The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Takeshi Serizawa - One of the best experts on this subject based on the ideXlab platform.
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Self-Assembly of Cellulose Oligomers into Nanoribbon Network Structures Based on Kinetic Control of Enzymatic Oligomerization
2017Co-Authors: Takeshi Serizawa, Yuka Fukaya, Toshiki SawadaAbstract:The ability to chemically synthesize desired molecules followed by their in situ self-assembly in reaction solution has attracted much attention as a simple and environmentally friendly method to produce self-assembled nanoStructures. In this study, α-d-glucose 1-phosphate monomers and cellobiose primers were subjected to cellodextrin phosphorylase-catalyzed reverse phosphorolysis reactions in aqueous solution in order to synthesize cellulose oligomers, which were then in situ self-assembled into crystalline nanoribbon Network Structures. The average degree-of-polymerization (DP) values of the cellulose oligomers were estimated to be approximately 7–8 with a certain degree of DP distribution. The cellulose oligomers crystallized with the cellulose II allomorph appeared to align perpendicularly to the base plane of the nanoribbons in an antiparallel manner. Detailed analyses of reaction time dependence suggested that the production of nanoribbon Network Structures was kinetically controlled by the amount of water-insoluble cellulose oligomers produced
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enzymatic synthesis of oligo ethylene glycol bearing cellulose oligomers for in situ formation of hydrogels with crystalline nanoribbon Network Structures
Langmuir, 2016Co-Authors: Takatoshi Nohara, Toshiki Sawada, Hiroshi Tanaka, Takeshi SerizawaAbstract:Enzymatic synthesis of cellulose and its derivatives has gained considerable attention for use in the production of artificial crystalline nanocelluloses with unique structural and functional properties. However, the poor colloidal stability of the nanocelluloses during enzymatic synthesis in aqueous solutions limits their crystallization-based self-assembly to greater architectures. In this study, oligo(ethylene glycol) (OEG)-bearing cellulose oligomers with different OEG chain lengths were systematically synthesized via cellodextrin phosphorylase-catalyzed oligomerization of α-d-glucose l-phosphate monomers against OEG-bearing β-d-glucose primers. The products were self-assembled into extremely well-grown crystalline nanoribbon Network Structures with the cellulose II allomorph, potentially due to OEG-derived colloidal stability of the nanoribbon’s precursors, followed by the in situ formation of physically cross-linked hydrogels. The monomer conversions, average degree of polymerization, and morphologi...
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Enzymatic Synthesis of Oligo(ethylene glycol)-Bearing Cellulose Oligomers for in Situ Formation of Hydrogels with Crystalline Nanoribbon Network Structures
2016Co-Authors: Takatoshi Nohara, Toshiki Sawada, Hiroshi Tanaka, Takeshi SerizawaAbstract:Enzymatic synthesis of cellulose and its derivatives has gained considerable attention for use in the production of artificial crystalline nanocelluloses with unique structural and functional properties. However, the poor colloidal stability of the nanocelluloses during enzymatic synthesis in aqueous solutions limits their crystallization-based self-assembly to greater architectures. In this study, oligo(ethylene glycol) (OEG)-bearing cellulose oligomers with different OEG chain lengths were systematically synthesized via cellodextrin phosphorylase-catalyzed oligomerization of α-d-glucose l-phosphate monomers against OEG-bearing β-d-glucose primers. The products were self-assembled into extremely well-grown crystalline nanoribbon Network Structures with the cellulose II allomorph, potentially due to OEG-derived colloidal stability of the nanoribbon’s precursors, followed by the in situ formation of physically cross-linked hydrogels. The monomer conversions, average degree of polymerization, and morphologies of the nanoribbons changed significantly, depending on the OEG chain length. Taken together, our findings open a new avenue for the enzymatic reaction-based facile production of novel cellulosic soft materials with regular nanoStructures
Tarek Abdelzaher - One of the best experts on this subject based on the ideXlab platform.
