In music production, spatial audio has seen slow progress, despite the advantages it offers with respect to the reduction of masking and listener envelopment. Producing spatial audio is significantly more difficult than traditional stereo mixing, it requires specifying several parameters for each sounds location and spread in three dimensions. This has opened up the opportunity for intelligent tools to assist users in implementing the technical aspects of a spatial mix while leaving room for artistic expression. The aim of this research is to develop and validate an intelligent machine learning model that is designed to reduce the number of parameters needed for the production of spatial audio. To accomplish this, a machine learning model will be trained on datasets compiled of Room Impulse Responses (RIR), 3D multitrack mixes and spatial panning parameters. Prior to training, we plan to devise and assess different ways to objectively measure the quality of spatial audio. The first approach will be to optimise a reduced parameter set for an individual audio mix with a defined instrument setup. The second approach will be to optimise the model to generalise to unseen audio mixes with unknown sources. The interface itself will be assessed through user studies to measure usability and perceptual quality. A dedicated vocabulary will guide the user reviews, providing a structured framework for evaluating the system’s immersive capabilities. The success of this research will allow the production of spatial audio to become simpler and more accessible to non?specialists, which could pave the way for widespread adoption of spatial audio.
Recent data suggests approximately 10.6 million individuals (ONS, 2023) experience more than one physical or mental health condition. Health anxiety, with population prevalence of approximately 4.5%, has been identified as one factor driving an increase in cyberchondria, whereby individuals excessively search online for medical information. Individuals with higher health anxiety may be driven to seek help for what are considered normal physical sensations but perceived as symptoms, generating worry and concern. This can place additional physical and financial strain on healthcare services. This highlights a need for innovative and cost?effective approaches to help with prevention and management of such conditions. Previous research has suggested that use of humour may play a role in influencing physical and mental health, such as anxiety, with studies finding people who engage with humour report fewer physical symptoms (Richards & Kruger, 2017). However, more research is needed to determine whether these findings are sufficiently robust to integrate humour in interventions for mental and physical health conditions. The current study aims to investigate the extent of which humour affects health anxiety and physical symptoms. This current study is a correlational survey-based design assessing the role of humour style, sense of humour and general affect as predictors of health anxiety, cyberchondria and somatic symptoms. The study used six questionnaires, the Humour Styles Questionnaire (HSQ), Sense of Humour Questionnaire-6 (SHQ-6), General Anxiety Disorder-7 (GAD-7), Cyberchondria Severity Scale-12 (CSS-12), Patient Health Questionnaire (PHQ-15) and the Positive and Negative Affect Schedule (PANAS). Multiple regression analyses will be presented that assess the extent to which humour style, sense of humour and mood predict health anxiety, cyberchondria and physical symptoms. Potential implications from findings may highlight that particular humour styles and degree of sense of humour are factors that could be used in targeting reduction in health anxiety, cyberchondria and physical symptoms.
This study investigates how the Ludic Space of Applied Theatre (AT) influences the possibility of re-experiencing/connecting with childhood and its space in adult participants of activities. Although numerous descriptions of such re-connections exist in the literature, this topic has not been directly studied. Understanding this influence enhances the AT awareness and potential as a practice and research method, supporting positive individual and group transformations. Conducted within an art-based Paradigm, this research utilises Applied Theatre as a Research Method (ATAR) to generate, collect, analyse, and disseminate data. The project integrates artistic and performance techniques with qualitative methodologies, providing participants with a holistic experience deepening their insight through various self-expression forms. The research is structured into three cycles, each involving different groups comprising at least six sessions, including individualand group play, artistic, reflective workshops, and celebratory moments. The researcher’s perspective is enriched by active participation, autoethnography, and recall techniques. After completing the first research cycle, preliminary findings emerged regarding playfulness, its significance for personal and group experiences, the facilitation and inhibition of its occurrence, and positive dynamics. The potential link was revealed between participants’ access to playfulness and their internal factors related to childhood (such as regression, shame, self-criticism, trust, self?confidence). The playfulness concept is described within a network of terms: freedom, authenticity, spontaneity, creativity, curiosity, presence, courage, choice, and pleasure. Understanding playfulness and supporting participants in accessing it can benefit their personal and social growth. In AT, playfulness is a recommended approach for conducting sessions and research. An interdisciplinary literature review separates playfulness (attitude of mind) from play (activity). However, in AT literature, this division is not consistently applied. Playfulness is often used to describe other terms, while its nature seems to be insufficiently present and explored. The study addresses this problem.
