Transformative Events:A Migrant Narrative of Identity and Belonging at the Edinburgh Festival Fringe

This chapter presents a personal case study of a Syrian scholar performing research on stage at the Edinburgh Festival Fringe. It explores how narrative performances can challenge stereotypes and singular narratives about migration, fostering a sense of belonging and identity among migrants. The chapter emphasizes the transformative power of storytelling events in creating inclusive spaces and promoting social change. It highlights the importance of narrative performance as a method for engaging public audiences and addressing issues of migration and displacement

Overlapping Direct Radiating Arrays with an Interleaved Layer of Tiles for Satellite Communications

Hybrid beamforming schemes have been welcomed in the past years as a prolific way to reduce the digital complexity of direct radiating arrays. Nevertheless, such cascade of analog and digital beamforming implies the addition of interfering grating lobes, considered as highly problematic in broadband satellite communications. Overlapping is seen as a solution and has emerged in the literature as an efficient technique to partially mitigate some or all these unwanted lobes. Often, it guarantees strong performances by increasing the analog complexity and is limited by the hardware implementation on-board the satellite. A solution is proposed in the abstract that avoids such limitations. An additional layer of tiles shifted by half the size of a sub array along 2-D is interleaved with the classic hybrid beamforming scheme. The advantages and drawbacks of the methodology are discussed

A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data

Energy dispersive X-ray (EDX) spectrum imaging yields compositional information with a spatial resolution down to the atomic level. However, experimental limitations often produce extremely sparse and noisy EDX spectra. Under such conditions, every detected X-ray must be leveraged to obtain the maximum possible amount of information about the sample. To this end, we introduce a robust multiscale Bayesian approach that accounts for the Poisson statistics in the EDX data and leverages their underlying spatial correlations. This is combined with EDX spectral simulation (elemental contributions and Bremsstrahlung background) into a Bayesian estimation strategy. When tested using simulated datasets, the chemical maps obtained with this approach are more accurate and preserve a higher spatial resolution than those obtained by standard methods. These properties translate to experimental datasets, where the method enhances the atomic resolution chemical maps of a canonical tetragonal ferroelectric PbTiO3 sample, such that ferroelectric domains are mapped with unit-cell resolution

Multiband waveguide filters with advanced filtering characteristics based on an in‐band transmission zeros method and stacked cylindrical resonators

This paper significantly improves the previously proposed novel multiband waveguide filter implementation employing cylindrical resonators. The improved model has the advantages of a further reduced footprint using stacking shunt resonators horizontally and vertically and the ability to realise advanced filtering functions, including transmission zeros below and above the passbands. The coupling matrix synthesis with a brief example and a detailed filter design with considerations for additional coupling and in‐line and folded topologies is given. Several filter prototypes, namely third‐order quad‐band and quintuple‐band in‐line filters and a sixth‐order dual‐band folded filter in Ku‐band, were designed to validate the proposed model. Selective laser melting (SLM), a metal 3‐D printing technique where metal powder is selectively melted with a laser layer by layer, was used to fabricate a dual‐band folded filter prototype in copper to validate the proposed model since the model has a complex inner geometry. Additionally, selective laser melting has the advantage of monolithic near‐net shape fabrication, eliminating assembly, improving reliability, and reducing weight. The measured results show good agreement with the simulations

Exploring the Viability of Socially Assistive Robots for At-Home Cognitive Monitoring:Potential and Limitations

The early detection of mild cognitive impairment, a condition of increasing impact in our aging society, is a challenging task with no established answer. One promising solution is the deployment of robotic systems and ambient assisted living technology in the houses of older adults for monitoring and assistance. In this work, we address and discuss a qualitative analysis on the feasibility and acceptability of a socially assistive robot (SAR) deployed in prospective users’ houses to monitor their cognitive capabilities through a set of digitalised neuropsychological tests and spot questions conveniently integrated within the robotic assistant’s daily tasks. We do this by describing an experimental campaign where a robotic system, integrated with a larger framework, was installed in the house of 10 users for a duration of at least 10 weeks, during which their cognitive capabilities were monitored by the robot. Concretely, the robots supervised the users during the completion of the tests and transparently monitored them by asking questions interleaved in their everyday activities. Results show a general acceptance of such technology, being able to carry out the intended tasks without being too invasive, paving the way for an impactful at-home use of SARs.</p

Feasibility analysis of EIT-guided lung tumor tracking with prior information for robotic arm-assisted radiotherapy

Lung cancer remains one of the most prevalent and lethal forms of cancer worldwide. Radiotherapy is an effective therapeutic strategy for its management, where precise tumor localization plays a pivotal role in ensuring treatment accuracy and efficacy. Electrical Impedance Tomography (EIT), a non-invasive and cost-effective imaging technique with a high temporal resolution, shows promise for tissue abnormality detection by exploiting the inherent differences in electrical properties among biological tissues. In this study, we propose a method that combines EIT with prior information from X-ray Computed Tomography (XCT) to determine tumor position. The integrated localization data are then transferblack to a robotic arm, which uses this information to accurately guide radiotherapy procedures. The detailed system design of the EIT-guided robotic arm for radiotherapy is presented. Additionally, both simulations and water tank experiments are conducted to validate the feasibility of this approach. The results demonstrate the potential of integrating EIT with XCT and robotic systems for tumor localization and targeted radiotherapy, thereby broadening the clinical applications of EIT in the medical field

