Subjective evaluation of carbon nanotube thermophones using spoken text

2020 ◽  
Vol 148 (4) ◽  
pp. 2695-2695
Author(s):  
Troy Bouman ◽  
Andrew R. Barnard
Keyword(s):  
Carbon Nanotube ◽  
Subjective Evaluation ◽  
Spoken Text

Carbon Nanotube and Graphene Device Physics

2009 ◽  
Author(s):  
H.-S. Philip Wong ◽  
Deji Akinwande
Keyword(s):  
Carbon Nanotube ◽  
Device Physics

Carbon nanotube instillation in mice causes sustained pulmonary inflammation and apoptosis of alveolar macrophages

Pneumologie ◽  
2011 ◽  
Vol 65 (12) ◽  
Author(s):  
NC Habel ◽  
S Hirn ◽  
F Tian ◽  
O Eickelberg ◽  
T Stoeger
Keyword(s):  
Carbon Nanotube ◽  
Alveolar Macrophages ◽  
Pulmonary Inflammation

A Multicenter Pharmacosurveillance Study for the Evaluation of the Efficacy and Safety of Recombinant Factor VIII in the Treatment of Patients with Hemophilia A

1997 ◽  
Vol 78 (05) ◽  
pp. 1352-1356 ◽  
Author(s):  
Emel Aygören-Pürsün ◽  
Inge Scharrer ◽  
Keyword(s):  
Factor Viii ◽  
Hemophilia A ◽  
Parvovirus B19 ◽  
Subjective Evaluation ◽  
Recombinant Factor Viii ◽  
Fviii Inhibitor ◽  
Previously Treated Patients ◽  
Previously Treated ◽  
Bleeding Episodes ◽  
Recombinant Fviii

SummaryIn this open multicenter study the safety and efficacy of recombinant factor VIII (rFVIII) was assessed in 39 previously treated patients with hemophilia A (factor VIII basal activity ≤15%).Recombinant FVIII was administered for prophylaxis and treatment of bleeding episodes and for surgical procedures. A total of 3679 infusions of rFVIII were given. Efficacy of rFVIII as assessed by subjective evaluation of response to infusion and mean annual consumption of rFVIII was comparable to that of plasma derived FVIII concentrates. The incremental recovery of FVIII (2.4 ± 0,83%/IU/kg, 2.12 ± 0.61%/IU/kg, resp.) was within the expected range. No clinical significant FVIII inhibitor was detected in this trial. Five of 16 susceptible patients showed a seroconversion for parvovirus B19. However, the results are ambiguous in two cases and might be explained otherwise in one further case. Thus, in two patients a reliable seroconversion for parvovirus B19 was observed.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Sign in / Sign up

Export Citation Format

Share Document