In the last decade, the rise of Unmanned Aerial Vehicles (UAVs) has revolutionised geophysics. UAV?based geophysical methods have expanded data collection into inaccessible areas, increasing spatial coverage and reducing operator effort and survey duration. Consequently, several non-destructive geophysical sensors, including magnetometers and ground penetrating radars (GPR), have been optimized to serve as UAV payloads, while still ensuring good-quality data acquisition. Among geophysical methods, GPR and magnetic surveys are the most widely adopted for geo?environmental, archaeological, geotechnical, and engineering purposes. Their success stems from their cost-efficiency, versatility, data collection capabilities, non-invasiveness, and ability to detect buried targets through magnetic and electromagnetic sensing capabilities. This study presents a geophysical survey conducted to test drone-based GPR and magnetometric technologies, namely the Zond Aereo LF and the MagNimbus. The collected data were processed and analysed to show the results with advanced visualisation techniques, enhancing the interpretation of subsurface features and highlighting the potential of the combined use of these technologies to address environmental challenges, particularly in complex natural and urban settings. The multi-geophysical survey was performed by the Institute for the Electromagnetic Sensing of the Environment of the National Research Council of Italy (IREA-CNR) at the Altopiano di Verteglia (Montella, AV – Southern Italy). The site is a small tectonic-karst basin situated in the Northen-Western sector of the Piacentini Mountains group (Southern Italy). The area has previously been partially studied through ground-based geophysical investigations, additionally, magnetic measurements have been performed in both ground- and drone-based configurations, confirming the presence of three steel metallic pipes system at variable depth (approximately from 1 to 2 m). Currently, the pipe system is out of use. The proposed methodology offers a novel approach to visualise the acquired data and contributes significantly to environmental characterization and detection of buried infrastructure, supporting their potential recovery and integration into urban regeneration processes
Tissue regeneration involves replacing, repairing, or regenerating damaged tissues using biological substitutes such as scaffolds or micro-implants that deliver therapeutic drugs or cells directly to affected sites. This innovative approach holds significant promise for addressing diverse medical conditions, from chronic wounds to organ-specific diseases like nerve and eye disorders. Despite notable advancements, current drug delivery methods face critical challenges, particularly in achieving precise control over drug release profiles and accurately targeting specific tissues. 3D bioprinting has emerged as an essential technique in tissue engineering, capable of creating complex, porous scaffolds designed as effective carriers for therapeutic agents. These structures facilitate controlled, localised drug release directly at targeted tissue sites. Extrusion-based 3D bioprinting is widely employed due to its affordability, adaptability, and compatibility with various biomaterials. However, achieving optimal scaffold printability and structural integrity requires careful optimisation of biomaterial properties, including their biological, mechanical, and rheological characteristics. Traditional experimental methods to optimise these parameters are often resource-intensive and time?consuming. Artificial intelligence (AI), particularly machine learning, offers a powerful alternative to streamline this optimisation. By analysing extensive datasets on biomaterial characteristics, printing parameters, and drug release performance, machine learning can efficiently predict ideal printing conditions, significantly reducing experimental demands and enhancing scaffold quality. We aim to leverage extrusion-based 3D bioprinting techniques to fabricate scaffolds with precise shapes and sizes for localised drug delivery applications. To accelerate the process and reduce experimentation costs, machine learning methods will be employed to identify optimal printing configurations, ultimately advancing the effectiveness and efficiency of scaffold-based drug delivery system
This work builds on ultra-realist criminology as a critical approach that seeks to confront the current criminogenic conditions of late-capitalism to offer a more accurate understanding of the underlying, real causes of crime, deviance, and harm. Ultra-realism accounts for both structure and agency but has an incomplete model of subjectivity, and is thereby limited. By incorporating Linda Zagzebski’s concept of epistemic authority, this research intends to enhance ultra-realism’s analytical framework and theory of subjectivity. In doing so, it will create a more fundamental understanding of the underlying, epistemological elements of deviant behaviour broadly, and specifically within my case study of relatively deprived neighbourhoods in East London. Epistemic authority develops from an idea of rationality being compatible with autonomy. In this way, Zagzebski seeks to develop a model of rationality that can account for dissonant beliefs (and subsequent behaviours) that have often been labelled as irrational. This reading of rationality can strengthen ultra-realism’s analytical capability, specifically when considering the formation of subjectivity and attitudes towards crime and deviance. This presentation will offer a theoretical argument and the initial data from the case study using a grounded theory methodology. Through unstructured interviews in an area of relative deprivation, I will provide qualitative data on the location of epistemic authority, sense of subjectivity and community, and attitudes towards crime, deviance, and harm. This will in turn be generated into a substantive theory within the broader ultra-realist cannon.
