National University of Ireland, Maynooth

ICD-11 presents updated descriptions for single episode and recurrent depressive disorder. A depressive episode (DE) is integral to these disorders and contrasting descriptions of the structure of DE symptoms are presented in ICD-11 and the Clinical Descriptions and Diagnostic Requirements for ICD-11 (CDDR). The former presents symptoms as unidimensional while the latter suggests symptoms are multidimensional reflecting affective, cognitive-behavioural, and vegetative clusters. This study examined if the latent structure of the DE symptoms is better represented by a one- or three-factor model. Data were collected from general population samples from the United Kingdom (UK: N=975) and Ukraine (N=2050). DE symptoms were measured using the International Depression Questionnaire, and the latent structure of the DE symptoms was assessed using confirmatory factor analysis. The three-factor model of DE symptoms outlined in the CDDR performed substantially better than the one-factor model and was a close fit to the UK and Ukrainian data. This model was invariant for sex in each nation. Prevalence estimates based on a three-factor model were similar to those based on the ICD-11 requirements, and similarly sized sex differences were observed. The multidimensional description of the DE symptoms outlined in the CDDR better represented data from two culturally distinct samples. Discrepancies in how DE symptoms are described in ICD-11 and the CDDR should be resolved to facilitate future research efforts.

Future flood dynamics in a lowland karst catchment draining into Galway Bay, Ireland, have been assessed under climate-change scenarios. Bayesian neural network model (BNN) was calibrated on 1980-2015 observations of rainfall, tides, and turlough flood volumes, yielding correlations of R = 0.95 (training) and R = 0.78 (overall). Projections driven by CORDEX under RCP 4.5 and 8.5 for 2016-2100 reveal ensemble-mean rainfall increases of 1.2mm decade-1 and 2.5mm decade-1, respectively, corresponding to flood-volume growth rates of 5 × 10⁶ m3 decade-1 and 1.1 × 107 m3 decade-1. Wavelet coherence indicated high-frequency coupling (> 0.7) between rainfall and floods under RCP 8.5 versus < 0.5 under RCP 4.5. Extreme-event analysis showed a 40% rise in joint 95th -percentile rainfall and flood-volume events under RCP 8.5 (p < 0.05). Generalized extreme-value fits to annual maxima for 2018-2037 versus 2080-2099 project that a historical 100-year storm becomes 1-in-16-year event under RCP 8.5. 10-year rolling 90th -percentile analysis revealed rapid intensification of upper-tail floods under RCP 8.5 than RCP 4.5. These findings demonstrated that high-emission pathways substantially amplify flood magnitudes and frequencies and underscores the utility of integrated statistical and machine-learning frameworks for robust flood-risk assessment and climate-adaptation planning.

Human activities on earth result in global disturbances of natural systems that manifest as natural resource exploitation, pollution, climate change and biodiversity loss which negatively impact human health in turn. In response, the concept of Planetary Health (PH) has emerged, recognizing the need for a systemic understanding of interconnectedness between human health and that of the natural systems on which it depends. Cities constitute a relevant ground for health interventions as they are currently home to more than half of the world’s population and paradoxically also among the most vulnerable locations for the impact of human-induced PH pressures.Despite its growing scientific prominence, the field of PH lacks in action-based research. Therefore, this review seeks to map the existing evidence of approaches that operationalize the planetary health concept, and its application across urban contexts. It illuminates entry points and provides guidance for PH researchers, educators, local governments and urban planners in the pursuit of operationalizing PH in urban areas.A literature search for peer-reviewed publications was conducted across 7 databases (N=7843), using the keyword “Planetary Health.” Using the PRISMA-ScR extension guidelines a team of 3 researchers identified 35 articles for the final synthesis.The included studies consist of various types of research investigating how the concept of PH can be operationalized in urban areas. Some approaches are associated with PHs conceptual foundations in the form of frameworks, literacy/ education models and practices, as well as the formulation of measurement and evaluation methods. Then there are applied approaches, consisting of interdisciplinary PH research-projects, diverse case studies and papers that examine PH’s application potential within policy.Among the heterogenous application of the concept across a diversity of contexts, the review identified several best practices, draws out present conceptual and research limitations, as well as challenges and opportunities for embedding the concept across diverse disciplines and as part of various urban interventions.

