Hasil untuk "Technology"

B. Phillips, Hongxin Zhao

T. Giallorenzi, J. Bucaro, A. Dandridge et al.

The current state of the art of optical fiber sensors is reviewed. The principles of operation are detailed and the various types of fiber sensors are outlined. Achievable performance and limitations are discussed and a description of technology used to fabricate the sensor is presented. The characteristics of acoustic, magnetic, gyro, laser diode, and other sensors are described. Trends in the development of this sensor technology and expected application areas are briefly outlined.

S. Jasanoff, G. E. Markle, J. Petersen et al.

T. Mason, L. Paniwnyk, J. Lorimer

D. A. I. L. Aibson, Ose ´ A. S Cheinkman, C. Hristine et al.

C. Locatis

Janet Fulk

M. Turner, B. Kitchenham, P. Brereton et al.

P. Ajayan, L. Schadler, P. Braun

1. Bulk Metal and Ceramics Nanocomposites (Pulickel M. Ajayan).1.1 Introduction.1.2 Ceramic/Metal Nanocomposites.1.2.1 Nanocomposites by Mechanical Alloying.1.2.2 Nanocomposites from SolGel Synthesis.1.2.3 Nanocomposites by Thermal Spray Synthesis.1.3 Metal Matrix Nanocomposites.1.4 Bulk Ceramic Nanocomposites for Desired Mechanical Properties.1.5 Thin-Film Nanocomposites: Multilayer and Granular Films.1.6 Nanocomposites for Hard Coatings.1.7 Carbon Nanotube-Based Nanocomposites.1.8 Functional Low-Dimensional Nanocomposites.1.8.1 Encapsulated Composite Nanosystems.1.8.2 Applications of Nanocomposite Wires.1.8.3 Applications of Nanocomposite Particles.1.9 Inorganic Nanocomposites for Optical Applications.1.10 Inorganic Nanocomposites for Electrical Applications.1.11 Nanoporous Structures and Membranes: Other Nanocomposites.1.12 Nanocomposites for Magnetic Applications.1.12.1 Particle-Dispersed Magnetic Nanocomposites.1.12.2 Magnetic Multilayer Nanocomposites.1.12.2.1 Microstructure and Thermal Stability of Layered Magnetic Nanocomposites.1.12.2.2 Media Materials.1.13 Nanocomposite Structures having Miscellaneous Properties.1.14 Concluding Remarks on Metal/Ceramic Nanocomposites.2. Polymer-based and Polymer-filled Nanocomposites (Linda S. Schadler).2.1 Introduction.2.2 Nanoscale Fillers.2.2.1 Nanofiber or Nanotube Fillers.2.2.1.1 Carbon Nanotubes.2.2.1.2 Nanotube Processing.2.2.1.3 Purity.2.2.1.4 Other Nanotubes.2.2.2 Plate-like Nanofillers.2.2.3 Equi-axed Nanoparticle Fillers.2.3 Inorganic FillerPolymer Interfaces.2.4 Processing of Polymer Nanocomposites.2.4.1 Nanotube/Polymer Composites.2.4.2 Layered FillerPolymer Composite Processing.2.4.2.1 Polyamide Matrices.2.4.2.2 Polyimide Matrices.2.4.2.3 Polypropylene and Polyethylene Matrices.2.4.2.4 Liquid-Crystal Matrices.2.4.2.5 Polymethylmethacrylate/Polystyrene Matrices.2.4.2.6 Epoxy and Polyurethane Matrices.2.4.2.7 Polyelectrolyte Matrices.2.4.2.8 Rubber Matrices.2.4.2.9 Others.2.4.3 Nanoparticle/Polymer Composite Processing.2.4.3.1 Direct Mixing.2.4.3.2 Solution Mixing.2.4.3.3 In-Situ Polymerization.2.4.3.4 In-Situ Particle Processing Ceramic/Polymer Composites.2.4.3.5 In-Situ Particle Processing Metal/Polymer Nanocomposites.2.4.4 Modification of Interfaces.2.4.4.1 Modification of Nanotubes.2.4.4.2 Modification of Equi-axed Nanoparticles.2.4.4.3 Small-Molecule Attachment.2.4.4.4 Polymer Coatings.2.4.4.5 Inorganic Coatings.2.5 Properties of Composites.2.5.1 Mechanical Properties.2.5.1.1 Modulus and the Load-Carrying Capability of Nanofillers.2.5.1.2 Failure Stress and Strain Toughness.2.5.1.3 Glass Transition and Relaxation Behavior.2.5.1.4 Abrasion and Wear Resistance.2.5.2 Permeability.2.5.3 Dimensional Stability.2.5.4 Thermal Stability and Flammability.2.5.5 Electrical and Optical Properties.2.5.5.1 Resistivity, Permittivity, and Breakdown Strength.2.5.5.2 Optical Clarity.2.5.5.3 Refractive Index Control.2.5.5.4 Light-Emitting Devices.2.5.5.5 Other Optical Activity.2.6 Summary.3. Natural Nanobiocomposites, Biomimetic Nanocomposites, and Biologically Inspired Nanocomposites (Paul V. Braun).3.1 Introduction.3.2 Natural Nanocomposite Materials.3.2.1 Biologically Synthesized Nanoparticles.3.2.2 Biologically Synthesized Nanostructures.3.3 Biologically Derived Synthetic Nanocomposites.3.3.1 Protein-Based Nanostructure Formation.3.3.2 DNA-Templated Nanostructure Formation.3.3.3 Protein Assembly.3.4 Biologically Inspired Nanocomposites.3.4.1 Lyotropic Liquid-Crystal Templating.3.4.2 Liquid-Crystal Templating of Thin Films.3.4.3 Block-Copolymer Templating.3.4.4 Colloidal Templating.3.5 Summary.4. Modeling of Nanocomposites (Catalin Picu and Pawel Keblinski).4.1 Introduction The Need For Modeling.4.2 Current Conceptual Frameworks.4.3 Multiscale Modeling.4.4 Multiphysics Aspects.4.5 Validation.Index.

