University of West London

SummaryElectroglottography is a technology developed for measuring the vocal fold contact area during human voice production. Although considered subjective and unreliable as a sole diagnostic method, with the correct application of relevant computational methods, it can constitute a most promising non-invasive voice disorder diagnostic tools in a form of a digital vocal tract pathology classifier.The aim of the following study is to gather and evaluate currently existing digital voice quality assessment systems and vocal tract abnormality classification systems that rely on the use of electroglottographic bio-impedance signals. To fully comprehend the findings of this review, first the subject of electroglottography is introduced. For that, we summarise most relevant existing research on electroglottography with the particular focus on its application in diagnostics. Then, we move on to the focal point of this work, which is describing and comparing the existing EGG-based digital voice pathology classification systems.With the application of PRISMA model, 13 articles were chosen and analysed in detail. Direct comparison between chosen studies brought us to pivotal conclusions, which have been described in section 5 of this report. Meanwhile, certain limitations arising from the literature were identified, such as questionable understanding of the nature of EGG bio-impedance signals. The appropriate recommendations for future work were made, including the application of different methods for EGG feature extraction, as well as the need for continuous EGG datasets development containing signals gathered in various conditions and with different equipment.

People with learning disabilities often require medicines for various comorbidities. However, limited understanding of this population among healthcare professionals can contribute to high rates of overprescription and polypharmacy, which may adversely affect people’s health and quality of life. While polypharmacy may be appropriate and necessary in many cases, effective medicines management is essential. This article explores the challenges in relation to medicines experienced by many people with learning disabilities, family members, carers and healthcare professionals. It also discusses the vital role of the multidisciplinary team in reducing overprescription, optimising medicines use and promoting shared decision-making to enhance health outcomes.

Real-time flood risk assessment requires integrated frameworks that not only forecast flood dynamics accurately [1] but also capture how people respond to rapidly changing hazard conditions [2,3]. This study develops a novel real-time flood impact assessment framework that couples AI-driven flood forecasting (Long Short-Term Memory) with an agent-based model (ABM) to evaluate human mobility disruption and behavioural adaptation during flood events.The outputs of the flood forecasting model are dynamically transferred to an ABM that represents urban populations, daily activity schedules, and transport networks. Agent (i.e., road users with different demographic attributes) decision-making is governed by a novel risk priority index (RPI), which integrates direct flood exposure, official communication, and demographic vulnerability of agents. Watts-Strogatz small-world network was used to consider the interaction of agents and realistically represent information diffusion, allowing the dissemination of risk awareness.Results reveal substantial real-time impacts on urban mobility, with significant increases in travel times, particularly during peak hours. Moreover, incorporating behavioural adaptation through the RPI in agent-based modelling highlights the critical role of flood-risk information sharing among. A balanced combination of personal flood experience (Individual RPI) and socially shared information (average RPI of neighbouring agents) leads to faster and more effective formation of risk awareness across the population. The proposed AI-driven, agent-based framework enables real-time evaluation of flood impacts on population groups and transport systems, offering a powerful tool for emergency response planning and operational flood risk mitigation by local authorities.

The growing reliance on early flood warning systems reflects broader advances in real-time flood forecasting, where increasing data availability, modelling complexity, and the integration of data-driven and AI-based approaches have been identified as critical enablers for effective urban flood risk management [1]. To investigate if flood warning distribution is affected by hydrological variables, socioeconomic vulnerabilities or physical exposure, this study evaluates the spatial and temporal trends of the Environment Agency (EA)’s flood warnings between 2006-2024 within London boroughs.By applying a comprehensive methodology incorporating GIS analysis, socioeconomic correlation analysis and time-series integration, Flood Alert (FA), Flood Warning (FW) and Severe Flood Warning (SFW) data were analysed using EA’s open-access data. Significant spatial variations were identified within the analysis. Riparian boroughs along the River Thames corridor experienced significantly higher FW rates (>293 alerts per borough approximately per year) compared to non-riparian boroughs (

Community engagement is a fundamental pillar of sustainable urban forestry, essential for expanding canopy cover and promoting a societal "stewardship for trees". While traditional outreach aligns urban development with local priorities, it often faces challenges in continuity and representation, frequently resulting in "engagement fatigue" [1]. There is currently a major missed opportunity to use digital tools to connect expert tree-care decisions with public input. Although smart-city technologies offer new ways to communicate, complex tech can accidentally reinforce disparities in egagement if it is not managed carefully [2].To address this, the London Tree Officers Association (LTOA) established a Working Party on “Promoting Community Engagement for Trees Through Technology-Informed Practices.” This initiative aims to develop strategic pathways toward new guidelines that empower local stakeholders in tree management, improving socio-economic resilience and environmental stewardship. Central to this mission is the "Technological Level of Preparedness" (TLOP) model, which categorises community groups by technology familiarity to ensure equitable and accessible engagement.The initiative is driven by ongoing, data-informed collaboration among a diverse range of urban forestry stakeholders. Regular meetings enable the Working Party to integrate expert knowledge with local insights to better understand the environmental and social priorities, such as urban cooling and biodiversity, that guide tree management. Recent deliberations also emphasis that content quality is key to maintaining interest, shifting focus from simple planting to long-term stewardship.To support the formulation of these guidelines, the Working Party is currently shaping potential case studies to test technologies tailored to different identified TLOP levels. Proposed initiatives include the use of gamification via mobile applications to engage younger demographics; for example, trivia games and interactive digital badges could be implemented to incentivise physical interaction with urban nature [4]. Additionally, the application of immersive technologies, such as Augmented Reality (AR) and Virtual Reality (VR), is being explored to help visualise urban regeneration, making abstract environmental data tangible [5]. The initiative also explores enhancing visualisations of canopy health through geospatial and remote sensing technologies.By exploring how to integrate traditional methods, like guided tree walks, with these new digital tools, the initiative seeks to build a strong foundation. This framework aims to provide the groundwork that could eventually lead to formal guidance for practitioners.Keywords: Community Engagement; Urban Forestry; Digital Inclusion; Immersive Technology; Geospatial Data. Acknowledgements: This work is supported by the London Tree Officers Association (LTOA) Working Party on Community Engagement.