Early Detection of Cardiovascular Diseases

Cardiovascular diseases (CVD) represent a significantly perilous category of health conditions that directly affect the heart as well as the blood vessels. Notoriously recognised for their critical impact on global health, these diseases stand at the forefront among the primary reasons for mortality worldwide, notably accounting for the highest number of deaths attributed to non-communicable diseases. Given these alarming statistics, it becomes imperative to develop methods and create models capable of accurately predicting the onset of CVD among individuals who are currently perceived as healthy. This approach is not only essential for the early detection and subsequent management of such conditions but, most crucially, serves as a cornerstone for the prevention strategies aimed at mitigating the risk and potential severity of cardiovascular diseases for those who might fall ill in the future. This study proposes a promising machine learning-based approach for early CVD detection and is comparing various state-of-the-art techniques. The methodology is applied on the Framingham dataset aiming to indicate the possibility of developing a coronary heart disease (CHD) within ten years. With an accuracy of 93%, the stacking classifier model with synthetic data outperformed all existing approaches applied on the same dataset. The obtained results are indicating that approaches like the one we present hold great potential in revolutionising CVD detection

Masculinities and gender dynamics in Scottish aquaculture: the need for transformative and collective action

Aquaculture is a rapidly growing sector and plays a pivotal role in Scotland’s economy, contributing to income generation, employment and economic growth, particularly in remote communities. As with other primary industries, the sector’s growth has not been synonymous with inclusivity. In particular, women have faced significant hurdles despite their vital contributions to the sector’s development. This study investigates the barriers to women’s inclusion and progression within Scottish aquaculture, highlighting the role of entrenched gender norms and dominant masculinities that uphold a patriarchal culture. Our findings are based on qualitative interviews with 28 key industry informants across Scottish aquaculture. The study argues that achieving gender equality in aquaculture requires a collaborative, sector-wide approach that challenges traditional practices and fosters inclusivity. These barriers include entrenched gender norms, toxic masculine behaviours, inadequate safety measures, lack of flexible working arrangements, and youth disengagement. The study calls for a transformation in the aquaculture sector’s approach, advocating for collaborations with gender experts, inclusive business models and value chains that balance economic, environmental and social sustainability. Key recommendations include creating partnerships with gender-focused organisations, ensuring robust grievance mechanisms, collecting gender-disaggregated data, and promoting flexible work arrangements, amongst others. To ensure a more inclusive sector in which women and other marginalised groups can fully participate in and benefit from, Scottish aquaculture leaders must commit to advancing gender equality

A Unifying Bias-aware Multidisciplinary Framework for Investigating Socio-Technical Issues

This paper aims to bring together the disciplines of social science (SS) and computer science (CS) in the design and implementation of a novel multidisciplinary framework for systematic, transparent, ethically-informed, and bias-aware investigation of socio-technical issues. For this, various analysis approaches from social science and machine learning (ML) were applied in a structured sequence to arrive at an original methodology of identifying and quantifying objects of inquiry. A core feature of this framework is that it highlights where bias occurs and suggests possible steps to mitigate it. This is to improve the robustness, reliability, and explainability of the framework and its results. Such an approach also ensures that the investigation of socio-technical issues is transparent about its own limitations and potential sources of bias. To test our framework, we utilised it in the multidisciplinary investigation of the online harms encountered by minoritised ethnic (ME) communities when accessing and using digitalised social housing services in the UK. We draw our findings from 100 interviews with ME individuals in four cities across the UK to understand ME vulnerabilities when accessing and using digitalised social housing services. In our framework, a sub-sample of interviews focusing on ME individuals residing in social housing units were inductively coded. This resulted in the identification of the topics of discrimination, digital poverty, lack of digital literacy, and lack of English proficiency as key vulnerabilities of ME communities. Further ML techniques such as Topic Modelling and Sentiment Analysis were used within our framework where we found that Black African communities are more likely to experience these vulnerabilities in the access, use and outcome of digitalised social housing services

Asymmetry of Cyclonic Sea Surface Wind and Wave Observed by SAR

CyclObs-derived wind and SWH field are extracted from over 600 dual-polarized Sentinel-1 (S-1) images of around 300 tropical cyclones (TCs) over the past eight years to investigate asymmetry of wind and wave fields during TCs. Fetch analysis and machine learning technique, eXtreme Gradient Boosting (XGBoost), is used to establish a relationship between TC wind speed and significant wave height (SWH). It was found that TC wind and SWH radii become asymmetric as sea states intensify. Notably, wind radii correlations (CORs) increase on the left-right and left-back quadrants for wind speeds larger than 20 m/s, while SWH radii exhibit the opposite trend. XGBoost is employed to obtain the improved relationship between wind fetch and SWH (COR &lt; 0.17). Validation against buoys and Haiyang-2 (HY-2) observations of 20 TCs indicates that the root mean squared error in SWH predictions is reduced by up to 1.1 m using XGBoost instead of empirical model. The new TC wave model by XGBoost is particularly robust under high-wind conditions, therefore vital for warning and mitigation of extreme storms and improved parameterizations of air-sea interaction