure and folding; and molecular interactions and dynamics.

Subjects

basic computational methods cell on a molecular level | basic computational methods cell on a molecular level | sequence alignment algorithms | sequence alignment algorithms | dynamic programming | dynamic programming | hashing | hashing | suffix trees | suffix trees | Gibbs sampling | Gibbs sampling | genetic and physical mapping | genetic and physical mapping | genome sequencing | genome sequencing | assembly | assembly | and annotation | and annotation | RNA expression and secondary structure | RNA expression and secondary structure | protein structure and folding | protein structure and folding | and molecular interactions and dynamics | and molecular interactions and dynamics | annotation | annotation | molecular interactions and dynamics | molecular interactions and dynamics

License

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6.047 Computational Biology: Genomes, Networks, Evolution (MIT) 6.047 Computational Biology: Genomes, Networks, Evolution (MIT)

Description

This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include: Genomes: biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly Networks: gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution Evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include: Genomes: biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly Networks: gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution Evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution

Subjects

computational biology | computational biology | algorithms | algorithms | machine learning | machine learning | biology | biology | biological datasets | biological datasets | genomics | genomics | proteomics | proteomics | genomes | genomes | sequence analysis | sequence analysis | sequence alignment | sequence alignment | genome assembly | genome assembly | network motifs | network motifs | network evolution | network evolution | graph algorithms | graph algorithms | phylogenetics | phylogenetics | comparative genomics | comparative genomics | python | python | probability | probability | statistics | statistics | entropy | entropy | information | information

License

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17.263 U.S. National Elections (MIT) 17.263 U.S. National Elections (MIT)

Description

This course provides a selective overview of electoral politics in the United States, with an emphasis on presidential and congressional elections. It examines the macro-level determinants of electoral outcomes as well as the political behavior of individual Americans. Each week covers a different topic, with readings designed to highlight controversies or debates in the political science literature. This course provides a selective overview of electoral politics in the United States, with an emphasis on presidential and congressional elections. It examines the macro-level determinants of electoral outcomes as well as the political behavior of individual Americans. Each week covers a different topic, with readings designed to highlight controversies or debates in the political science literature.

Subjects

election | election | barack obama | barack obama | mitt romney | mitt romney | hillary clinton | hillary clinton | political geography | political geography | realignment | realignment | political parties | political parties | democrat | democrat | republican | republican | incumbency advantage | incumbency advantage | electoral college | electoral college | partisan | partisan | demographics | demographics | campaigns | campaigns | constituencies | constituencies | voters | voters | voting | voting | gridlock | gridlock | campaign finance reform | campaign finance reform | lobbying | lobbying | campaign spending | campaign spending | citizens united | citizens united | referendums | referendums | turnout | turnout | representation | representation | governance | governance | government | government | inequality | inequality | gerrymandering | gerrymandering | redistricting | redistricting | policy | policy

License

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7.91J Foundations of Computational and Systems Biology (MIT) 7.91J Foundations of Computational and Systems Biology (MIT)

Description

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas. This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

Subjects

7.91 | 7.91 | 20.490 | 20.490 | 20.390 | 20.390 | 7.36 | 7.36 | 6.802 | 6.802 | 6.874 | 6.874 | HST.506 | HST.506 | computational biology | computational biology | systems biology | systems biology | bioinformatics | bioinformatics | artificial intelligence | artificial intelligence | sequence analysis | sequence analysis | proteomics | proteomics | sequence alignment | sequence alignment | protein folding | protein folding | structure prediction | structure prediction | network modeling | network modeling | phylogenetics | phylogenetics | pairwise sequence comparisons | pairwise sequence comparisons | ncbi | ncbi | blast | blast | protein structure | protein structure | dynamic programming | dynamic programming | genome sequencing | genome sequencing | DNA | DNA | RNA | RNA | x-ray crystallography | x-ray crystallography | NMR | NMR | homologs | homologs | ab initio structure prediction | ab initio structure prediction | DNA microarrays | DNA microarrays | clustering | clustering | proteome | proteome | computational annotation | computational annotation

