Interactive learning objects as a solution to challenges in basic medical science teaching

Background. As a core component of any health professions curriculum, basic medical science modules facilitate learning of biology, anatomy, histology and physiology content. To redress the challenges of class size and poor tertiary education readiness, interactive learning objects could facilitate learning and enhance engagement between lecturers and students. Objective. To determine whether the use of learning objects in a basic medical science first-year module is an effective tool for enhancing the student learning experience. Methods. A case study research design with mixed methods of data collection was used. Participants provided informed consent for this study. Learning objects were incorporated into a basic medical sciences first-year module in the Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa. A correlation analysis between usage statistics and assessment results was used to determine the academic effectiveness of this intervention. A thematic network analysis identified the barriers and enablers of the intervention. Results. Student attempts at learning objects correlated with a higher assessment outcome for two of the three tutorials. Technical difficulties, timing and assessment format were barriers to learning with the use of learning objects. Enablers to learning included student enjoyment, facilitating understanding of core concepts, adaptation to new ways of learning and formative assessment. The module team received valuable feedback on the constructed learning environment through the qualitative data collected from students within this study. Conclusion. Interactive learning objects are useful and effective tools for facilitating learning in the context of large, diverse first-year health professions education classes

Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?

In this paper, we evaluate the generalization power of deep features (ConvNets) in two new scenarios: aerial and remote sensing image classification. We evaluate experimentally ConvNets trained for recognizing everyday objects for the classification of aerial and remote sensing images. ConvNets obtained the best results for aerial images, while for remote sensing, they performed well but were outperformed by low-level color descriptors, such as BIC. We also present a correlation analysis, showing the potential for combining/fusing different ConvNets with other descriptors or even for combining multiple ConvNets. A preliminary set of experiments fusing ConvNets obtains state-of-the-art results for the well-known UCMerced dataset

Income inequality and the labour market in Britain and the US

We study household income inequality in both Great Britain and the United States and the interplay between labour market earnings and the tax system. While both Britain and the US have witnessed secular increases in 90/10 male earnings inequality over the last three decades, this measure of inequality in net family income has declined in Britain while it has risen in the US. To better understand these comparisons, we examine the interaction between labour market earnings in the family, assortative mating, the tax and welfare-benefit system and household income inequality. We find that both countries have witnessed sizeable changes in employment which have primarily occurred on the extensive margin in the US and on the intensive margin in Britain. Increases in the generosity of the welfare system in Britain played a key role in equalizing net income growth across the wage distribution, whereas the relatively weak safety net available to non-workers in the US mean this growing group has seen particularly adverse developments in their net incomes

Understanding lived experiences and perceptions of resilience in black and South Asian Muslim children living in East London: a qualitative study protocol.

INTRODUCTION: It is important to promote resilience in preadolescence; however, there is limited research on children's understandings and experiences of resilience. Quantitative approaches may not capture dynamic and context-specific aspects of resilience. Resilience research has historically focused on white, middle-class Western adults and adolescents, creating an evidence gap regarding diverse experiences of resilience in middle childhood which could inform interventions. East London's Muslim community represents a diverse, growing population. Despite being disproportionately affected by deprivation and racial and cultural discrimination, this population is under-represented in resilience research. Using participatory and arts-based methods, this study aims to explore lived experiences and perceptions of resilience in black and South Asian Muslim children living in East London. METHODS AND ANALYSIS: We propose a qualitative study, grounded in embodied inquiry, consisting of a participatory workshop with 6-12 children and their parents/carers to explore lived experiences and perceptions of resilience. Participants will be identified and recruited from community settings in East London. Eligible participants will be English-speaking Muslims who identify as being black or South Asian, have a child aged 8-12 years and live in East London. The workshop (approx. 3.5 hours) will take place at an Islamic community centre and will include body mapping with children and a focus group discussion with parents/carers to explore resilience perspectives and meanings. Participants will also complete a demographic survey. Workshop audio recordings will be transcribed verbatim and body maps and other paper-based activities will be photographed. Data will be analysed using systematic visuo-textual analysis which affords equal importance to visual and textual data. ETHICS AND DISSEMINATION: The Queen Mary Ethics of Research Committee at Queen Mary University of London has approved this study (approval date: 9 October 2023; ref: QME23.0042). The researchers plan to publish the results in peer-reviewed journals and present findings at academic conferences

