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- Language: English
- Creators: Ira A. Fulton Schools of Engineering
Description
Background: Despite the high prevalence of seizure, epilepsy and abnormal electroencephalograms in individuals with autism spectrum disorder (ASD), there is little information regarding the relative effectiveness of treatments for seizures in the ASD population. In order to determine the effectiveness of traditional and non-traditional treatments for improving seizures and influencing other clinical factor relevant to ASD, we developed a comprehensive on-line seizure survey.
Methods: Announcements (by email and websites) by ASD support groups asked parents of children with ASD to complete the on-line surveys. Survey responders choose one of two surveys to complete: a survey about treatments for individuals with ASD and clinical or subclinical seizures or abnormal electroencephalograms, or a control survey for individuals with ASD without clinical or subclinical seizures or abnormal electroencephalograms. Survey responders rated the perceived effect of traditional antiepileptic drug (AED), non-AED seizure treatments and non-traditional ASD treatments on seizures and other clinical factors (sleep, communication, behavior, attention and mood), and listed up to three treatment side effects.
Results: Responses were obtained concerning 733 children with seizures and 290 controls. In general, AEDs were perceived to improve seizures but worsened other clinical factors for children with clinical seizure. Valproic acid, lamotrigine, levetiracetam and ethosuximide were perceived to improve seizures the most and worsen other clinical factors the least out of all AEDs in children with clinical seizures. Traditional non-AED seizure and non-traditional treatments, as a group, were perceived to improve other clinical factors and seizures but the perceived improvement in seizures was significantly less than that reported for AEDs. Certain traditional non-AED treatments, particularly the ketogenic diet, were perceived to improve both seizures and other clinical factors. For ASD individuals with reported subclinical seizures, other clinical factors were reported to be worsened by AEDs and improved by non-AED traditional seizure and non-traditional treatments. The rate of side effects was reportedly higher for AEDs compared to traditional non-AED treatments.
Conclusion: Although this survey-based method only provides information regarding parental perceptions of effectiveness, this information may be helpful for selecting seizure treatments in individuals with ASD.
ContributorsFrye, Richard E. (Author) / Sreenivasula, Swapna (Author) / Adams, James (Author) / Ira A. Fulton Schools of Engineering (Contributor)
Created2011-05-18
Description
Different environmental factors, such as ultraviolet radiation (UV), relative humidity (RH) and the presence of reducing gases (acetone and ethanol), play an important role in the daily life of human beings. UV is very important in a number of areas, such as astronomy, resin curing of polymeric materials, combustion engineering, water purification, flame detection and biological effects with more recent proposals like early missile plume detection, secure space-to-space communications and pollution monitoring. RH is a very common parameter in the environment. It is essential not only for human comfort, but also for a broad spectrum of industries and technologies. There is a substantial interest in the development of RH sensors for applications in monitoring moisture level at home, in clean rooms, cryogenic processes, medical and food science, and so on. The concentration of acetone and other ketone bodies in the exhaled air can serve as an express noninvasive diagnosis of ketosis. Meanwhile, driving under the influence of alcohol is a serious traffic violation and this kind of deviant behavior causes many accidents and deaths on the highway. Therefore, the detection of ethanol in breath is usually used as a quick and reliable screening method for the sobriety checkpoint. Traditionally, semiconductor metal oxide sensors are the major candidates employed in the sensing applications mentioned above. However, they suffer from the low sensitivity, poor selectivity and huge power consumption. In this dissertation, Zinc Oxide (ZnO) based Film Bulk Acoustic Resonator (FBAR) was developed to monitor UV, RH, acetone and ethanol in the environment. FBAR generally consists of a sputtered piezoelectric thin film (ZnO/AlN) sandwiched between two electrodes. It has been well developed both as filters and as high sensitivity mass sensors in recent years. FBAR offers high sensitivity and excellent selectivity for various environment monitoring applications. As the sensing signal is in the frequency domain, FABR has the potential to be incorporated in a wireless sensor network for remote sensing. This study extended our current knowledge of FBAR and pointed out feasible directions for future exploration.