Conservation of historical heritage sites requires a balance between respecting historical authenticity and addressing environmental risks. This study focuses on Wolvesey Castle in Winchester, managed by English Heritage, using a multi-temporal remote sensing approach to monitor its structural vulnerability and environmental exposure. For this, we utilised a combination of satellite-derived indices, including Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Normalised Difference Moisture Index (NDMI), and Normalised Difference Water Index (NDWI). These indices were employed to assess thermal stress, vegetation dynamics, moisture retention, and water presence across the site. Average annual values were analysed alongside time series covering several years of observations, in order to capture environmental trends and anomalies. Data from Landsat-8 and Sentinel-2 satellites were processed and integrated with preliminary surface deformation analysis from PS-InSAR products. Results highlighted major seasonal fluctuations in LST, moderate human intervention to vegetation, variable moisture availability, and localised water retention patterns within the castle walls. Surface deformation analysis also indicates signs of subsidence trend, although further high-resolution radar observations are recommended for detailed structural monitoring. This study shows that satellite remote sensing offers a non-invasive, cost-effective tool that can complement the traditional methods in historical site conservation. By detecting early signs of environmental stress and structural change, this approach supports policy makers and gives them a better view for implementing the best strategies necessary. The findings at Wolvesey Castle underscore the importance of integrating spectral indices, deformation monitoring, and conventional site inspections to protect historic structures under dynamic environmental pressures
Since his harsh captivity and execution in 1738, the life of the court Jew Joseph Süss Oppenheimer has been (mis)represented numerous times. In this presentation, I will share insights into my PhD research, which aims to reframe both our understanding of this important historical figure and the nature of adaptation. My PhD encompasses a thesis and a feature film script that break away from a centuries-long tradition of distortion. The screenplay portrays Süss as a visionary entrepreneur driven by his wish for personal acceptance who is scapegoated for the power claims of his patron, Carl Alexander, the Duke of Württemberg. My narrative, grounded in historical research, allows the thesis to explore how storytelling for screen with historical accuracy can provide a creative method for challenging antisemitic stereotypes. To this end, my thesis first traces the similarities between previous Süss misrepresentations. Focusing on how the concept of vampires, and their deployment in a specifically antisemitic mode, has shaped the evolution of Süss portrayals, my research highlights the adaptive nature of stereotypes. Research Questions • How do previous Süss misrepresentations adapt the Jewish vampire stereotype, to an extent that they can be considered as adaptations of each other? How do they relate to the portrayal of vampires across various media? • How can Creative Practice in the form of a more accurate screenplay counter the Jewish vampire stereotype? • How can the script for a new screen portrayal of Süss draw on historical source material while telling the story for a contemporary audience? To what extent can such an approach be considered historically authentic?
