Search results for: Demircan Canadinc

Authors: Burak Bal, Demircan Canadinc

Abstract:

Dynamic strain aging (DSA), resulting from the reorientation of C-Mn clusters in the core of dislocations, can provide a strain hardening mechanism. In addition, in Hadfield steel, negative strain rate sensitivity is observed due to the DSA. In our study, we incorporated dynamic strain aging onto crystal plasticity computations to predict the local instabilities and corresponding negative strain rate sensitivity. Specifically, the material response of Hadfield steel was obtained from monotonic and strain-rate jump experiments under tensile loading. The strain rate range was adjusted from 10⁻⁴ to 10⁻¹s ⁻¹. The crystal plasticity modeling of the material response was carried out based on Voce-type hardening law and corresponding Voce hardening parameters were determined. The solute pinning effect of carbon atom was incorporated to crystal plasticity simulations at microscale level by computing the shear stress contribution imposed on an arrested dislocation by carbon atom. After crystal plasticity simulations with modifying hardening rule, which takes into account the contribution of DSA, it was seen that the model successfully predicts both the role of DSA and corresponding strain rate sensitivity.

Keywords: crystal plasticity, dynamic strain aging, Hadfield steel, negative strain rate sensitivity

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Authors: Elnaz Lashgari, Emel Demircan

Abstract:

Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

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Authors: Beyza Sukran Isik, Gokce Altin, Ipek Yalcinkaya, Evren Demircan, Asli Can Karaca, Beraat Ozcelik

Abstract:

Turmeric (Curcuma longa L.), is used as a food additive (spice), preservative and coloring agent in Asian countries, including China and South East Asia. It is also considered as a medicinal plant. Traditional Indian medicine evaluates turmeric powder for the treatment of biliary disorders, rheumatism, and sinusitis. It has rich polyphenol content. Turmeric has yellow color mainly because of the presence of three major pigments; curcumin 1,7-bis(4-hydroxy-3-methoxyphenyl)-1, 6-heptadiene-3,5-dione), demethoxy-curcumin and bis demothoxy-curcumin. These curcuminoids are recognized to have high antioxidant activities. Curcumin is the major constituent of Curcuma species. Method: To prepare turmeric tea, 0.5 gram of turmeric powder was brewed with 250 ml of water at 90°C, 10 minutes. 500 grams of fresh turmeric washed and shelled prior to squeezing. Both turmeric tea and turmeric juice pass through 45 lm filters and stored at -20°C in the dark for further analyses. Curcumin was extracted from 20 grams of turmeric powder by 70 ml ethanol solution (95:5 ethanol/water v/v) in a water bath at 80°C, 6 hours. Extraction was contributed for 2 hours at the end of 6 hours by addition of 30 ml ethanol. Ethanol was removed by rotary evaporator. Remained extract stored at -20°C in the dark. Total phenolic content and phenolic profile were determined by spectrophotometric analysis and ultra-fast liquid chromatography (UFLC), respectively. Results: The total phenolic content of ethanolic extract of turmeric, turmeric juice, and turmeric tea were determined 50.72, 31.76 and 29.68 ppt, respectively. The ethanolic extract of turmeric, turmeric juice, and turmeric tea have been injected into UFLC and analyzed for curcumin contents. The curcumin content in ethanolic extract of turmeric, turmeric juice, and turmeric tea were 4067.4, 156.7 ppm and 1.1 ppm, respectively. Significance: Turmeric is known as a good source of curcumin. According to the results, it can be stated that its tea is not sufficient way for curcumin consumption. Turmeric juice can be preferred to turmeric tea for higher curcumin content. Ethanolic extract of turmeric showed the highest content of turmeric in both spectrophotometric and chromatographic analyses. Nonpolar solvents and carriers which have polar binding sites have to be considered for curcumin consumption due to its nonpolar nature.

Keywords: phenolic compounds, spectrophotometry, turmeric, UFLC

Procedia PDF Downloads 199