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Post-Doctoral Intensities and Becomings in Writing
Get PDFThis experimental poem emerged from thousands of words of prose written after completing a much-loved PhD. With writing often becoming trapped, insisting on circling the same themes, changing from prose to verse has been freeing: releasing academic writing from its usual constraints, enabling something other to emerge
Numerical modelling of gravel transportation by a tsunami with the extended XBeach-G
Get PDFEstimating coastal erosion by a tsunami is essential for land use planning, assessing hazards for current structures (e.g., coastal nuclear power plants), and for paleotsunami reconstruction. Such estimations are currently available only for sandy beaches, using sand sediment transport models, which are not applicable to gravel beaches, which are the most common beach type in high-latitude settings. This study extended the one-dimensional cross-shore XBeach-G model to account for two-dimensional gravel transport by a tsunami. First, this study confirmed that the extended XBeach-G model can simulate a time series of waveforms of solitary waves during laboratory experiments. The proposed model was then applied to gravel transport by the 2011 Tohoku-oki tsunami at Koyadori, Japan, and found that the simulation results were consistent with observations of gravel deposits in previous studies. It was revealed that infiltration and exfiltration have an impact on morphological change caused by a tsunami on gravel coasts. In the simulation, inundation depth over land by the tsunami increased due to groundwater exfiltration, which increased the onshore deposition volume of gravel tsunami deposits. The groundwater flow calculation has not been incorporated so far for tsunami modelling, but this is important for modelling tsunami inundation at gravel beaches and gravel sediment transport by a tsunami. However, choosing appropriate values for the sediment friction factor and multiplier in the equation for gravel transport is more critical to reproducing the deposition of gravel sediments by a tsunami because these parameters are more sensitive than the parameter of groundwater flow. Although the presented model has been developed for tsunami simulation on any gravel beach, further testing and validation are recommended
Transformer-Based Drug-Resistant TB Diagnosis from Chest X-Rays: Multi-Class Evaluation and Deployment Insights
No full textDrug-resistant tuberculosis (DR-TB) is a major global health problem, especially in areas with inadequate resources and diagnostic facilities. Chest X-ray (CXR) imaging is widely accessible but relies on expert interpretation, a bottleneck in burdened TB settings. Addressing data scarcity and computational constraints dominant in such environments, this study presents a data-efficient deep learning framework that uses a Data-Efficient Image Transformer (DEiT) model to automate DR-TB classification from CXR images. DEiT is rigorously benchmarked against leading neural networks (DenseNet121, InceptionResNetV2, MobileNetNetV2) in a curated data set of 7,961 CXRs from 13 countries with a high prevalence of drug-resistant tuberculosis. The DEiT model demonstrated superior diagnostic performance, attaining an AUC of 0.800 (vs. 0.787–0.792 for CNNs), with balanced precision (0.826) and recall (0.828). Bootstrapped statistical validation confirmed DEiT’s performance advantage. Importantly, DEiT’s efficient fine-tuning strategy, freezing the transformer encoder and updating only 0.24\% of parameters (202K/86M), resulted in reduced trainable parameters and faster convergence. This design improves suitability for deployment in under-resourced healthcare settings where hardware limitations and small data\-sets prevail. The knowledge distillation structure of the model is responsible for its improved generalization since it reliably utilizes pre-trained representations in situations with little data. In an extended multi-class classification task involving DS-TB, MDR-TB, Non-MDR-TB, and XDR-TB, DEiT maintained consistent discriminative performance. Targeted augmentation and class-balanced training further improved the recognition of underrepresented subtypes, reducing diagnostic bias. These findings underscore the potential of data-efficient transformer-based models to overcome the limitations of conventional CNNs. With appropriate integration into clinical workflows and infrastructure, such models could enhance the scalability and precision of drug-resistant tuberculosis diagnosis
A multi-center comparative analysis of the G8-score and Charlson Comorbidity Index as general health assessment tools in older patients with prostate cancer