Introduction Hypertension is a major global health issue, especially in developing countries, where its prevalence is increasing and contributing significantly to cardiovascular diseases. Despite the growing urgency to control hypertension, there is limited understanding of healthcare-seeking behaviour among adults with the condition. This study explores how hypertensive adults seek healthcare in Africa and identifies factors influencing these practices across the continent. Methods This scoping review used the Arksey and O’Malley framework, enhanced by the Joanna Briggs Institute (JBI) approach, and followed the PRISMA-ScR checklist. Five stages guided the review: identifying the research question, identifying relevant studies, selecting studies, charting data, and summarizing results. A comprehensive search was conducted across six databases: CINAHL, PubMed, Scopus, Academic Search Elite, Embase, and PsycINFO. Search terms focused on “healthcare-seeking behavior,” “hypertension,” “adults,” and “Africa.” Using the PCC framework, the population included hypertensive adults aged 18 and above; the concept was healthcare-seeking behaviour; and the context included both urban and rural African settings. Results Most included studies were quantitative and community-based. Findings show that adults with both hypertension and diabetes are more likely to use formal health services. Public hospitals and health centres are preferred for quality and accessibility, while informal providers and over-the-counter medicine shops are more common in rural areas. Traditional remedies are frequently used alongside biomedical treatments, particularly among rural and low-income populations. Despite interactions with the health system, blood pressure control remains low, especially in underserved areas. Key research gaps include integrating NCD services, the role of health insurance, urban-rural disparities, and traditional healing practices. Conclusion Understanding healthcare-seeking behaviour for hypertension is crucial for improving care in Africa. Policies must consider socio-economic and cultural factors. Further research is vital to strengthen healthcare use and reduce hypertension-related complications.
In vehicular ad-hoc networks (VANETs), ensuring the reliability of vehicle-to-vehicle communications, requires robust intrusion detection systems (IDS) capable of identifying malicious behaviours. One of the main challenges in developing reliable solutions for vehicular networks is identifying a representative dataset that comprehensively captures all types of misbehaviours. Many existing datasets either lack diversity, are outdated, or do not accurately reflect real-world conditions. This makes it difficult to ensure that all potential attack scenarios are properly represented and evaluated, ultimately impacting the reliability and generalisation of the developed approaches. The VeReMi dataset is a widely recognised benchmark that attempts to address these issues, featuring various simulated misbehaviours such as Constant Position, Constant Offset, Random Position, Random Offset, and Eventual Stop attacks. While attacks like Constant and Random Positions are relatively easy to detect due to their erratic nature, more subtle attacks, including Constant Offset and Eventual Stop, closely mimic legitimate behaviours, making detection significantly more challenging. Additionally, the dataset’s naturally imbalanced distribution, where attack instances are vastly outnumbered by benign traffic, further complicates detection. To address this imbalance, the project focuses exclusively on oversampling techniques to enhance minority class representation, deliberately avoiding undersampling to preserve valuable benign data. Conventional methods, such as Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), improve class balance but often fail to capture the underlying complexity of subtle attacks. In contrast, more recent approaches based on Generative Adversarial Networks (GANs), including Conditional GANs (CGANs) and Wasserstein GANs (WGANs), demonstrate superior capabilities in synthesising realistic and diverse minority instances. Leveraging these advances, the project adopts CGAN-based oversampling to better model the nuanced behaviours present in vehicular networks, thereby enhancing the IDS’s ability to detect both obvious and subtle misbehaviours with greater generalisation and robustness. In doing so, it aims to enhance intrusion detection systems with improved generalisation and robustness, ultimately supporting researchers in developing more scalable and effective solutions for real-world VANET environments.
The emotional impact on families of missing people is profound yet it remains poorly understood and often overlooked. Every year, an estimated 170,000 persons are reported missing in the United Kingdom, with long-term psychological and emotional consequences for their loved ones. Despite the high prevalence of grief and trauma, many affected families do not seek help.Yet effective support services are crucial for building resilience and restoring trust in institutions, improving psychological well-being. However, many doctors, therapists, and the wider public are unaware of these difficulties. Art-based methodologies can provide a way out of this impasse by helping individuals articulate their experiences and reconstructing personal meaning through narrative and creative expression. This art-based research explores the impact of moral injury and hermeneutical injustice, when people are denied the resources to interpret their experiences, within the context of ambiguous loss. It includes interviews, creative workshops, and auto-ethnographic storytelling to understand how relatives of missing people make sense of their trauma. The aim is threefold: to amplify the voices of those affected, offer creative community-building activities for those affected and to integrate their lived experience into medical, nursing, and psychotherapy education. The objective is to use art to increase empathy and understanding. By embedding visual and narrative materials into training programmes, the study hopes to encourage more empathy, recognition, and compassion in care, emphasising the need of therapeutic frameworks that address the unique issues of unresolved loss. This research contributes novel insights to the body of knowledge on relatives of missing people by offering a diverse range of narrative formats from stories, films, animation to augmented reality paintings, collages, and drawings. It seeks to inform transform and promote person-centred support for families of the missing.
