Demand of pyruvic acid is increasing many fold due to its multifarious applications in the food, agro, and chemical industries. Pyruvic acid production through biotechnological methods has emerged as efficient, low cost, and sustainable technology with few challenges at downstream stage (separation and purification). Reactive extraction is gaining attention over other existing method of separation of carboxylic acids from fermentation broth due to its various peculiar advantages like energy efficiency, high yield and selectivity, low cost, and environmental friendliness. In this context, reactive extraction of pyruvic acid (0.03 kmol•m −3 to 0.4 kmol•m −3 ) using tri-n-octylamine (TOA, 0.114 to 0.343 kmol•m −3 ) as an extractant and decanol and kerosene as a diluents has been studied at T = 303 K ± 1 K. The extraction efficiency using only diluent was found inferior to extractant−diluent system. Physical equilibria were interpreted in terms of partition factor (Ψ) and dimerization factor (Φ).Chemical equilibrium was treated according to mass action model and results are presented in terms of distribution coefficient (D C ), loading factor (L r ), and percent extraction (% η). Estimation of equilibrium complexation constant (β e (x,y) ) and complex stoichiometry (x:y) was ascertained by following two different methods. For (TOA + decanol) system, both (1:1) and (2:1) type acid:amine complex was proposed, whereas only (1:1) type was with (TOA + kerosene). Extraction equilibrium complexation constant for (1:1) acid:amine complex were evaluated to 133.90 m 3 •kmol −1 and 5.51 m 3 •kmol −1 for (TOA + decanol) and (TOA + kerosene) extraction system, respectively. Effect of temperature (303 K to 343 K) on the extraction was also visualized, wherein extraction decreases on increasing temperature. Enthalpy change (ΔH) for the extraction of pyruvic acid using (TOA + decanol) and (TOA + kerosene) are estimated to be −21.92 kJ•mol −1 and −10.57 kJ•mol −1 , respectively. Change in free energy (ΔG) and change in entropy (ΔS) was also computed. Up to 98 % pyruvic acid (0.05 kmol• m −3 ) could be recovered using tri-n-octylamine (0.343 kmol•m −3 ) in decanol. (TOA + decanol) is found to be a better extraction system than (TOA +kerosene) for separation of pyruvic acid. Results obtained could be utilized for design of extractor.
Hypernasality refers to the perception of excessive nasal resonances in vowels and voiced consonants. Existing speech processing based approaches concentrate only on the classification of speech into normal or hypernasal, which do not give the degree of hypernasality in terms of continuous values like nasometer. Motivated by the functionality of nasometer, in this work, a method is proposed for the evaluation of hypernasality. Speech signals representing two extremely opposite cases of nasality are used to develop the acoustic models, where oral sentences (rich in vowels, stops, and fricatives) of normal speakers and nasal sentences (rich in nasals and nasalized vowels) of moderate-severe hypernasal speakers represent the groups with minimum and maximum attainable degrees of nasality, respectively. The acoustic features derived from glottal activity regions are used to model the maximum and minimum nasality classes using Gaussian mixture model and deep neural network approaches. The posterior probabilities obtained for nasal sentence class are referred to as hypernasality scores. The scores show a significant correlation (p < 0.01) with respect to perceptual ratings of hypernasality, provided by expert speechlanguage pathologists. Further, hypernasality scores are used for the detection of hypernasality, and the results are compared with the nasometer based approach.
Airwriting recognition is the problem of identifying letters written in free space with finger movement. It is essentially a specialized case of gesture recognition, wherein the vocabulary of gestures corresponds to letters as in a particular language. With the wide adoption of smart wearables in the general population, airwriting recognition using motion sensors from a smart band can be used as a medium of user input for applications in human-computer interaction. There has been limited work in the recognition of in-air trajectories using motion sensors, and the performance of the techniques in the case when the device used to record signals is changed has not been explored hitherto. Motivated by these, a new paradigm for device and user-independent airwriting recognition based on supervised contrastive learning is proposed. A two-stage classification strategy is employed, the first of which involves training an encoder network with supervised contrastive loss. In the subsequent stage, a classification head is trained with the encoder weights kept frozen. The efficacy of the proposed method is demonstrated through experiments on a publicly available dataset and also with a dataset recorded in our lab using a different device. Experiments have been performed in both supervised and unsupervised settings and compared against several state-of-the-art domain adaptation techniques. Data and the code for our implementation will be made available at https://github.com/ayushayt/SCLAiR.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.