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deepiot compressing deep neural Network Structures for sensing systems with a compressor critic framework
International Conference on Embedded Networked Sensor Systems, 2017Co-Authors: Shuochao Yao, Yiran Zhao, Aston Zhang, Tarek AbdelzaherAbstract:Recent advances in deep learning motivate the use of deep neutral Networks in sensing applications, but their excessive resource needs on constrained embedded devices remain an important impediment. A recently explored solution space lies in compressing (approximating or simplifying) deep neural Networks in some manner before use on the device. We propose a new compression solution, called DeepIoT, that makes two key contributions in that space. First, unlike current solutions geared for compressing specific types of neural Networks, DeepIoT presents a unified approach that compresses all commonly used deep learning Structures for sensing applications, including fully-connected, convolutional, and recurrent neural Networks, as well as their combinations. Second, unlike solutions that either sparsify weight matrices or assume linear structure within weight matrices, DeepIoT compresses neural Network Structures into smaller dense matrices by finding the minimum number of non-redundant hidden elements, such as filters and dimensions required by each layer, while keeping the performance of sensing applications the same. Importantly, it does so using an approach that obtains a global view of parameter redundancies, which is shown to produce superior compression. The compressed model generated by DeepIoT can directly use existing deep learning libraries that run on embedded and mobile systems without further modifications. We conduct experiments with five different sensing-related tasks on Intel Edison devices. DeepIoT outperforms all compared baseline algorithms with respect to execution time and energy consumption by a significant margin. It reduces the size of deep neural Networks by 90% to 98.9%. It is thus able to shorten execution time by 71.4% to 94.5%, and decrease energy consumption by 72.2% to 95.7%. These improvements are achieved without loss of accuracy. The results underscore the potential of DeepIoT for advancing the exploitation of deep neural Networks on resource-constrained embedded devices.
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deepiot compressing deep neural Network Structures for sensing systems with a compressor critic framework
arXiv: Learning, 2017Co-Authors: Shuochao Yao, Yiran Zhao, Aston Zhang, Tarek AbdelzaherAbstract:Recent advances in deep learning motivate the use of deep neutral Networks in sensing applications, but their excessive resource needs on constrained embedded devices remain an important impediment. A recently explored solution space lies in compressing (approximating or simplifying) deep neural Networks in some manner before use on the device. We propose a new compression solution, called DeepIoT, that makes two key contributions in that space. First, unlike current solutions geared for compressing specific types of neural Networks, DeepIoT presents a unified approach that compresses all commonly used deep learning Structures for sensing applications, including fully-connected, convolutional, and recurrent neural Networks, as well as their combinations. Second, unlike solutions that either sparsify weight matrices or assume linear structure within weight matrices, DeepIoT compresses neural Network Structures into smaller dense matrices by finding the minimum number of non-redundant hidden elements, such as filters and dimensions required by each layer, while keeping the performance of sensing applications the same. Importantly, it does so using an approach that obtains a global view of parameter redundancies, which is shown to produce superior compression. We conduct experiments with five different sensing-related tasks on Intel Edison devices. DeepIoT outperforms all compared baseline algorithms with respect to execution time and energy consumption by a significant margin. It reduces the size of deep neural Networks by 90% to 98.9%. It is thus able to shorten execution time by 71.4% to 94.5%, and decrease energy consumption by 72.2% to 95.7%. These improvements are achieved without loss of accuracy. The results underscore the potential of DeepIoT for advancing the exploitation of deep neural Networks on resource-constrained embedded devices.
Takatoshi Nohara - One of the best experts on this subject based on the ideXlab platform.