License

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ESD.60 Lean/Six Sigma Processes (MIT) ESD.60 Lean/Six Sigma Processes (MIT)

Description

Students of this course will develop a broad understanding of Lean/Six Sigma principles and practices, build capability to implement Lean/Six Sigma initiatives in manufacturing operations, and learn to operate with awareness of Lean/Six Sigma at the enterprise level. All course materials are organized around a common "single-point lesson" (SPL) format, with some of the SPLs provided by the instructor and guests and with some developed and delivered by student teams. Students of this course will develop a broad understanding of Lean/Six Sigma principles and practices, build capability to implement Lean/Six Sigma initiatives in manufacturing operations, and learn to operate with awareness of Lean/Six Sigma at the enterprise level. All course materials are organized around a common "single-point lesson" (SPL) format, with some of the SPLs provided by the instructor and guests and with some developed and delivered by student teams.

Subjects

lean thinking | lean thinking | variance reduction | variance reduction | design of experiments | design of experiments | team-based work systems | team-based work systems | in-station process control | in-station process control | total productive maintenance | total productive maintenance | synchronous material flow | synchronous material flow | value stream mapping | value stream mapping | knowledge and information flow | knowledge and information flow | pull-based systems in contrasting industry settings | pull-based systems in contrasting industry settings | enterprise alignment | enterprise alignment

License

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6.047 Computational Biology (MIT) 6.047 Computational Biology (MIT)

Description

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets. This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

Subjects

Genomes | Genomes | Networks | Networks | Evolution | Evolution | computational biology | computational biology | genomics | genomics | comparative genomics | comparative genomics | epigenomics | epigenomics | functional genomics | motifs | functional genomics | motifs | phylogenomics | phylogenomics | personal genomics | personal genomics | algorithms | algorithms | machine learning | machine learning | biology | biology | biological datasets | biological datasets | proteomics | proteomics | sequence analysis | sequence analysis | sequence alignment | sequence alignment | genome assembly | genome assembly | network motifs | network motifs | network evolution | network evolution | graph algorithms | graph algorithms | phylogenetics | phylogenetics | python | python | probability | probability | statistics | statistics | entropy | entropy | information | information

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.096 Algorithms for Computational Biology (MIT) 6.096 Algorithms for Computational Biology (MIT)

Description

This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks. This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.

Subjects

biological sequence analysis | biological sequence analysis | gene finding | gene finding | motif discovery | motif discovery | RNA folding | RNA folding | global and local sequence alignment | global and local sequence alignment | genome assembly | genome assembly | comparative genomics | comparative genomics | genome duplication | genome duplication | genome rearrangements | genome rearrangements | evolutionary theory | evolutionary theory | gene expression | gene expression | clustering algorithms | clustering algorithms | scale-free networks | scale-free networks | machine learning applications | machine learning applications

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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HST.508 Quantitative Genomics (MIT) HST.508 Quantitative Genomics (MIT)

Description

This course provides a foundation in the following four areas: evolutionary and population genetics; comparative genomics; structural genomics and proteomics; and functional genomics and regulation. This course provides a foundation in the following four areas: evolutionary and population genetics; comparative genomics; structural genomics and proteomics; and functional genomics and regulation.

Subjects

genomics | genomics | quantitative genomics | quantitative genomics | comparative genomics | comparative genomics | genes | genes | genome | genome | SNPs | SNPs | haplotypes | haplotypes | sequence alignment | sequence alignment | protein structure | protein structure | protein folding | protein folding | proteomics | proteomics | structural genomics | structural genomics | functional genomics | functional genomics | networks | networks | systems biology | systems biology | biological networks | biological networks | RNA | RNA | DNA | DNA | gene expression | gene expression | evolutionary genetics | evolutionary genetics | population genetics | population genetics

License

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6.801 Machine Vision (MIT) 6.801 Machine Vision (MIT)

Description

Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed. Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed.

