Identifying Consumer Perceptions of Fresh-market Blackberries

Blackberries are grown worldwide for commercial fresh markets, but there is limited information on consumer perceptions of this fruit. In this study, physiochemical and consumer sensory attributes of three Arkansas-grown fresh-market blackberry genotypes were evaluated and consumer perceptions of fresh-market blackberries were also investigated though an online survey. Two cultivars (Natchez and Ouachita) and one advanced selection (A-2418) were evaluated for compositional and nutraceutical analysis and consumer sensory analysis. Natchez had the highest berry weight, length, drupelets and pyrenes/berry, and pyrene weight/berry. Ouachita had the highest soluble solids content (11.9%), pH (3.18) and soluble solids/titratable acidity ratio (10.92). There were no significant differences between genotypes for titratable acidity, organic acids, sugars, and most of the nutraceuticals. In a sensory panel (n = 80) of these genotypes, consumers rated Natchez highest in overall impression, overall flavor, and sweetness, and Natchez was ranked as the most liked blackberry more often than Ouachita or A-2418 on a 9-point verbal hedonic liking scale and 5-point Just About Right scale. An online consumer survey (n = 879) was done to gain information on consumers’ opinions and habits relating to fresh-market blackberries. Results indicated the most important factors to influence blackberry purchases are the freshness of the berries, the type and size of package, the uniformity of berry color, and the price. Results also suggested consumers prefer larger sized blackberries and blackberries with an oblong shape. Identifying marketability attributes of fresh-market blackberries helps provide information to advance breeding efforts for fruit with commercial potential

Evaluating Consumer Sensory and Composition Attributes of Arkansas-Grown Fresh-Market Blackberries

Blackberries are grown worldwide for commercial fresh markets. Three Arkansas-grown fresh-market blackberry genotypes (‘Natchez’, ‘Ouachita’, and A-2418) were evaluated for consumer sensory and compositional attributes at the University of Arkansas Food Science Department, Fayetteville. The compositional attributes of the blackberries were within an acceptable range for commercial markets (soluble solids=8.20-11.90%, pH=2.79-3.18, titratable acidity=1.09-1.32%). In terms of soluble solids to titratable acidity ratio, ‘Ouachita’ (10.92) had the highest ratio, followed by ‘Natchez’ (8.93) and A-2418 (6.25). A consumer sensory panel (n=80) evaluated fresh-market blackberry attributes using a 9-point hedonic scale for overall impression, overall flavor, sweetness, and sourness and a 5-point Just-about-Right (JAR) scale for sweetness and sourness. The participants also ranked the blackberries in order of overall liking from most to least liked. For overall impression, overall flavor, and sweetness, ‘Natchez’ scored higher than ‘Ouachita’ and A-2418, but the panelists did not detect differences in sourness. In terms of JAR for sweetness, 64% of consumers scored ‘Natchez’ JAR, followed by ‘Ouachita’ (39%) and A-2418 (34%). Whereas, 42% percent found A-2418 “Too Sour”, followed by ‘Ouachita (33%) and ‘Natchez’ (25%). In terms of ranking the blackberries, ‘Natchez’ was the most liked blackberry followed by ‘Ouachita’ and A-2418. When looking only at blackberries ranked first, 53% of consumers ranked ‘Natchez’ as their most liked berry, compared to A-2418 (26%) and ‘Ouachita’ (21%). The results from this research suggested that fresh-market blackberries with medium-level sweetness to sourness ratios were preferred though more consumers than expected preferred the blackberries with the more extreme ratios

The Influence of Prenatal Yoga on Maternal Identity Formation

Introduction: •The social groups that one belongs to influence the creation of self-identity •Changes in self-identity that occur during pregnancy set the stage for how a woman is able to incorporate her child into her own self-identity •Prenatal yoga reduces stress and anxiety during pregnancy, leading to increased feelings of attachment to the baby •Limited research on prenatal yoga and maternal identity formation Research Aim: To examine how participating in a prenatal yoga class with other mothers-to-be may influence maternal identity formation

An Augmented Reality Application for Personalised Diamond Shopping

This paper presents an Augmented Reality (AR) Application (App) developed for bespoke jewellery solutions. The App allows users to experience a visual representation of diamond rings through their mobile device. Customers could design and review bespoke jewellery without having to visit the jeweller’s shop and could communicate changes to the jeweller in real-time (saving time and money). The use of AR would allow customers to view their bespoke jewellery on their fingers to gain a better idea of how it would look once completed. Twenty seven participants evaluated the App by completing a questionnaire after using it. The application of Exploratory Factor Analysis resulted in four factors (compatibility, likeability, functionality and usability). Overall, the participants appreciated the AR App; regardless of their gender, age and experience

Investigating Rumor News Using Agreement-Aware Search

Recent years have witnessed a widespread increase of rumor news generated by humans and machines. Therefore, tools for investigating rumor news have become an urgent necessity. One useful function of such tools is to see ways a specific topic or event is represented by presenting different points of view from multiple sources. In this paper, we propose Maester, a novel agreement-aware search framework for investigating rumor news. Given an investigative question, Maester will retrieve related articles to that question, assign and display top articles from agree, disagree, and discuss categories to users. Splitting the results into these three categories provides the user a holistic view towards the investigative question. We build Maester based on the following two key observations: (1) relatedness can commonly be determined by keywords and entities occurring in both questions and articles, and (2) the level of agreement between the investigative question and the related news article can often be decided by a few key sentences. Accordingly, we use gradient boosting tree models with keyword/entity matching features for relatedness detection, and leverage recurrent neural network to infer the level of agreement. Our experiments on the Fake News Challenge (FNC) dataset demonstrate up to an order of magnitude improvement of Maester over the original FNC winning solution, for agreement-aware search

An Intelligent Architecture Based on Field Programmable Gate Arrays Designed to Detect Moving Objects by Using Principal Component Analysis

This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices

Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations

Holistic corpus-based dialectology

This paper is concerned with sketching future directions for corpus-based dialectology. We advocate a holistic approach to the study of geographically conditioned linguistic variability, and we present a suitable methodology, 'corpusbased dialectometry', in exactly this spirit. Specifically, we argue that in order to live up to the potential of the corpus-based method, practitioners need to (i) abandon their exclusive focus on individual linguistic features in favor of the study of feature aggregates, (ii) draw on computationally advanced multivariate analysis techniques (such as multidimensional scaling, cluster analysis, and principal component analysis), and (iii) aid interpretation of empirical results by marshalling state-of-the-art data visualization techniques. To exemplify this line of analysis, we present a case study which explores joint frequency variability of 57 morphosyntax features in 34 dialects all over Great Britain

Measuring self-efficacy to deal with infertility: Psychometric properties and confirmatory factor analysis of the Portuguese version of the infertility self-efficacy scale

This study explores the psychometric properties and factor structure of the Portuguese version of the Infertility Self-Efficacy Scale (ISE-P), using translation and back-translation of the original version; principal component analysis; confirmatory factor analysis (CFA); and internal consistency, and test-retest reliability analyses. A total of 287 participants (156 women and 131 men) seeking medical treatment were recruited from public and private fertility centers. CFA revealed that the single-component model fit the data well. The instrument showed excellent internal consistency, good test-retest reliability, and correlations with other mental health measures suggesting good convergent and discriminant validity. In conclusion, The ISE-P is a valid and reliable Portuguese-language measure of perceived self-efficacy to cope with infertility