Cross-cultural collaboration plays a vital role in the success of the digital economy specifically in the era of accelerated globalisation. On the other hand, the role of soft skills in improving team dynamics and project success in the ICT sector has received growing attention. However, as the “skills” are often socially and culturally formed and developed, individuals from diverse backgrounds may not be at the same level of proficiency or share the same understanding of what constitutes a soft skill, which ultimately causes challenges in culturally diverse project teams. This study employs a mixed methodological approach to provide a comprehensive insight into the topic. Quantitative data are collected through a validated questionnaire assessing the importance of soft skills, and participants’ skill level. Qualitative insights are gathered through semi-structured interviews with ICT professionals to examine how these skills manifest in real-world multicultural team environments. A pre-pilot test was conducted with 15 experts from the School of Computing and Engineering (SCE) to validate the research instrument. Results confirmed acceptable internal consistency (Cronbach’s Alpha = 0.77) and content validity, with experts rating the questionnaire as “Effective to Very Effective” (average score: 3.8/5). Notably, preliminary findings indicate a trend of overestimation among the participants about their soft skill level. In approximately 70% of listed soft skills, participants rated themselves higher than demonstrated in scenario-based responses. Besides, communication, teamwork, and time management were selected as the most consistently recognised core skills. However, the results also highlighted notable challenges in time management and emotional intelligence, meaning that participants often struggled in managing multiple tasks in tense projects, showing empathy while managing their own emotions.
The Brazilian Federal Constitution, in its articles 176 and 231, addresses mining in indigenous lands by calling for specific supplementary legislation to regulate this activity. Since the Constitution was promulgated in 1988 there have been unsuccessful attempts at passing such legislation, however, for the past 37 years this persistent legislative gap remains. The aim of the research is to identify and address the challenges vis-à-vis the constitutional requirements to the approval of legislation to regulate mining in indigenous lands in Brazil. In this sense, the objectives of the research will be to 1) identify and explain the legislative gap created by the aforementioned constitutional articles and to critically analyse, by means of comparative research, past legislative attempts to fulfil it, in order to understand why they were unsuccessful and how to avoid the same mistakes in the future; 2) explore the concept of ‘national interest’ in Brazilian law, as it is a pivotal constitutional requirement for the creation of the supplementary legislation; 3) critically analyse impact of the current legislative gap over the rights of indigenous peoples 4) analyse Brazil’s international commitments on environmental and indigenous protection to assess their effectiveness and how they affect the creation of the relevant supplementary legislation. The research methodology combines both quantitative and qualitative data in Portuguese, English and Spanish which will be duly translated by the author. Through a critical evaluation of the systemic legal?cultural reasons why previous attempts at creating a legislation have been unsuccessful, the research outlined will make a significant impact in aiding in the elaboration of the requirements to establish a successful legislative framework to bridge the existing legislative gap concerning mining in indigenous lands in Brazil
