3,848 research outputs found
Analytical studies of new airfoils for wind turbines
Computer studies were conducted to analyze the potential gains associated with utilizing new airfoils for large wind turbine rotor blades. Attempts to include 3-dimensional stalling effects were inconclusive. It is recommended that blade pressure measurements be made to clarify the nature of blade stalling. It is also recommended that new laminar flow airfoils be used as rotor blade sections
Feasibility study of aileron and spoiler control systems for large horizontal axis wind turbines
The feasibility of using aileron or spoiler controls as alternates to pitch control for large horizontal axis wind turbines was studied. The NASA Mod-0 100 kw machine was used as the basis for the study. Specific performance studies were conducted for 20% chord ailerons over the outboard 30% span, and for 10% chord spoilers over the same portion of the span. Both control systems utilized control deflections up to 60 deg. Results of the study show that either ailerons or spoilers can provide the control necessary to limit turbine power in high wind conditions. The aileron system, as designed, provides overspeed protection at hurricane wind speeds, low wind speed starting torque of 778 N-m (574 ft. lb) at 3.6 m/sec, and a 1.3 to 1.5% increase in annual energy compared to a fixed pitch rotor. The aileron control system preliminary design study includes aileron loads analysis and the design of a failsafe flyweight actuator for overspeed protection in the event of a hydraulic system failure
Squamous Cell Carcinoma of Horse and Cow
On March 31, 1942 an eight-year-old Hereford cow was admitted to the clinic. No history of the case was obtained
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Aberrant activity in conceptual networks underlies N400 deficits and unusual thoughts in schizophrenia.
BackgroundThe N400 event-related potential (ERP) is triggered by meaningful stimuli that are incongruous, or unmatched, with their semantic context. Functional magnetic resonance imaging (fMRI) studies have identified brain regions activated by semantic incongruity, but their precise links to the N400 ERP are unclear. In schizophrenia (SZ), N400 amplitude reduction is thought to reflect overly broad associations in semantic networks, but the abnormalities in brain networks underlying deficient N400 remain unknown. We utilized joint independent component analysis (JICA) to link temporal patterns in ERPs to neuroanatomical patterns from fMRI and investigate relationships between N400 amplitude and neuroanatomical activation in SZ patients and healthy controls (HC).MethodsSZ patients (n = 24) and HC participants (n = 25) performed a picture-word matching task, in which words were either matched (APPLE→apple) by preceding pictures, or were unmatched by semantically related (in-category; IC, APPLE→lemon) or unrelated (out of category; OC, APPLE→cow) pictures, in separate ERP and fMRI sessions. A JICA "data fusion" analysis was conducted to identify the fMRI brain regions specifically associated with the ERP N400 component. SZ and HC loading weights were compared and correlations with clinical symptoms were assessed.ResultsJICA identified an ERP-fMRI "fused" component that captured the N400, with loading weights that were reduced in SZ. The JICA map for the IC condition showed peaks of activation in the cingulate, precuneus, bilateral temporal poles and cerebellum, whereas the JICA map from the OC condition was linked primarily to visual cortical activation and the left temporal pole. Among SZ patients, fMRI activity from the IC condition was inversely correlated with unusual thought content.ConclusionsThe neural networks associated with the N400 ERP response to semantic violations depends on conceptual relatedness. These findings are consistent with a distributed network underlying neural responses to semantic incongruity including unimodal visual areas as well as integrative, transmodal areas. Unusual thoughts in SZ may reflect impaired processing in transmodal hub regions such as the precuneus, leading to overly broad semantic associations
Excitation of the n=2 State of Atomic Hydrogen by Electron Impact in the Distorted-Wave Approximation
The differential cross sections for excitation of the n=2 state of atomic hydrogen by the impact of electrons with energies of 54, 100, 136, and 200 eV are calculated in the distorted-wave approximation with exchange included. Cross sections for excitation of the 2s1/2, 2p1/2, and 2p3/2 states are calculated and summed to give the n=2 differential cross sections. The results of this calculation are compared with previous theoretical calculations and with recent absolute experimental data
Erratum: Excitation of the n=2 State of Atomic Hydrogen by Electron Impact in the Distorted-Wave Approximation (Physical Review A (1977) 16, 2)
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
Behavioral changes are the earliest signs of a mental disorder, but arguably,
the dynamics of brain function gets affected even earlier. Subsequently,
spatio-temporal structure of disorder-specific dynamics is crucial for early
diagnosis and understanding the disorder mechanism. A common way of learning
discriminatory features relies on training a classifier and evaluating feature
importance. Classical classifiers, based on handcrafted features are quite
powerful, but suffer the curse of dimensionality when applied to large input
dimensions of spatio-temporal data. Deep learning algorithms could handle the
problem and a model introspection could highlight discriminatory
spatio-temporal regions but need way more samples to train. In this paper we
present a novel self supervised training schema which reinforces whole sequence
mutual information local to context (whole MILC). We pre-train the whole MILC
model on unlabeled and unrelated healthy control data. We test our model on
three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers
and four different studies. Our algorithm outperforms existing self-supervised
pre-training methods and provides competitive classification results to
classical machine learning algorithms. Importantly, whole MILC enables
attribution of subject diagnosis to specific spatio-temporal regions in the
fMRI signal.Comment: Accepted at MICCAI 2020. arXiv admin note: substantial text overlap
with arXiv:1912.0313
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