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enzymatic synthesis of oligo ethylene glycol bearing cellulose oligomers for in situ formation of hydrogels with crystalline nanoribbon Network Structures
Langmuir, 2016Co-Authors: Takatoshi Nohara, Toshiki Sawada, Hiroshi Tanaka, Takeshi SerizawaAbstract:Enzymatic synthesis of cellulose and its derivatives has gained considerable attention for use in the production of artificial crystalline nanocelluloses with unique structural and functional properties. However, the poor colloidal stability of the nanocelluloses during enzymatic synthesis in aqueous solutions limits their crystallization-based self-assembly to greater architectures. In this study, oligo(ethylene glycol) (OEG)-bearing cellulose oligomers with different OEG chain lengths were systematically synthesized via cellodextrin phosphorylase-catalyzed oligomerization of α-d-glucose l-phosphate monomers against OEG-bearing β-d-glucose primers. The products were self-assembled into extremely well-grown crystalline nanoribbon Network Structures with the cellulose II allomorph, potentially due to OEG-derived colloidal stability of the nanoribbon’s precursors, followed by the in situ formation of physically cross-linked hydrogels. The monomer conversions, average degree of polymerization, and morphologi...
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Enzymatic Synthesis of Oligo(ethylene glycol)-Bearing Cellulose Oligomers for in Situ Formation of Hydrogels with Crystalline Nanoribbon Network Structures
2016Co-Authors: Takatoshi Nohara, Toshiki Sawada, Hiroshi Tanaka, Takeshi SerizawaAbstract:Enzymatic synthesis of cellulose and its derivatives has gained considerable attention for use in the production of artificial crystalline nanocelluloses with unique structural and functional properties. However, the poor colloidal stability of the nanocelluloses during enzymatic synthesis in aqueous solutions limits their crystallization-based self-assembly to greater architectures. In this study, oligo(ethylene glycol) (OEG)-bearing cellulose oligomers with different OEG chain lengths were systematically synthesized via cellodextrin phosphorylase-catalyzed oligomerization of α-d-glucose l-phosphate monomers against OEG-bearing β-d-glucose primers. The products were self-assembled into extremely well-grown crystalline nanoribbon Network Structures with the cellulose II allomorph, potentially due to OEG-derived colloidal stability of the nanoribbon’s precursors, followed by the in situ formation of physically cross-linked hydrogels. The monomer conversions, average degree of polymerization, and morphologies of the nanoribbons changed significantly, depending on the OEG chain length. Taken together, our findings open a new avenue for the enzymatic reaction-based facile production of novel cellulosic soft materials with regular nanoStructures
Geoffrey A Lockett - One of the best experts on this subject based on the ideXlab platform.
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mixed mode Network Structures the strategic use of electronic communication by organizations
Organization Science, 1997Co-Authors: Christopher P Holland, Geoffrey A LockettAbstract:The impact of interorganizational systems (IOSs) on the structure of market Networks is analyzed from a management perspective. A research framework is applied to various organizational settings, yielding a range of mixed mode forms in which elements of both market and hierarchy are evident. These forms are more complex than the simple Network or hybrid Structures postulated in the management and information systems literature. The framework represents a departure from electronic markets theory, questioning its basic predictions that as companies trade electronically there will be proportionately more markets than hierarchies. Instead, IOSs make possible relationships that combine market and hierarchy elements simultaneously. Although economic forces are driving the changes in Network structure, economic variables are tempered by individual firm strategies reflecting investment, Network structure and IOS choices. The implications for theory are that traditional analyses of Network Structures and competiti...
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mixed mode Network Structures the strategic use of electronic communication by organizations
Organization Science, 1997Co-Authors: Christopher P Holland, Geoffrey A LockettAbstract:The impact of interorganizational systems (IOSs) on the structure of market Networks is analyzed from a management perspective. A research framework is applied to various organizational settings, yielding a range of mixed mode forms in which elements of both market and hierarchy are evident. These forms are more complex than the simple Network or hybrid Structures postulated in the management and information systems literature. The framework represents a departure from electronic markets theory, questioning its basic predictions that as companies trade electronically there will be proportionately more markets than hierarchies. Instead, IOSs make possible relationships that combine market and hierarchy elements simultaneously. Although economic forces are driving the changes in Network structure, economic variables are tempered by individual firm strategies reflecting investment, Network structure and IOS choices. The implications for theory are that traditional analyses of Network Structures and competition in business markets do not describe or explain adequately the structure and dynamics of competition in an electronic trading environment. The implications for managers are that they should consider the effects of mixed mode Network Structures on their processes for forming and managing business relationships supported by IOSs. The contribution of the paper is to provide a more accurate model of competition in business markets by demonstrating that multiple forms of mixed mode Network Structures exist. The mixed mode proposition is illustrated with case data from a range of mixed mode Network Structures.