Historical meteorological observations are crucial for better assessing past climate variability and trends in the frequency, intensity, duration, and distribution of extreme weather events, and for placing the current climate change into historical context. Specifically, long-term high-resolution data at daily and hourly scales are essential for a more accurate assessment of past and rare extreme weather events. Historical meteorological observations are crucial for generating climate products, such as reanalysis and gridded datasets.Ireland has a great heritage of historical instrumental meteorological observations (Mateus, 2021). This presentation will primarily focus on four historical meteorological collections from Ireland, which have been rescued from the original paper sources and digitally preserved, including examples of data application for climate research:1) Meteorological observations from over 40 locations in Ireland registered from 1783 to 1854 and preserved in the archives of the Royal Irish Academy. The meteorological records include observations of air temperature, maximum and minimum air temperatures, dry and wet bulb temperatures, sea temperature, rainfall, pressure, wind direction and force, cloud cover, cloud form, tension of vapour and weather remarks.2) Meteorological observations from over 70 locations in Ireland registered from 1808 to 1939, which were published in newspapers. The majority of the original manuscripts are not traceable; hence, the importance of rescuing these meteorological observations from newspapers. Observed climate variables include maximum and minimum air temperatures, dry and wet bulb temperatures, rainfall, pressure, wind direction and speed, maximum air temperature in the sun, humidity, cloud cover, and qualitative remarks on the state of the weather.3) Meteorological observations from Dunsink Observatory from 1818 to 1850.4) A network of meteorological observations from multiple sites in Ulster (1796 – 1919), which includes the long-term series registered at the Linen Hall (1796 – 1895) and Queen’s College Belfast (1850-1919).Many well-known historical extreme weather events in Ireland, such as extreme air temperatures and storms, are documented in historical instrumental and documentary meteorological records.The metadata and data from these meteorological collections have been rescued and will be made available as open access in forthcoming peer-reviewed publications and digital datasets. ReferencesMateus, C., 2021. Searching for historical meteorological observations on the Island of Ireland. Weather, 76(5), pp.160-165.

Ireland’s winters are getting wetter, with more frequent heavy precipitation events increasing flooding risk across the Island. Extreme precipitation is a key driver of flooding in northwestern Europe; however, observational records are relatively short and represent only a single realisation of the climate state. As a result, they are inadequate for sampling low-likelihood, high-impact flood-relevant extreme precipitation events and for quantifying plausible maxima of such extremes. In this study, we quantify plausible maxima for flood-relevant winter precipitation under the current climate. We apply the UNprecedented Simulated Extremes using Ensembles (UNSEEN) approach to the flood-relevant winter precipitation indices (Rx1day, Rx5day, and Rx30days), using daily winter observations, the ECMWF SEAS5 seasonal prediction systems, and the CANARI Single Model Initial-condition Large Ensemble (SMILE) over the Island of Ireland. These indices are consistently derived across observations, pooled SEAS5 winter ensembles (ensemble member x lead times), and the CANARI SMILE. Model fidelity for CANARI and ensemble independence, stability, and fidelity for pooled SEAS5 are assessed to ensure that both models realistically represent extreme precipitation. Preliminary results indicate that both SEAS5 and the CANARI sample the physically plausible Rx1day and Rx5day extremes that exceed the maximum observed in the current climate, while neither system produces UNSEEN values exceeding the observed maximum Rx30day. The CANARI large ensemble passes the fidelity test without bias correction, whereas the SEAS5 passes the fidelity test after applying simple multiplicative mean scaling bias correction. For CANARI, plausible maxima are approximately 18.01% higher for Rx1day and 20.77% higher for Rx5day than observed maxima, while Rx30day plausible maxima are approximately 8.70% lower than the highest observed Rx30day. For SEAS5, plausible maxima exceed observations by approximately 3.05% for Rx1day and 17.68% for Rx5day, while Rx30day plausible maxima are approximately 17.74% lower than the highest observed. These results highlight the limitations of observational records in sampling extreme tails and indicate that CANARI SMILE captures a broader range of internal climate variability than the initialised SEAS5 seasonal prediction system. They also show that UNSEEN ensembles are more effective at sampling short-duration precipitation extremes (Rx1day and Rx5day) than longer-duration accumulation precipitation extremes (Rx30day). Our study highlights the value of combining the UNSEEN approach with both seasonal prediction systems and SMILEs to better understand unprecedented flood-relevant precipitation extremes in the current climate.