Subjects

machine vision | machine vision | image formation physics | image formation physics | image analysis | image analysis | binary image processing | binary image processing | image filtering | image filtering | recovering | recovering | shape | shape | lightness | lightness | orientation | orientation | motion | motion | photometric stereo | photometric stereo | extended Gaussian | extended Gaussian | environment interaction | environment interaction | motion vision | motion vision | shape from shading | shape from shading | photogrammetry | photogrammetry | stereo | stereo | object representation alignment | object representation alignment | analog VLSI | analog VLSI | computational vision | computational vision | robot vision | robot vision

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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7.91J Foundations of Computational and Systems Biology (MIT) 7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology. Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | computational biology | systems biology | systems biology | bioinformatics | bioinformatics | sequence analysis | sequence analysis | proteomics | proteomics | sequence alignment | sequence alignment | protein folding | protein folding | structure prediction | structure prediction | network modeling | network modeling | phylogenetics | phylogenetics | pairwise sequence comparisons | pairwise sequence comparisons | ncbi | ncbi | blast | blast | protein structure | protein structure | dynamic programming | dynamic programming | genome sequencing | genome sequencing | DNA | DNA | RNA | RNA | x-ray crystallography | x-ray crystallography | NMR | NMR | homologs | homologs | ab initio structure prediction | ab initio structure prediction | DNA microarrays | DNA microarrays | clustering | clustering | proteome | proteome | computational annotation | computational annotation | BE.490J | BE.490J | 7.91 | 7.91 | 7.36 | 7.36 | BE.490 | BE.490 | 20.490 | 20.490

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Tensile failure surface of a carbon fibre composite

Description

This is from the region of the fracture surface which was in tension. The fibres show clean, brittle fracture surfaces (with no fibrillation or distortion and little distortion of the matrix) and have broken at varying lengths and hence some stick out while others have left holes in the matrix. This is indicative of fibre pull-out having occurred after an initial failure of the matrix, followed by failure of the fibres themselves. This is a toughening mechanism in fibre reinforced composites. It is also involved in composite crack stopping properties; each fibre has briefly slowed the progress of the crack by opening it up along the weak fibre-matrix interface and hence blunting the crack tip. The crack therefore advances relatively slowly, with much lateral meandering.

Subjects

alignment | carbon | carbon fibres | CFC | composite material | epoxy | fibre | fracture | neutral axis | polymer composite | pull-out | reinforcement | DoITPoMS | University of Cambridge | micrograph | corematerials | ukoer

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Kevlar fibre composite fracture surface

Description

Because a relatively short beam was used, significant shear stresses existed in the beam, and failure has occurred principally by shear. In this mode, the specimen splits longitudinally along planes parallel to its neutral axis, due to shear failure within the matrix and at the weak interface between fibres and matrix. Matrix porosity (and particularly the long longitudinal voids present in this specimen), the poor wetting of fibres by the resin, and poor fibre distribution will all promote failure by shear. However, it may be that this failure mechanism has been partly inhibited by poor fibre alignment since some off-axis fibres will reinforce the matrix in shear

Subjects

alignment | composite material | epoxy | fibre | fibrillation | fracture | hackle region | Kevlar | liquid crystalline polymer (LCP) | lyotropic | polymer | polymer composite | reinforcement | shear | DoITPoMS | University of Cambridge | micrograph | corematerials | ukoer

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Kevlar fibre composite shear surface

Description

This is an image of the shear surface in a failed composite beam. 'Hackles' of matrix are clearly visible where shear has occurred within the matrix and it is also clear that shear has occurred across the fibre/matrix interface. The fibres are for the most part totally unscathed, though some mis-aligned fibres have become caught between the shear surfaces and 'fibrillated' by rolling and bending actions. It may be that this failure mechanism has been partly inhibited by poor fibre alignment since some off-axis fibres will reinforce the matrix in shear. It will have been promoted, however, by the extensive longitudinal voids.