Facing the Void: Overcoming Missing Data in Multi-View Imagery

In some scenarios, a single input image may not be enough to allow the object classification. In those cases, it is crucial to explore the complementary information extracted from images presenting the same object from multiple perspectives (or views) in order to enhance the general scene understanding and, consequently, increase the performance. However, this task, commonly called multi-view image classification, has a major challenge: missing data. In this paper, we propose a novel technique for multi-view image classification robust to this problem. The proposed method, based on state-of-the-art deep learning-based approaches and metric learning, can be easily adapted and exploited in other applications and domains. A systematic evaluation of the proposed algorithm was conducted using two multi-view aerial-ground datasets with very distinct properties. Results show that the proposed algorithm provides improvements in multi-view image classification accuracy when compared to state-of-the-art methods. The code of the proposed approach is available at https://github.com/Gabriellm2003/remote_sensing_missing_data.Output Status: Forthcoming/Available Onlin

‘The Rest is Silence’:Psychogeography, Soundscape and Nostalgia in Pat Collins’ Silence

Guy Debord defines the term psychogeography as 'the study of the precise laws and specific effects of the geographical environment, consciously organised or not, on the emotions and behaviour of individuals' (Debord 1955: 23). Similar to the belief of psychogeographers that the geography of an environment has a psychological effect on the human mind, proponents of acoustic ecology such as R. Murray Schafer hold that humans are affected by the sound of the environment in which they find themselves. Further to this, they examine the extent to which soundscapes can be shaped by human behaviour. Recently a body of Irish films has emerged that directly engages with the Irish soundscape and landscape on a psychogeographical level. Rather than using landscape as a physical space for the locus of action, these representations of the Irish landscape allow for an engagement with the aesthetic effects of the geographical landscape as a reflection of the psychological states of the protagonists. Bearing this in mind, this article examines how Silence (Collins 2012) arguably demonstrates the most overt and conscious incursion into this area to date. It specifically interrogates how the filmic representation of the psychogeography and soundscape of the Irish rural landscape can serve to express emotion, alienation and nostalgia, thus confronting both the Irish landscape and the weight of its associated history

Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks

Plant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plant monitoring, demanding the creation of methods capable of locating, and identifying plant species through time and space. However, this is a challenging task given the high volume of data, the constant data missing from temporal dataset, the heterogeneity of temporal profiles, the variety of plant visual patterns, and the unclear definition of individuals' boundaries in plant communities. In this letter, we propose a novel method, suitable for phenological monitoring, based on convolutional networks (ConvNets) to perform spatio-temporal vegetation pixel classification on high-resolution images. We conducted a systematic evaluation using high-resolution vegetation image datasets associated with the Brazilian Cerrado biome. Experimental results show that the proposed approach is effective, overcoming other spatio-temporal pixel-classification strategies

Facing Erosion Identification in Railway Lines Using Pixel-wise Deep-based Approaches

Soil erosion is considered one of the most expensive natural hazards with a high impact on several infrastructure assets. Among them, railway lines are one of the most likely constructions for the appearance of erosion and, consequently, one of the most troublesome due to the maintenance costs, risks of derailments, and so on. Therefore, it is fundamental to identify and monitor erosion in railway lines to prevent major consequences. Currently, erosion identification is manually performed by humans using huge image sets, a time-consuming and slow task. Hence, automatic machine learning methods appear as an appealing alternative. A crucial step for automatic erosion identification is to create a good feature representation. Towards such objective, deep learning can learn data-driven features and classifiers. In this paper, we propose a novel deep learning-based framework capable of performing erosion identification in railway lines. Six techniques were evaluated and the best one, Dynamic Dilated ConvNet, was integrated into this framework that was then encapsulated into a new ArcGIS plugin to facilitate its use by non-programmer users. To analyze such techniques, we also propose a new dataset, composed of almost 2,000 high-resolution images