Background Smoking remains a leading global public health issue, with young adults being particularly at risk. Online social networks and chat groups present scalable, cost-effective opportunities for delivering behavioural smoking cessation interventions with peer support and real-time engagement. Aim This systematic review evaluates the effectiveness of online social networks and chat groups for smoking cessation among young adult smokers aged 18–29 years. Methods Using PRISMA guidelines, a comprehensive search was conducted across MEDLINE, CINAHL, PsycINFO, and other databases. Eligible studies met PICO criteria and included interventions delivered via online social networks or chat groups. Primary outcomes were 7-day and 30-day abstinence and quit attempts. Only studies rated as medium or high quality using the JBI tool were included. Findings Search results yielded 10,852 records, of which thirteen papers (seven original studies) met inclusion criteria - two RCTs, two quasi-experimental studies and three pilot/feasibility studies. Six papers were secondary analyses of these studies. All the studies were conducted in the United States and Canada. Four studies used Facebook in closed, stage-matched groups moderated by trained facilitators while three used mobile apps with chat features. Participants were mainly non-Hispanic white, heterosexual young adults. Key features across the studies included daily prompts, text/multimedia content, and peer interactions. All studies reported improved cessation outcomes at 3 months. However, two studies reported no differences at 6 and12 months respectively. Engagement varied but was mostly positive across studies. Higher engagement was associated with more quit attempts. Significance for Research and Practice This review highlights the potential of online social networks and chat-based interventions as effective, scalable tools for youth smoking cessation. However, long-term effectiveness remains uncertain. All studies were from North America, limiting global relevance. Future research should focus on generating evidence outside of North America and explore newer platforms such as WhatsApp, TikTok or AI-enabled platforms
This research aims to develop a non-invasive and accurate system for detecting and classifying laryngeal pathologies – especially cancerous and precancerous lesions – by combining audio and laryngeal bioimpedance signals in a multimodal deep learning classification architecture. It addresses the central research question: Can a multimodal deep learning framework using audio and electroglottographic (EGG) signals outperform traditional and unimodal approaches in classifying laryngeal conditions? To answer this, a novel dataset was collected, featuring simultaneous recordings of audio and laryngeal bioimpedance from healthy individuals and patients with a variety of laryngeal disorders. The methodology includes feature extraction using Equivalent Rectangular Bandwidth-based methods, and the design of both unimodal and multimodal deep learning models based on Convolutional Neural Networks (CNNs). The performance of Recurrent Neural Networks (RNNs) for laryngeal pathology classification is also investigated. The final system adopts a late fusion architecture: two one-dimensional CNNs independently process each modality, and their outputs are combined using a stacked generalisation approach with an ECOC-based meta-classifier. Models were trained and evaluated across two datasets – a custom database and the Saarbruecken Voice Database (SVD) – to ensure generalisability of the developed system. The results show that the multimodal classification approach significantly outperforms unimodal baselines, particularly in malignant case detection, achieving 89.23% ± 1.95 accuracy, 82.32% ± 2.78 precision, and 83.27% ± 3.43 sensitivity. For general pathology detection, the system achieved even higher classification metrics (94.92% ± 2.82 accuracy and 96.67% ± 2.90 precision). Continuous speech outperformed the sustained phonation, and ERB-based feature provided richer pathological discrimination than other feature representations. This study contributes a robust, accurate, and clinically relevant framework for automated laryngeal pathology screening. Its findings support the integration of multimodal voice analysis into future diagnostic tools and lay the groundwork for real-time, non-invasive applications in clinical settings.
