scholarly journals Ultrasound-assisted transesterification of soybean oil using low power and high frequency and no external heating source

2021 ◽  
Vol 78 ◽  
pp. 105709
Author(s):  
Pâmella A. Oliveira ◽  
Raphaela M. Baesso ◽  
Gabriel C. Morais ◽  
André V. Alvarenga ◽  
Rodrigo P.B. Costa-Félix
2018 ◽  
Vol 40 ◽  
pp. 912-920 ◽  
Author(s):  
Machhindra S. Bhalerao ◽  
Vaishali M. Kulkarni ◽  
Anand V. Patwardhan

2018 ◽  
Vol 34 (2) ◽  
pp. 343-353
Author(s):  
A. Bulent Koc Koc ◽  
Bo Liu

Abstract. Ultrasound-assisted cutting has been used to cut materials with high precision, improved quality and reduced cutting forces. The research objective was to investigate the effects of high-frequency vibrations on the cutting force and cutting energy of switchgrass and miscanthus stems. Laboratory experiments were conducted on individual biomass stems at cutting speeds between 3 and 400 mm/s. An experimental cutting system with an ultrasound generator, an ultrasonic blade, a load cell, and a data acquisition system was developed. The custom designed blade was 5-cm wide and vibrated at 19.551 kHz with 2.8 µm tip vibration amplitude. There were significant measured differences in the cutting forces and cutting energies between conventional cutting and ultrasonic cutting of switchgrass and miscanthus stems (p < 0.05). These results suggest that the use of high-frequency vibrations reduce cutting force and cutting energy of both switchgrass and miscanthus stems. Ultrasound-assisted cutting reduced the cutting energy of switchgrass by 66.85% at 100 mm/s and miscanthus by 80.58% at 30 mm/s. However, ultrasonic cutting did not have a significant effect on the cutting force and cutting energy when the cutting speed was equal to or greater than the blade tip vibration speed. The results of this research should be useful for adapting the ultrasonic technology in biomass harvesting, particle size reduction, and processing equipment. Keywords: Biomass, Blades, Energy, Finite element analysis, Miscanthus, Switchgrass, Ultrasonics.


2021 ◽  
Author(s):  
Karla Burelo ◽  
Georgia Ramantani ◽  
Giacomo Indiveri ◽  
Johannes Sarnthein

Abstract Background: Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long- term EEG recording. Spiking neural networks (SNN) have been shown to be optimal architectures for being embedded in compact low-power signal processing hardware. Methods: We analyzed 20 scalp EEG recordings from 11 patients with pediatric focal lesional epilepsy. We designed a custom SNN to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band. Results: We identified the optimal SNN parameters to automatically detect EoI and reject artifacts. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.83, p < 0.0001, Spearman’s correlation).Conclusions: The fully automated SNN detected clinically relevant HFO in the scalp EEG. This is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.


Author(s):  
Y. Royter ◽  
K.R. Elliott ◽  
P.W. Deelman ◽  
R.D. Rajavel ◽  
D.H. Chow ◽  
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2009 ◽  
Vol 92 (4) ◽  
pp. 49-55
Author(s):  
Tsutomu Sasaki ◽  
Yasutomo Tanaka ◽  
Tatsuya Omori ◽  
Ken-Ya Hashimoto ◽  
Masatsune Yamaguchi

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