Understanding whether and why observed flood hazards are changing remains a central challenge for hydrology. While event-based attribution has advanced rapidly, robust attribution of changes in observed flood series remains difficult due to the integration of exogenous climatic forcing with endogenous catchment change (e.g. urbanisation, landuse change) and data quality challenges (especially for extremes). Here we develop and apply a causal-chain framework to detect, attribute, and scale changes in annual maximum floods using observational data. Taking the Shannon catchment in Ireland as an exemplar, we i) identify causal chains to reconstruct annual maximum instantaneous discharge from flood-relevant precipitation indices, ii) separate climate-driven and residual components of observed change, iii) evaluate the emergence of a climate change signal in causal chains by regressing (multiple linear regression) local precipitation indices onto global mean surface temperature (GMST), and iv) employ these results to scale local changes in flood magnitude to observed and future changes in GMST. Results show that increases in flood magnitude across the catchment are predominantly climate-driven, with multi-day precipitation totals representing antecedent conditions, particularly annual 30-day maxima and the number of very wet days in winter, emerging as the dominant causal pathways. These precipitation indices exhibit detectable warming-related signals that have emerged from variability and explain a substantial proportion of observed increasing flood trends at all sites (ranging between 28 and 93 percent). Residual trends highlight the role of endogenous catchment factors, especially data quality, changing hydrometric conditions and arterial drainage. By linking local flood discharge directly to GMST via causal chains, the framework quantifies catchment-specific flood sensitivity expressed as percentage change per degree of warming, enabling scaling to future warming levels. Results indicate that flood sensitivity per degree increase in GMST varies substantially across catchments, ranging from 8 to 18 percent per degree warming across the catchment sample. The approach provides an observation-based framework for flood attribution, leveraging established methods to bridge trend detection, process understanding, and climate scaling. Moreover, the approach can identify sensitive catchments, sentinel indices for monitoring floods and help better inform adaptation strategies. The approach is readily transferable to other catchments and hydrological extremes.

Negative healthcare professional experiences of work can undermine health system stability by contributing to shortages, burnout and poor retention. Research into doctors’ and nurses’ working conditions indicates the important role of 'solidarity' to their working experience. There is limited understanding of formal (trade unions, industrial action) and informal (peer-support, camaraderie) solidarity in shaping doctors’ and nurses’ working experience. This study aims to conceptualise both forms of solidarity as perceived by doctors and nurses to understand its potential on their working experience. This qualitative evidence synthesis (QES) protocol is reported following PRISMA-P recommendations. A search strategy has been developed using controlled vocabulary and free terms (MEDLINE, CINAHL, Scopus Embase, PubMed databases), following the Peer Review of Electronic Search Strategies (PRESS) process. English language articles will be included if they report primary, conceptually rich and contextually thick qualitative data, exploring the perceptions of informal and formal solidarity as experienced by qualified doctors and/or nurses. Its influence on the working experience, as reported by included studies, will be explored. Initial title and abstract screening, followed by full-text screening of included articles, will be completed independently by two reviewers. A grey literature search will be employed, including a targeted, domain-specific Google search of doctor and nurse national unions within ten countries with highest union density, and websites of intergovernmental organisations/agencies. Piloted data extraction forms will be used to extract study characteristics and qualitative data. The CASP (Critical Appraisals Skills Programme) tool will be used to assess the quality of included studies by two reviewers, independently. Confidence in cumulative findings will be assessed using GRADE-CERQual guidelines. The QES will be reported using eMERGe guidelines and will follow the Noblit and Hare meta-ethnography approach. Registration Number: This protocol has been registered via the PROSPERO database, the International Prospective Register of Systematic Reviews. The protocol can be found on the register under the following number: CRD420251150676. Available from: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251150676. PROSPERO Registration: CRD420251150676

This article presents a detailed description, and step-by-step process, required to build a small-scale wave energy tank demonstrator (Tiny-T). The system consists of an operative wave energy converter (WEC) with active control, representing the first demonstration of electronic WEC control at 1/100 scale. A key feature of Tiny-T is that the full demonstration system costs below €600. This document provides complete details on the materials, construction, and testing of the system, as well as aspects related to the appeal of the demonstration. As an experimental platform, Tiny-T represents a valuable resource for introductory research inquiries, catering to both high school and university-level studies. Overall, Tiny-T accessibility opens doors for a broader audience to engage with the promising potential of wave energy and real-time control technologies.