The development of mispronunciation detection and diagnosis systems is crucial for enhancing effective communication and pronunciation learning for non-native speakers. While existing Mispronunciation Detection and Diagnosis (MDD) studies predominantly focus on American English, the need for dialect-specific systems becomes evident with the growing demand for accurate pronunciation tools across diverse linguistic contexts. This study addresses a significant gap in British English MDD research by introducing a novel, dialect specific dataset. The creation of the Modern Received Pronunciation (MRP) dataset involved compiling phonetically rich sentences and passages from public domain texts, recruiting and recording the speech of 29 participants. These recordings were processed, segmented and transcribed in preparation for their use in a deep learning system. A baseline model was established using Convolution Neural Networks, Long Short-Term Memory Networks, and Connectionist Temporal Classification. Experiments were conducted to compare baseline model performance when trained on MRP data against an industry standard corpus, TIMIT. Results show that MRP outperformed TIMIT in phoneme error rate, correct diagnosis rate and F1 score, when tested against utterances from L2 Arctic. TIMIT achieved a lower false rejection rate, demonstrating the need for accessible L2 corpora containing British English mispronunciation annotations. This research lays the foundation for further improving dialect specific MDD systems through techniques like language models and attention mechanisms. The novel dataset not only facilitates testing of CTC models on British English but also contributes to the development of digital tools for pronunciation error detection, aiding language learners in producing consistent speech. Furthermore, it bridges a critical gap in UK-based MDD research, offering potential for broader applications in dialect learning and therapeutic contexts
For non-destructive subsurface imaging in a variety of geophysical and civil engineering applications, ground penetrating radar (GPR) has proven to be an effective technique. The use of sophisticated GPR attribute analysis for tree trunk structural health monitoring is investigated in this work. We evaluate internal anomalies, decay, and structural integrity in tree trunks using attribute-based imaging approaches. To improve the visualisation and interpretation of subsurface structures within tree trunks, key GPR textural attributes are analysed, such as Contrast, Energy, Homogeneity, Dissimilarity, and Entropy. Numerical simulations and experimental GPR scans are used to validate the concept, showing how well attribute analysis works to detect internal defects. Overlays and RGB mixed models are utilised to display composite displays of textural features and multiple properties. To better visualise internal reflection patterns, several GPR attributes are characterised using a multi-attribute comparative analysis. The results demonstrate GPR’s potential as a non-invasive diagnostic method for tree trunk health monitoring research, which could enhance tree health evaluation and urban forestry management
Built infrastructures including road, buildings and airport terrains are critical to sustaining societal functionality and safety. Despite their importance, these structures inevitably degrade over time. Remote non-destructive testing (NDT) technologies, such as Ground Penetrating Radar (GPR) and Light Detection and Ranging (LiDAR), employ sensors to inspect the condition of these assets, generating large volumes of data (e.g., position, deformation, velocity, displacement). These datasets are essential for identifying cracks, structural weaknesses, and variations in electromagnetic or thermal properties. Traditionally, data visualisation has been limited to two-dimensions (2D) screens, which inherently lack true depth perception, restrict viewing angles, and reduce interactivity challenges that affect the accurate interpretation of subtle defects or complex information. To address these limitations, this project explores the design of effective three-dimensional (3D) Extended Reality (XR) environments for visualising and communicating NDT sensor data. A systematic review of existing approaches to XR-based visualisation is being conducted, with a focus on three key domains: civil infrastructure, green infrastructure and heritage assets. Relevant stakeholder groups are being identified and engaged throughout the process. The project follows an agile design science research methodology, enabling an iterative development of XR artefacts based on continuous stakeholder interaction and feedback. Early prototypes, including a visualisation of runway deformation data through a 3D runway model overlaid with sensor outputs, are informing the evolving design. Artefacts are progressively evaluated to ensure their effectiveness and usability, with refinements being incorporated as the research advances. The findings of this study are expected to contribute to the maintenance of the civil, green and heritage infrastructures. Furthermore, the project aims to deliver practical and theoretical guidelines on how to integrate sensor data into XR environments and to demonstrate how 3D data visualisation can enhance the interpretation and communication of detected degradation and displacement in infrastructures.
The objective of this paper is to explore the methods through which I musicalise the dance of contraction and release, with particular focus on the exercises of spirals and pleadings. These techniques are embodied in a new composition for harp and piano, titled Descent into a Spiral Dance, which highlights key elements of the dance: the shape of the dancer’s body and the relationship between its parts, especially the opposition between the spine and the core. The main research questions that this study is concerned with are In what ways can contraction and release be translated into music? What are the musical devices that correlate to the core elements of spirals and pleadings? In what ways can music represent the shapes of spirals and pleadings? In what ways can music imprint the opposition between spine and core? The method used to investigate this topic is practice as research. The practice informed the process and decisions, through collaboration and devising with the dancers and musicians. The reflection upon this process led to new findings that moved the research forward. The main findings of this composition are the musical devices that can effectively represent the dance elements that interest this study and performance ways of enhancing the compositional ideas. These findings are of significant contribution to my research but also to contemporary compositional methods. This study also contributes to the theoretical and practical promotion of choreomusicological studies, which are concerned with the relationship between music and dance, which have been neglected especially when it comes to practice.