ContributorsQiu, Xiaotun (Author) / Yu, Hongyu (Thesis advisor) / Christen, Jennifer Blain (Committee member) / Aberle, James T., 1961- (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2011
Finite element analysis of silicon thin films on soft substrates as anodes for lithium ion batteries
Description
The wide-scale use of green technologies such as electric vehicles has been slowed due to insufficient means of storing enough portable energy. Therefore it is critical that efficient storage mediums be developed in order to transform abundant renewable energy into an on-demand source of power. Lithium (Li) ion batteries are seeing a stream of improvements as they are introduced into many consumer electronics, electric vehicles and aircraft, and medical devices. Li-ion batteries are well suited for portable applications because of their high energy-to-weight ratios, high energy densities, and reasonable life cycles. Current research into Li-ion batteries is focused on enhancing its energy density, and by changing the electrode materials, greater energy capacities can be realized. Silicon (Si) is a very attractive option because it has the highest known theoretical charge capacity. Current Si anodes, however, suffer from early capacity fading caused by pulverization from the stresses induced by large volumetric changes that occur during charging and discharging. An innovative system aimed at resolving this issue is being developed. This system incorporates a thin Si film bonded to an elastomeric substrate which is intended to provide the desired stress relief. Non-linear finite element simulations have shown that a significant amount of deformation can be accommodated until a critical threshold of Li concentration is reached; beyond which buckling is induced and a wavy structure appears. When compared to a similar system using rigid substrates where no buckling occurs, the stress is reduced by an order of magnitude, significantly prolonging the life of the Si anode. Thus the stress can be released at high Li-ion diffusion induced strains by buckling the Si thin film. Several aspects of this anode system have been analyzed including studying the effects of charge rate and thin film plasticity, and the results are compared with preliminary empirical measurements to show great promise. This study serves as the basis for a radical resolution to one of the few remaining barriers left in the development of high performing Si based electrodes for Li-ion batteries.
ContributorsShaffer, Joseph (Author) / Jiang, Hanqing (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Peralta, Pedro (Committee member) / Arizona State University (Publisher)
Created2011
Description
The field of Structural Health Monitoring (SHM) has grown significantly over the past few years due to safety and performance enhancing benefits as well as potential life saving capabilities offered by technology. Current advances in SHM systems have lead to a variety of techniques capable of identifying damage. However, few strategies exist for using this information to quickly react to environmental or material conditions needed to repair or protect the system. Rather, current systems simply relay this information to a central processor or human operator who then decides on a course of action, such as altering the mission or scheduling a repair operation. Biological systems exhibit many advanced sensory and healing traits that can be applied to the design of material systems. For instance, bones are the major structural component in vertebrates; however, unlike modern structural materials, bones have many properties that make it effective for arresting the development and propagation of cracks and subsequent healing of the damaged region. Mimicking biological materials, an autonomous material system was developed that uses Shape Memory Polymers (SMPs) with an embedded fiber optic network. This thesis researches a novel system that uses SMPs and employs an optical fiber network as both a damage detection sensor and a network to deliver stimulus to the damage site, initiating active toughening and healing algorithms. In the presence of damage, the fiber optic fractures, which allowed a high power laser diode to deposit a controlled level of thermal energy at the damage site, locally reducing the modulus and blunting the crack tip. The shape memory polymer not only provided a sharp glass transition, but also allowed for the application of an programmed global pre-strain, which under thermal loads induced the shape memory effect to close the crack and adequately heal the polymer to its designed operational conditions recovering full strength. It will be shown that the material can be significantly toughened and that control algorithms combined with the shape memory properties can further increase the toughening and healing effect. The entire system will be able to effectively sense damage, defend its propagation by actively toughening, and subsequently heal the structure, autonomously in a real time operational environment.
ContributorsGarcia, Michael (Author) / Sodano, Henry A (Thesis advisor) / Jiang, Hanqing (Committee member) / Lin, Yirong (Committee member) / Arizona State University (Publisher)
Created2010
Description
Ordered buckling of stiff films on elastomeric substrates has many applications in the field of stretchable electronics. Mechanics plays a very important role in such systems. A full three dimensional finite element analysis studying the pattern of wrinkles formed on a stiff film bonded to a compliant substrate under the action of a compressive force has been widely studied. For thin films, this wrinkling pattern is usually sinusoidal, and for wide films the pattern depends on loading conditions. The present study establishes a relationship between the effect of the load applied at an angle to the stiff film. A systematic experimental and analytical study of these systems has been presented in the present study. The study is performed for two different loading conditions, one with the compressive force applied parallel to the film and the other with an angle included between the application of the force and the alignment of the stiff film. A geometric model closely resembling the experimental specimen studied is created and a three dimensional finite element analysis is carried out using ABAQUS (Version 6.7). The objective of the finite element simulations is to validate the results of the experimental study to be corresponding to the minimum total energy of the system. It also helps to establish a relation between the parameters of the buckling profile and the parameters (elastic and dimensional parameters) of the system. Two methods of non-linear analysis namely, the Newton-Raphson method and Arc-Length method are used. It is found that the Arc-Length method is the most cost effective in terms of total simulation time for large models (higher number of elements).The convergence of the results is affected by a variety of factors like the dimensional parameters of the substrate, mesh density of the model, length of the substrate and the film, the angle included. For narrow silicon films the buckling profile is observed to be sinusoidal and perpendicular to the direction of the silicon film. As the angle increases in wider stiff films the buckling profile is seen to transit from being perpendicular to the direction of the film to being perpendicular to the direction of the application of the pre-stress. This study improves and expands the application of the stiff film buckling to an angled loading condition.