V. Wallace, B. Cole, R. Woodward et al.

D. Blumenthal

Richard Uhlig, G. Neiger, Dion Rodgers et al.

J. Curran, M. Meuter

Anthony R. Metke, R. Ekl

Heather K. Holden, R. Rada

D. Popp, I. Haščič, Neelakshi Medhi

Susan A. Brown, A. Dennis, V. Venkatesh

The paper presents a model integrating theories from collaboration research (i.e., social presence theory, channel expansion theory, and the task closure model) with a recent theory from technology adoption research (i.e., unified theory of acceptance and use of technology, abbreviated to UTAUT) to explain the adoption and use of collaboration technology. We theorize that collaboration technology characteristics, individual and group characteristics, task characteristics, and situational characteristics are predictors of performance expectancy, effort expectancy, social influence, and facilitating conditions in UTAUT. We further theorize that the UTAUT constructs, in concert with gender, age, and experience, predict intention to use a collaboration technology, which in turn predicts use. We conducted two field studies in Finland among (1) 349 short message service (SMS) users and (2) 447 employees who were potential users of a new collaboration technology in an organization. Our model was supported in both studies. The current work contributes to research by developing and testing a technology-specific model of adoption in the collaboration context.

J. Mankins

Juan Carlos Ramírez-Vázquez, Guadalupe Esmeralda Rivera-García, Marco Antonio Gómez-Guzmán et al.

A substantial number of students with hearing impairments are enrolled in higher education, motivating the development of inclusive assistive technologies that reduce communication barriers. This study developed and evaluated a prototype electronic glove that translates Mexican Sign Language (LSM) signs into Spanish text using machine learning. Eight participants (four deaf and four hearing with LSM proficiency) completed four sessions involving 12 signs; three sessions (S1–S3) were used for model development and one session (T) was held out for evaluation. Models were trained on S1–S3 and tested on T using a session-level split without window mixing across sessions; therefore, results represent a speaker-dependent, inter-session pilot assessment rather than a speaker-independent generalization test. The glove integrates flex sensors and an inertial measurement unit IMU MPU6050 connected to an ESP32-C3 SuperMini microcontroller. These components were selected due to their low cost, availability, and ease of integration, making them suitable for the development of accessible wearable assistive technologies. Under this protocol, the system achieved a window-level overall test accuracy of 97.0% (95% CI computed at the window level: 96.00–97.00), with higher performance for the dynamic subset (98.0%) than for the static subset (95.0%), and an algorithmic decision delay of 1.2 s. Usability and acceptance were evaluated using the System Usability Scale (SUS) and a Technology Acceptance Model (TAM)-based questionnaire. The mean SUS score was 50.6 ± 1.8 (marginal usability), while participants reported positive perceptions across TAM constructs. Overall, findings demonstrate technical feasibility under controlled inter-session conditions and provide a foundation for iterative user-centered refinement, followed by strict speaker-independent validation and classroom deployment studies in future work.