2,712 research outputs found
Fabrication and characterization of shape memory polymers at small scales
The objective of this research is to thoroughly investigate the shape memory effect
in polymers, characterize, and optimize these polymers for applications in information storage systems.
Previous research effort in this field concentrated on shape memory metals for
biomedical applications such as stents. Minimal work has been done on shape memory poly-
mers; and the available work on shape memory polymers has not characterized the behaviors
of this category of polymers fully. Copolymer shape memory materials based on diethylene
glycol dimethacrylate (DEGDMA) crosslinker, and tert butyl acrylate (tBA) monomer are
designed. The design encompasses a careful control of the backbone chemistry of the materials.
Characterization methods such as dynamic mechanical analysis (DMA), differential
scanning calorimetry (DSC); and novel nanoscale techniques such as atomic force microscopy
(AFM), and nanoindentation are applied to this system of materials. Designed experiments
are conducted on the materials to optimize spin coating conditions for thin films. Furthermore,
the recovery, a key for the use of these polymeric materials for information storage, is
examined in detail with respect to temperature. In sum, the overarching objectives of the
proposed research are to: (i) design shape memory polymers based on polyethylene glycol
dimethacrylate (PEGDMA) and diethylene glycol dimethacrylate (DEGDMA) crosslinkers,
2-hydroxyethyl methacrylate (HEMA) and tert-butyl acrylate monomer (tBA). (ii) utilize
dynamic mechanical analysis (DMA) to comprehend the thermomechanical properties of
shape memory polymers based on DEGDMA and tBA. (iii) utilize nanoindentation and
atomic force microscopy (AFM) to understand the nanoscale behavior of these SMPs, and
explore the strain storage and recovery of the polymers from a deformed state. (iv) study
spin coating conditions on thin film quality with designed experiments. (iv) apply neural
networks and genetic algorithms to optimize these systems.Ph.D.Committee Chair: Gall, Ken; Committee Chair: May, Gary S; Committee Member: Brand, Oliver; Committee Member: Degertekin, F Levent; Committee Member: Milor, Linda
Intelligent Computing: The Latest Advances, Challenges and Future
Computing is a critical driving force in the development of human
civilization. In recent years, we have witnessed the emergence of intelligent
computing, a new computing paradigm that is reshaping traditional computing and
promoting digital revolution in the era of big data, artificial intelligence
and internet-of-things with new computing theories, architectures, methods,
systems, and applications. Intelligent computing has greatly broadened the
scope of computing, extending it from traditional computing on data to
increasingly diverse computing paradigms such as perceptual intelligence,
cognitive intelligence, autonomous intelligence, and human-computer fusion
intelligence. Intelligence and computing have undergone paths of different
evolution and development for a long time but have become increasingly
intertwined in recent years: intelligent computing is not only
intelligence-oriented but also intelligence-driven. Such cross-fertilization
has prompted the emergence and rapid advancement of intelligent computing.
Intelligent computing is still in its infancy and an abundance of innovations
in the theories, systems, and applications of intelligent computing are
expected to occur soon. We present the first comprehensive survey of literature
on intelligent computing, covering its theory fundamentals, the technological
fusion of intelligence and computing, important applications, challenges, and
future perspectives. We believe that this survey is highly timely and will
provide a comprehensive reference and cast valuable insights into intelligent
computing for academic and industrial researchers and practitioners
Current Trends in Intelligent Control Neural Networks for Thermal Processing (Foods): Systematic Literature Review
Thermal processing is a technique for sterilizing foods through heating at high temperatures. Thermal processing plays a significant role in preserving foods economically, efficiently, reliably, and safely. Control in thermal processing of foods is necessary to avoid any decrease in food quality, i.e., color change, reduced content, sensory quality, and nutrition. Artificial Neural Network (ANN) has been developed as a computing method in research and developments on thermal processing methods to discover one suitable for food processing without damaging food quality. To this date, ANN has been used in food industries for modeling many processes. The paper aims to identify the latest trend in intelligent neural network control for the thermal processing of foods. The paper conducted a systematic literature review with five research questions using Preferred Reporting Items for Systematic Review (PRISMA). According to screening results and article selection, 240 potential articles have fulfilled the inclusion criteria. Then, each article was explored to identify the advantage and the advance of intelligent network control in thermal food processing. It can be concluded that the technology in information and computations of food processing has rapidly developed and advanced through the utilization of a combination of ANN with fuzzy logic and/or genetic algorithms
Harnessing Artificial Intelligence for the Next Generation of 3D Printed Medicines
Artificial intelligence (AI) is redefining how we exist in the world. In almost every sector of society, AI is performing tasks with super-human speed and intellect; from the prediction of stock market trends to driverless vehicles, diagnosis of disease, and robotic surgery. Despite this growing success, the pharmaceutical field is yet to truly harness AI. Development and manufacture of medicines remains largely in a āone size fits allā paradigm, in which mass-produced, identical formulations are expected to meet individual patient needs. Recently, 3D printing (3DP) has illuminated a path for on-demand production of fully customisable medicines. Due to its flexibility, pharmaceutical 3DP presents innumerable options during formulation development that generally require expert navigation. Leveraging AI within pharmaceutical 3DP removes the need for human expertise, as optimal process parameters can be accurately predicted by machine learning. AI can also be incorporated into a pharmaceutical 3DP āInternet of Thingsā, moving the personalised production of medicines into an intelligent, streamlined, and autonomous pipeline. Supportive infrastructure, such as The Cloud and blockchain, will also play a vital role. Crucially, these technologies will expedite the use of pharmaceutical 3DP in clinical settings and drive the global movement towards personalised medicine and Industry 4.0
NASA SBIR abstracts of 1992, phase 1 projects
The objectives of 346 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1992 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 346, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1992 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included
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