The effect of sourdough, turnips, and butternut squash on the physicochemical and nutritional properties of Doowina functional food during fermentation.

The dairy-cereal-based food, known as Doowina, is one of the traditional fermented foods in Iran. We aimed to improve the health-promoting properties of Doowina by using turnips, butternut squash, and sourdough as a new functional food with high nutritional value and antioxidant activity. Therefore, the physicochemical, microbial, and sensory properties of samples with nutritional supplements (8% turnip and 8% butternut squash) and different concentrations of sourdough (0, 0.5, and 1%) were studied during 0, 3, 6, and 9 days of fermentation time. The results showed that there was no significant difference (p < .05) in the moisture and ash content between the different formulations of Doowina. There was also no significant difference (p < .05) in the phenolic compound content and antioxidant activity of the Doowina samples during the fermentation period. However, the number of lactic acid bacteria (LAB) increased significantly (p < .05) until the 6th day of fermentation, and the protein content decreased significantly (p < .05) in all samples during the fermentation period. According to the results, the samples with butternut squash and sourdough had the highest total phenolic content, the highest antioxidant activity, the highest linoleic acid content, and the highest sensory rating of all samples.

Reconsidering Luria's speech mediation: Verbalization and haptic picture identification in children with congenital total blindness.

Current accounts of behavioral and neurocognitive correlates of plasticity in blindness are just beginning to incorporate the role of speech and verbal production. We assessed Vygotsky/Luria's speech mediation hypothesis, according to which speech activity can become a mediating tool for perception of complex stimuli, specifically, for encoding tactual/haptic spatial patterns which convey pictorial information (haptic pictures). We compared verbalization in congenitally totally blind (CTB) and age-matched sighted but visually impaired (VI) children during a haptic picture naming task which included two repeated, test-retest, identifications. The children were instructed to explore 10 haptic schematic pictures of objects (e.g., cup) and body parts (e.g., face) and provide (without experimenter's feedback) their typical name. Children's explorations and verbalizations were videorecorded and transcribed into audio segments. Using the Computerized Analysis of Language (CLAN) program, we extracted several measurements from the observed verbalizations, including number of utterances and words, utterance/word duration, and exploration time. Using the Word2Vec natural language processing technique we operationalized semantic content from the relative distances between the names provided. Furthermore, we conducted an observational content analysis in which three judges categorized verbalizations according to a rating scale assessing verbalization content. Results consistently indicated across all measures that the CTB children were faster and semantically more precise than their VI counterparts in the first identification test, however, the VI children reached the same level of precision and speed as the CTB children at retest. Overall, the task was harder for the VI group. Consistent with current neuroscience literature, the prominent role of speech in CTB and VI children's data suggests that an underlying cross-modal involvement of integrated brain networks, notably associated with Broca's network, likely also influenced by Braille, could play a key role in compensatory plasticity via the mediational mechanism postulated by Luria.

Machine Learning for Touch Localization on an Ultrasonic Lamb Wave Touchscreen.

Classification and regression employing a simple Deep Neural Network (DNN) are investigated to perform touch localization on a tactile surface using ultrasonic guided waves. A robotic finger first simulates the touch action and captures the data to train a model. The model is then validated with data from experiments conducted with human fingers. The localization root mean square errors (RMSE) in time and frequency domains are presented. The proposed method provides satisfactory localization results for most human-machine interactions, with a mean error of 0.47 cm and standard deviation of 0.18 cm and a computing time of 0.44 ms. The classification approach is also adapted to identify touches on an access control keypad layout, which leads to an accuracy of 97% with a computing time of 0.28 ms. These results demonstrate that DNN-based methods are a viable alternative to signal processing-based approaches for accurate and robust touch localization using ultrasonic guided waves.