Runways and taxiways constitute a vital component of an airport hub. They are engineered to endure for decades; however, there are reports indicating that they may deteriorate prior to the completion of their designated lifespan, as seen in cases where 30-year designs fail within the first decade. Such deterioration includes premature distress indicators such as rutting, mud pumping, concrete slab sinking, and reflective cracking, often linked to specific material factors such as 100% Ordinary Portland Cement (OPC) design mixes. These issues can be influenced by factors such as subgrade condition, drainage quality, seasonal moisture variations, and geotechnical stability. Furthermore, factors such as ambient temperature and construction workmanship can significantly reduce the design life; if the pavement is not cured properly or poured correctly, it becomes highly susceptible to early-stage failure.Present monitoring practices are predominantly reliant on limited in-situ testing and visual surveys, which often fail to capture the rate of deterioration at a network-wide scale or the root cause of early pavement failure. The advancement of remote sensing technologies, including Synthetic Aperture Radar (SAR) time-series, provides a reliable instrument for the precise monitoring of millimetric-scale deformations across extensive airport areas [1]. However, a comprehensive linkage between SAR observations, pavement failure mechanics, surface distress evolution, and long-term asset management decision making is still at an early stage.In this study, we propose a prototype multi-sensor framework designed for the early detection and characterisation of airport pavement failures. This framework integrates satellite InSAR technology for long-term and seasonal deformation time-series analysis, high-resolution optical imagery for surface distress mapping, and incorporates detailed in-situ pavement investigations. The methodology examines the correlation between seasonality of SAR displacement and ambient temperature, weather, or traffic records to separate consolidation, settlements, and thermoelastic responses. The framework evaluates the utility of these data streams to identify trend profiles that may help characterise the speed of deterioration in "difficult" sections, such as those experiencing mud pumping or sinking bays [2]. The distress metrics and their temporal evolution are extracted from co-registered optical products to track the progression of visible surface damage. In-situ observations serve to validate and provide structural and materials ground truth [3]. The combination of multi-source data facilitates deformation features relating to environmental drivers. These data sources are essential to better understand the pavement states and different scenarios of changes over time. This approach supports asset owners in moving toward data-driven pavement management and optimal budget allocation. Keywords: InSAR Time-Series Analysis; Airport Asset Management; Pavement Deterioration Modelling; Multi-modal Remote Sensing; Infrastructure Resilience References[1] Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8–20.[2] Gagliardi, V.; Bianchini Ciampoli, L.; Trevisani, S.; D’Amico, F.; Alani, A.M.; Benedetto, A.; Tosti, F. Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis. Sensors 2021, 21, 5769.[3] Asadollahkhan Vali, A. (2022). Airport Pavement Management System: Assessing current condition and estimating remaining life from aircraft demand. Spectrum Research Repository.

ABSTRACT Objectives This study aimed to estimate the prevalence of geriatric depression and identify its associated socio‐demographic, lifestyle, health, and psychosocial determinants among community‐dwelling older adults in Bangladesh. Methods A community‐based cross‐sectional study was conducted between April and December 2024 among 719 adults aged ≥ 60 years, selected by multistage random sampling across four administrative divisions. Data were collected using interviewer‐administered questionnaires. Depression was assessed using the GDS‐15 (cut‐off ≥ 5). Logistic regression models were applied to identify determinants, and adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. Results The prevalence of geriatric depression was 35.2%. Poor family bonding (AOR = 6.580, 95% CI = 3.540–12.210, p < 0.001), financial instability (AOR = 3.660, 95% CI = 2.550–5.260, p < 0.001), dissatisfaction with lifestyle (AOR = 3.590, 95% CI = 2.240–5.770, p < 0.001), and daily life stress (AOR = 3.640, 95% CI = 2.560–5.190, p < 0.001) were significant predictors. Comorbidities, lack of regular exercise, mobility, and hearing problems also increased depression risk. Conclusion Geriatric depression is common in Bangladesh and influenced by multidimensional determinants. Integrating geriatric mental health services into primary care and strengthening family and social support systems are essential for prevention and early management.