Commuting, transitions and belonging: the experiences of students living at home in their first year at university

In this study, our cross-case analysis of students’ lives challenges the conventional home–university model of transition and highlights the importance of acknowledging the influence of this complex symbiotic relationship for students who attend university and live at home. We argue that as with stay-at-home holidays, or “staycations”, which are of such crucial importance to the tourism industry, so stay-at-home students or commuter students are vital to higher education and the term utilised here is “stayeducation”. Through the narratives of “stayeducation” students, we see how family and community aspects of students’ lives are far more significant than previously realised, and our study suggests that these heavily influence the development of a student sense of belonging. Drawing upon biographical narrative method, this paper introduces three first-year Business and Economics students enrolled at different universities in London and explores their journeys through their transition through home, school and early university life. Ways in which key themes play out in the transition stories of our students and the challenges and obstacles for the individual are drawn out through the cross-case analysis. Findings support the existing literature around gender, class and identity; however, new insights into the importance, for these students, of family, friendships and community are presented. Our work has implications for academic staff, those writing institutional policies, and argues for the creation of different spaces within which students can integrate into their new environment

Recognition of Prior Learning (RPL) as pedagogical pragmatism

This case study is located within a joint venture between a University and a College of Further Education, with an explicit mission to bring into Higher Education under-represented groups, including mature learners and to promote part-time education. Our case study provides a review and evaluation of the successful development of an undergraduate programme in leadership and professional development for experienced learners, two-thirds of which is awarded through the Recognition of Prior Learning (RPL). The case study demonstrates how the research-informed RPL design has enabled the recognition of prior learning in a way that is both true to the students’ experience and works within the parameters of quality assurance frameworks. The term used is pedagogical pragmatism i.e. a process that rests on particular combinations of both technical rationality (e.g adherence to a Learning Outcome focused output which is static and a "given") and professional artistry (differential judgements made about the efficacy of approaches to RPL). The practices are contextual, however we would argue that sharing the conditions that underpin RPL as a specialised pedagogic practice is an important part of moving this agenda forward in the sector

Is speech the new blood? Recent progress in AI-based disease detection from audio in a nutshell

In recent years, advancements in the field of artificial intelligence (AI) have impacted several areas of research and application. Besides more prominent examples like self-driving cars or media consumption algorithms, AI-based systems have further started to gain more and more popularity in the health care sector, however whilst being restrained by high requirements for accuracy, robustness, and explainability. Health-oriented AI research as a sub-field of digital health investigates a plethora of human-centered modalities. In this article, we address recent advances in the so far understudied but highly promising audio domain with a particular focus on speech data and present corresponding state-of-the-art technologies. Moreover, we give an excerpt of recent studies on the automatic audio-based detection of diseases ranging from acute and chronic respiratory diseases via psychiatric disorders to developmental disorders and neurodegenerative disorders. Our selection of presented literature shows that the recent success of deep learning methods in other fields of AI also more and more translates to the field of digital health, albeit expert-designed feature extractors and classical ML methodologies are still prominently used. Limiting factors, especially for speech-based disease detection systems, are related to the amount and diversity of available data, e. g., the number of patients and healthy controls as well as the underlying distribution of age, languages, and cultures. Finally, we contextualize and outline application scenarios of speech-based disease detection systems as supportive tools for health-care professionals under ethical consideration of privacy protection and faulty prediction

The acoustic dissection of cough: diving into machine listening-based COVID-19 analysis and detection

OBJECTIVES: The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19′s transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge of the acoustic characteristics of COVID-19 cough sounds is limited but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds. METHODS: By applying conventional inferential statistics, we analyze the acoustic correlates of COVID-19 cough sounds based on the ComParE feature set, i.e., a standardized set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions. RESULTS: The experimental results demonstrate that a set of acoustic parameters of cough sounds, e.g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, bear essential acoustic information in terms of effect sizes for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our general automatic COVID-19 detection model performs significantly above chance level, i.e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201). CONCLUSIONS: Based on the acoustic correlates analysis on the ComParE feature set and the feature analysis in the effective COVID-19 detection approach, we find that several acoustic features that show higher effects in conventional group difference testing are also higher weighted in the machine learning models

New, nearby bright southern ultracool dwarfs

We report the discovery of twenty-one hitherto unknown bright southern ultracool dwarfs with spectral types in the range M7 to L5.5, together with new observations of a further three late M dwarfs previously confirmed. Three more objects are already identified in the literature as high proper motion stars;we derive their spectral types for the first time. All objects were selected from the 2MASS All Sky and SuperCOSMOS point source databases on the basis of their optical/near-infrared colours, $J$-band magnitudes and proper motions. Low resolution (R $\sim$ 1000) $JH$ spectroscopy with the ESO/NTT SOFI spectrograph has confirmed the ultracool nature of 24 targets, out of a total of 25 candidates observed. Spectral types are derived by direct comparison with template objects and compared to results from H$_2$O and FeH indices. We also report the discovery of one binary, as revealed by SOFI acquisition imaging; spectra were taken for both components. The spectral types of the two components are L2 and L4 and the distance $\sim$ 19 pc. Spectroscopic distances and transverse velocities are derived for the sample. Two $\sim$ L5 objects lie only $\sim$ 10 pc distant. Such nearby objects are excellent targets for further study to derive their parallaxes and to search for fainter, later companions with AO and/or methane imaging.Comment: 11 pages, 10 figures, accepted to MNRA

Study of polycaprolactone wet electrospinning process

Wet electrospinning is a useful method for 3-dimensional structure control of nanofibrous materials. This innovative technology uses a liquid collector instead of the metal one commonly used for standard electrospinning. The article compares the internal structural features of polycaprolactone (PCL) nanofibrous materials prepared by both technologies. We analyze the influence of different water/ethanol compositions used as a liquid collector on the morphology of the resultant polycaprolactone nanofibrous materials. Scanning electron micro-photographs have revealed a bimodal structure in the wet electrospun materials composed of micro and nanofibers uniformly distributed across the sample bulk. We have shown that the full-faced, twofold fiber distribution is due to the solvent composition and is induced and enhanced by increasing the ethanol weight ratio. Moreover, the comparison of fibrous layers morphology obtained by wet and dry spinning have revealed that beads that frequently appeared in dry spun materials are created by Plateau-Rayleigh instability of the fraction of thicker fibers. Theoretical conditions for spontaneous and complete immersion of cylindrical fibers into a liquid collector are also derived here