Further SEASAT SAR coastal ocean wave analysis

Analysis techniques used to exploit SEASAT synthetic aperture radar (SAR) data of gravity waves are discussed and the SEASAT SAR's ability to monitor large scale variations in gravity wave fields in both deep and shallow water is evaluated. The SAR analysis techniques investigated included motion compensation adjustments and the semicausal model for spectral analysis of SAR wave data. It was determined that spectra generated from fast Fourier transform analysis (FFT) of SAR wave data were not significantly altered when either range telerotation adjustments or azimuth focus shifts were used during processing of the SAR signal histories, indicating that SEASAT imagery of gravity waves is not significantly improved or degraded by motion compensation adjustments. Evaluation of the semicausal (SC) model using SEASAT SAR data from Rev. 974 indicates that the SC spectral estimates were not significantly better than the FFT results

Primary breakup in gas/liquid mixing layers for turbulent liquids

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77169/1/AIAA-1992-462-385.pd

Dispersed-phase structure of pressure-atomized sprays at various gasdensities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76056/1/AIAA-1992-230-405.pd

Graded index and randomly oriented core-shell silicon nanowires with broadband and wide angle antireflection

Antireflection with broadband and wide angle properties is important for a wide range of applications on photovoltaic cells and display. The SiOx shell layer provides a natural antireflection from air to the Si core absorption layer. In this work, we have demonstrated the random core-shell silicon nanowires with both broadband (from 400nm to 900nm) and wide angle (from normal incidence to 60o) antireflection characteristics within AM1.5 solar spectrum. The graded index structure from the randomly oriented core-shell (Air/SiOx/Si) nanowires may provide a potential avenue to realize a broadband and wide angle antireflection laye

GDNF reduces drug-induced rotational behavior after medial forebrain bundle transection by a mechanism not involving striatal dopamine

Parkinson's disease (PD) is characterized by the progressive loss of the substantia nigra (SN) dopaminergic neurons projecting to the striatum. Neurotrophic factors may have the potential to prevent or slow down the degenerative process occurring in PD. To that end, we examined whether low amounts of glial cell line-derived neurotrophic factor (GDNF) continuously released from polymer-encapsulated genetically engineered cells are able to prevent the loss of tyrosine hydroxylase immunoreactivity (TH-IR) in SN neurons and ameliorate the amphetamine-induced rotational asymmetry in rats that have been subjected to a unilateral medial forebrain bundle (MFB) axotomy. Baby hamster kidney (BHK) cells transfected with the cDNA for GDNF were encapsulated in a polymer fiber and implanted unilaterally at a location lateral to the MFB and rostral to the SN. ELISA assays before implantation show that the capsules release approximately 5 ng of GDNF/capsule per day. One week later, the MFB was axotomized unilaterally ipsilateral to the capsule placement. Seven days later, the animals were tested for amphetamine-induced rotational asymmetry and killed. The striatum was excised and analyzed either for catecholamine content or TH-IR, while the SN was immunostained for the presence of TH-IR. GDNF did not prevent the loss of dopamine in the striatum. However, GDNF significantly rescued TH-IR neurons in the SN pars compacta. Furthermore, GDNF also significantly reduced the number of turns per minute ipsilateral to the lesion under the influence of amphetamine. Improvement of rotational behavior in the absence of dopaminergic striatal reinnervation may reflect neuronal plasticity in the SN, as suggested by the dendritic sprouting observed in animals receiving GDNF. These results illustrate that the continuous release of low levels of GDNF close to the SN is capable of protecting the nigral dopaminergic neurons from an axotomy-induced lesion and significantly improving pharmacological rotational behavior by a mechanism other than dopaminergic striatal reinnervation

Mining Partially-Ordered Sequential Rules Common to Multiple Sequences

© 2015 IEEE. Sequential rule mining is an important data mining problem with multiple applications. An important limitation of algorithms for mining sequential rules common to multiple sequences is that rules are very specific and therefore many similar rules may represent the same situation. This can cause three major problems: (1) similar rules can be rated quite differently, (2) rules may not be found because they are individually considered uninteresting, and (3) rules that are too specific are less likely to be used for making predictions. To address these issues, we explore the idea of mining "partially-ordered sequential rules" (POSR), a more general form of sequential rules such that items in the antecedent and the consequent of each rule are unordered. To mine POSR, we propose the RuleGrowth algorithm, which is efficient and easily extendable. In particular, we present an extension (TRuleGrowth) that accepts a sliding-window constraint to find rules occurring within a maximum amount of time. A performance study with four real-life datasets show that RuleGrowth and TRuleGrowth have excellent performance and scalability compared to baseline algorithms and that the number of rules discovered can be several orders of magnitude smaller when the sliding-window constraint is applied. Furthermore, we also report results from a real application showing that POSR can provide a much higher prediction accuracy than regular sequential rules for sequence prediction