ContributorsKondagari, Swathi Sri (Author) / Jiang, Hanqing (Thesis advisor) / Yu, Hongyu (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2010
Description
The rheological properties at liquid-liquid interfaces are important in many industrial processes such as manufacturing foods, pharmaceuticals, cosmetics, and petroleum products. This dissertation focuses on the study of linear viscoelastic properties at liquid-liquid interfaces by tracking the thermal motion of particles confined at the interfaces. The technique of interfacial microrheology is first developed using one- and two-particle tracking, respectively. In one-particle interfacial microrheology, the rheological response at the interface is measured from the motion of individual particles. One-particle interfacial microrheology at polydimethylsiloxane (PDMS) oil-water interfaces depends strongly on the surface chemistry of different tracer particles. In contrast, by tracking the correlated motion of particle pairs, two-particle interfacial microrheology significantly minimizes the effects from tracer particle surface chemistry and particle size. Two-particle interfacial microrheology is further applied to study the linear viscoelastic properties of immiscible polymer-polymer interfaces. The interfacial loss and storage moduli at PDMS-polyethylene glycol (PEG) interfaces are measured over a wide frequency range. The zero-shear interfacial viscosity, estimated from the Cross model, falls between the bulk viscosities of two individual polymers. Surprisingly, the interfacial relaxation time is observed to be an order of magnitude larger than that of the PDMS bulk polymers. To explore the fundamental basis of interfacial nanorheology, molecular dynamics (MD) simulations are employed to investigate the nanoparticle dynamics. The diffusion of single nanoparticles in pure water and low-viscosity PDMS oils is reasonably consistent with the prediction by the Stokes-Einstein equation. To demonstrate the potential of nanorheology based on the motion of nanoparticles, the shear moduli and viscosities of the bulk phases and interfaces are calculated from single-nanoparticle tracking. Finally, the competitive influences of nanoparticles and surfactants on other interfacial properties, such as interfacial thickness and interfacial tension are also studied by MD simulations.
ContributorsSong, Yanmei (Author) / Dai, Lenore L (Thesis advisor) / Jiang, Hanqing (Committee member) / Lin, Jerry Y S (Committee member) / Raupp, Gregory B (Committee member) / Sierks, Michael R (Committee member) / Arizona State University (Publisher)
Created2011
Description
Background
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
ContributorsSun, Qian (Author) / Muckatira, Sherin (Author) / Yuan, Lei (Author) / Ji, Shuiwang (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Ira A. Fulton Schools of Engineering (Contributor)
Created2013-12-03
Description
Background:
Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.
Results:
We present a computational method to annotate gene expression pattern images automatically. The proposed method uses the bag-of-words scheme to utilize the existing information on pattern annotation and annotates images using a model that exploits correlations among terms. The proposed method can annotate images individually or in groups (e.g., according to the developmental stage). In addition, the proposed method can integrate information from different two-dimensional views of embryos. Results on embryonic patterns from BDGP data demonstrate that our method significantly outperforms other methods.
Conclusion:
The proposed bag-of-words scheme is effective in representing a set of annotations assigned to a group of images, and the model employed to annotate images successfully captures the correlations among different controlled vocabulary terms. The integration of existing annotation information from multiple embryonic views improves annotation performance.
ContributorsJi, Shuiwang (Author) / Li, Ying-Xin (Author) / Zhou, Zhi-Hua (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Ira A. Fulton Schools of Engineering (Contributor) / School of Electrical, Computer and Energy Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2009-04-21
Description
The impact of finite dielectric-covered ground-plane edge diffractions on the amplitude patterns of circular apertures is investigated. The model is based on the Geometrical Optics (GO) and the Uniform Theory of Diffraction (UTD) for an impedance wedge. The circular aperture antenna is mounted on square and circular finite ground planes that are coated with a thin lossy dielectric layer. The predictions based on the GO/UTD model are validated by comparisons to experimental results and simulated data.
ContributorsAboserwal, Nafati (Author) / Balanis, Constantine (Author) / Birtcher, Craig (Author) / Ira A. Fulton Schools of Engineering (Contributor) / School of Electrical, Computer and Energy Engineering (Contributor)
Created2014-11-30
Description
This paper presents a multiscale modeling approach to simulating the self-sensing behavior of a load sensitive smart polymer material. A statistical spring-bead based network model is developed to bridge the molecular dynamics simulations at the nanoscale and the finite element model at the macroscale. Parametric studies are conducted on the developed network model to investigate the effects of the thermoset crosslinking degree on the mechanical response of the self-sensing material. A comparison between experimental and simulation results shows that the multiscale framework is able to capture the global mechanical response with adequate accuracy and the network model is also capable of simulating the self-sensing phenomenon of the smart polymer. Finally, the molecular dynamics simulation and network model based simulation are implemented to evaluate damage initiation in the self-sensing material under monotonic loading.
ContributorsZhang, Jinjun (Author) / Koo, Bonsung (Author) / Liu, Yingtao (Author) / Zou, Jin (Author) / Chattopadhyay, Aditi (Author) / Dai, Lenore (Author) / Ira A. Fulton Schools of Engineering (Contributor) / School for the Engineering of Matter, Transport and Energy (Contributor)
Created2015-08-01