Subjects

alignment | composite material | epoxy | fibre | fibrillation | fracture | hackle region | Kevlar | liquid crystalline polymer (LCP) | lyotropic | polymer | polymer composite | reinforcement | shear | DoITPoMS | University of Cambridge | micrograph | corematerials | ukoer

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Kevlar fibre composite shear surface

Description

This is an image of the shear surface in a failed composite beam. 'Hackles' of matrix are clearly visible where shear has occurred within the matrix and it is also clear that shear has occurred across the fibre/matrix interface. The fibres are for the most part totally unscathed, though some mis-aligned fibres have become caught between the shear surfaces and 'fibrillated' by rolling and bending actions. It may be that this failure mechanism has been partly inhibited by poor fibre alignment since some off-axis fibres will reinforce the matrix in shear. It will have been promoted, however, by the extensive longitudinal voids.

Subjects

alignment | composite material | epoxy | fibre | fibrillation | fracture | hackle region | Kevlar | liquid crystalline polymer (LCP) | lyotropic | polymer | polymer composite | reinforcement | shear | DoITPoMS | University of Cambridge | micrograph | corematerials | ukoer

License

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6.801 Machine Vision (MIT)

Description

Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed.

Subjects

machine vision | image formation physics | image analysis | binary image processing | image filtering | recovering | shape | lightness | orientation | motion | photometric stereo | extended Gaussian | environment interaction | motion vision | shape from shading | photogrammetry | stereo | object representation alignment | analog VLSI | computational vision | robot vision

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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ESD.60 Lean/Six Sigma Processes (MIT)

Description

Students of this course will develop a broad understanding of Lean/Six Sigma principles and practices, build capability to implement Lean/Six Sigma initiatives in manufacturing operations, and learn to operate with awareness of Lean/Six Sigma at the enterprise level. All course materials are organized around a common "single-point lesson" (SPL) format, with some of the SPLs provided by the instructor and guests and with some developed and delivered by student teams.

Subjects

lean thinking | variance reduction | design of experiments | team-based work systems | in-station process control | total productive maintenance | synchronous material flow | value stream mapping | knowledge and information flow | pull-based systems in contrasting industry settings | enterprise alignment

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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Disclinations in a smectic LCP, revealed by decoration

Description

Detailed configurations of disclinations and their interactions have been elucidated by band texture decoration. The positive value of elastic anisotropy of a semi-flexible LCP, Cl-6, shows that bend distortion is favoured. The obvious variations of elastic anisotropy values measured from the different disclinations is probably related to the polydispersity of the polymer. The majority of disclination pairs showing random configurations indicate that that defect interactions are far from equilibrium in polymeric smectics.

Subjects

alignment | decoration | disclination | dislocation | liquid crystalline polymer (LCP) | polymer | smectic | spontaneous band texture | texture | DoITPoMS | University of Cambridge | micrograph | corematerials | ukoer

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Disclinations in a smectic LCP, revealed by decoration

Description

Detailed configurations of disclinations and their interactions have been elucidated by band texture decoration. The positive value of elastic anisotropy of a semi-flexible LCP, Cl-6, shows that bend distortion is favoured. The obvious variations of elastic anisotropy values measured from the different disclinations is probably related to the polydispersity of the polymer. The majority of disclination pairs showing random configurations indicate that that defect interactions are far from equilibrium in polymeric smectics.

Subjects

alignment | decoration | disclination | dislocation | liquid crystalline polymer (LCP) | polymer | smectic | spontaneous band texture | texture | DoITPoMS | University of Cambridge | micrograph | corematerials | ukoer

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Fracture surface in a liquid crystalline polymer, oriented by a magnetic field

Description

Above their melting point, liquid crystalline polymers exhibit a mesophase within which, although able to flow and are not arranged in a crystal, they exhibit long range orientational order. This molecular alignment can be exploited to make high strength and stiffness fibres such as Kevlar, or even mouldable thermotropics such as Vectra. Molecular alignment can also be controlled by an external applied field; a property exploited in liquid crystal displays. This polymer has been subjected to a magnetic field of 1.1T (normal to the fracture plane) for one hour and the resulting alignment is evident in the fractured surface.