Toshiki Sawada - One of the best experts on this subject based on the ideXlab platform.
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Self-Assembly of Cellulose Oligomers into Nanoribbon Network Structures Based on Kinetic Control of Enzymatic Oligomerization
2017Co-Authors: Takeshi Serizawa, Yuka Fukaya, Toshiki SawadaAbstract:The ability to chemically synthesize desired molecules followed by their in situ self-assembly in reaction solution has attracted much attention as a simple and environmentally friendly method to produce self-assembled nanoStructures. In this study, α-d-glucose 1-phosphate monomers and cellobiose primers were subjected to cellodextrin phosphorylase-catalyzed reverse phosphorolysis reactions in aqueous solution in order to synthesize cellulose oligomers, which were then in situ self-assembled into crystalline nanoribbon Network Structures. The average degree-of-polymerization (DP) values of the cellulose oligomers were estimated to be approximately 7–8 with a certain degree of DP distribution. The cellulose oligomers crystallized with the cellulose II allomorph appeared to align perpendicularly to the base plane of the nanoribbons in an antiparallel manner. Detailed analyses of reaction time dependence suggested that the production of nanoribbon Network Structures was kinetically controlled by the amount of water-insoluble cellulose oligomers produced
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enzymatic synthesis of oligo ethylene glycol bearing cellulose oligomers for in situ formation of hydrogels with crystalline nanoribbon Network Structures
Langmuir, 2016Co-Authors: Takatoshi Nohara, Toshiki Sawada, Hiroshi Tanaka, Takeshi SerizawaAbstract:Enzymatic synthesis of cellulose and its derivatives has gained considerable attention for use in the production of artificial crystalline nanocelluloses with unique structural and functional properties. However, the poor colloidal stability of the nanocelluloses during enzymatic synthesis in aqueous solutions limits their crystallization-based self-assembly to greater architectures. In this study, oligo(ethylene glycol) (OEG)-bearing cellulose oligomers with different OEG chain lengths were systematically synthesized via cellodextrin phosphorylase-catalyzed oligomerization of α-d-glucose l-phosphate monomers against OEG-bearing β-d-glucose primers. The products were self-assembled into extremely well-grown crystalline nanoribbon Network Structures with the cellulose II allomorph, potentially due to OEG-derived colloidal stability of the nanoribbon’s precursors, followed by the in situ formation of physically cross-linked hydrogels. The monomer conversions, average degree of polymerization, and morphologi...
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Enzymatic Synthesis of Oligo(ethylene glycol)-Bearing Cellulose Oligomers for in Situ Formation of Hydrogels with Crystalline Nanoribbon Network Structures
2016Co-Authors: Takatoshi Nohara, Toshiki Sawada, Hiroshi Tanaka, Takeshi SerizawaAbstract:Enzymatic synthesis of cellulose and its derivatives has gained considerable attention for use in the production of artificial crystalline nanocelluloses with unique structural and functional properties. However, the poor colloidal stability of the nanocelluloses during enzymatic synthesis in aqueous solutions limits their crystallization-based self-assembly to greater architectures. In this study, oligo(ethylene glycol) (OEG)-bearing cellulose oligomers with different OEG chain lengths were systematically synthesized via cellodextrin phosphorylase-catalyzed oligomerization of α-d-glucose l-phosphate monomers against OEG-bearing β-d-glucose primers. The products were self-assembled into extremely well-grown crystalline nanoribbon Network Structures with the cellulose II allomorph, potentially due to OEG-derived colloidal stability of the nanoribbon’s precursors, followed by the in situ formation of physically cross-linked hydrogels. The monomer conversions, average degree of polymerization, and morphologies of the nanoribbons changed significantly, depending on the OEG chain length. Taken together, our findings open a new avenue for the enzymatic reaction-based facile production of novel cellulosic soft materials with regular nanoStructures