Subjects

alignment | fibre | fibrillation | fracture | liquid crystalline polymer (LCP) | lyotropic | magnetic | nematic | polymer | DoITPoMS | University of Cambridge | micrograph | corematerials | ukoer

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Fracture surface in a liquid crystalline polymer

Description

Above their melting point, liquid crystalline polymers exhibit a mesophase within which, although able to flow and are not arranged in a crystal, they exhibit long range orientational order. This molecular alignment can be exploited to make high strength and stiffness fibres such as Kevlar, or even mouldable thermotropics such as Vectra. Molecular alignment can also be controlled by an external applied field; a property exploited in liquid crystal displays. This specimen has not had a magnetic field applied to it and it exhibits only local molecular self-alignment, without any overall orientation alignment. It is to be compared with a similar sample which has been subjected to a magnetic field of 1.1T for one hour and in which there is a high level of fibrillar alignment.

Subjects

alignment | fibre | fibrillation | fracture | liquid crystalline polymer (LCP) | lyotropic | magnetic | nematic | polymer | DoITPoMS | University of Cambridge | micrograph | corematerials | ukoer

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Fracture surface in a liquid crystalline polymer

Description

Above their melting point, liquid crystalline polymers exhibit a mesophase within which, although able to flow and are not arranged in a crystal, they exhibit long range orientational order. This molecular alignment can be exploited to make high strength and stiffness fibres such as Kevlar, or even mouldable thermotropics such as Vectra. Molecular alignment can also be controlled by an external applied field; a property exploited in liquid crystal displays. This specimen has not had a magnetic field applied to it and it exhibits only local molecular self-alignment, without any overall orientation alignment. It is to be compared with a similar sample which has been subjected to a magnetic field of 1.1T for one hour and in which there is a high level of fibrillar alignment.

Subjects

alignment | fibre | fibrillation | fracture | liquid crystalline polymer (LCP) | lyotropic | magnetic | nematic | polymer | DoITPoMS | University of Cambridge | micrograph | corematerials | ukoer

License

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Aligned carbon nanotubes, coated with a conducting polymer

Description

Carbon nanotubes are a long, tubular form of carbon that has an extremely high surface area. Conducting polymers, as their name suggests, are a group of polymers that exhibit excellent electrical conductivity, in some cases reaching that of copper.

Subjects

alignment | carbon | carbon nanotube | coating | conducting polymer | nanotube | polymer | polypyrrole | DoITPoMS | University of Cambridge | micrograph | corematerials | ukoer

License

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ESD.60 Lean/Six Sigma Processes (MIT)

Description

Students of this course will develop a broad understanding of Lean/Six Sigma principles and practices, build capability to implement Lean/Six Sigma initiatives in manufacturing operations, and learn to operate with awareness of Lean/Six Sigma at the enterprise level. All course materials are organized around a common "single-point lesson" (SPL) format, with some of the SPLs provided by the instructor and guests and with some developed and delivered by student teams.

Subjects

lean thinking | variance reduction | design of experiments | team-based work systems | in-station process control | total productive maintenance | synchronous material flow | value stream mapping | knowledge and information flow | pull-based systems in contrasting industry settings | enterprise alignment

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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6.801 Machine Vision (MIT)

Description

Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed.

Subjects

machine vision | image formation physics | image analysis | binary image processing | image filtering | recovering | shape | lightness | orientation | motion | photometric stereo | extended Gaussian | environment interaction | motion vision | shape from shading | photogrammetry | stereo | object representation alignment | analog VLSI | computational vision | robot vision

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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