No full textIntroduction Frail and older patients who develop prostate cancer (PCa), are at risk of overtreatment. Multiple general health assessment (GHA) tools have been validated in oncology. we aim to validate and compare the performance of the G8 screening tool, age and Charlson Comorbidity Index (CCI) as a predictor of overall survival (OS) in men older than 70 years with a newly diagnosed prostate cancer. Is it possible to identify a CCI cut-off indicative for frailty? Patients and methods Between 2009 and 2015, a national multicenter initiative on geriatric screening and GHA’s took place in Belgium. Baseline characteristics were collected on the date of inclusion on which a specialist nurse completed multiple GHA questionnaires for each patient. We performed a sub analysis on the prostate cancer ( n = 182) cohort, ≥ 70 years old. GHA’s were compared through multivariate-, Kaplan-Meier- and sensitivity analysis. If indicated, Case-control matching was applied to reduce variate heterogeneity. Ten-year OS is the primary endpoint. Results Men with a G8-score of ≤14 points had a significantly worse OS both in the unmatched: 86.8 months (95% CI: 70.5-103.1) vs. 137.9 months (95% CI: 129.4-146.4) ( P \u3c .001) and matched population: 99.3 months (95% CI 81.9-116.73) vs. 136.0 months (95% CI: 121.8-150.2) ( P \u3c .05). Defining frailty as the presence of comorbidities (CCI ≥ 1 point) shows inferior (HR:1.3, 95% CI: 0.8-2.1—AUC: 0.583) accuracy compared to the G8-score (HR 2.9, 95% CI 1.8-4.5—AUC: 0.715). If the frailty threshold of the CCI is raised to ≥ 2 points, accuracy is matched (HR 4.9, 95% CI 2.8-8.4—AUC 0.735). Conclusion This multicenter analysis validates the predictive value of the G8-score and CCI on OS in newly diagnosed PCa in the older patient. Both GHA’s appear more accurate than age. A CCI ≥ 2 points approximates the sensitivity of G8 defined frailty. External validation of these findings is needed
An evaluation of pre-registration nursing students’ experiences whilst enrolled onto a four-week art workshop (2024)
Get PDFIntegrating Identity-Based Identification Against Adaptive Adversaries in Federated Learning
Get PDFFederated Learning (FL) has recently emerged as a promising paradigm for privacy-preserving, distributed machine learning. However, FL systems face significant security threats, particularly from adaptive adversaries capable of modifying their attack strategies to evade detection. One such threat is the presence of Reconnecting Malicious Clients (RMCs), which exploit FL’s open connectivity by reconnecting to the system with modified attack strategies. To address this vulnerability, we propose the integration of Identity-Based Identification (IBI) as a security measure within FL environments. By leveraging IBI, we enable FL systems to authenticate clients based on cryptographic identity schemes, effectively preventing previously disconnected malicious clients from re-entering the system. Our approach is implemented using the TNC-IBI (Tan-Ng-Chin) scheme over elliptic curves to ensure computational efficiency, particularly in resource-constrained environments like the Internet of Things (IoT). Experimental results demonstrate that integrating IBI with secure aggregation algorithms, such as Krum and Trimmed Mean, significantly improves FL robustness by mitigating the impact of RMCs. We further discuss the broader implications of IBI in FL security, highlighting research directions for adaptive adversary detection, reputation-based mechanisms, and the applicability of identity-based cryptographic frameworks in decentralised FL architectures. Our findings advocate for a holistic approach to FL security, emphasising the necessity of proactive defence strategies against evolving adaptive adversarial threats
How do People Use Social Media to Maintain Continuing Bonds in Bereavement? A Qualitative Meta-Synthesis
Get PDFStaying connected with a person who has died is a common phenomenon in bereavement, generally referred to as continuing bonds (CB). This can take multitudinous forms, a novelty being the utilisation of social media to strengthen the connection. This qualitative meta-synthesis collated research on digital CB to identify themes to help us develop an overarching understanding of how digital platforms are used to express CB and what impact this may have. A systematic search of four relevant databases (PubMed, PsycInfo, CINAHL and Web of Science) was conducted. Papers were exported to EndNote for screening; ultimately, seven papers were selected for thematic analysis. Five main themes were identified: Broadcast grief, Immortalized in tech, Reachable entity, Collective grief and Revolutionised grief. The included papers clarified reasons for the growing interest in sharing grief digitally, such as affirming relationships and the co-constructed maintenance of identity. These findings can inform modernisation of grief interventions
Thin and ephemeral snow shapes melt and runoff dynamics in the Peruvian Andes
Get PDFThe snow and glaciers of the Peruvian Andes provide vital water supplies in a region facing water scarcity and substantial glacier change. However, there remains a lack of understanding of snow processes and quantification of the contribution of melt to runoff. Here we apply a distributed glacio-hydrological model over the Rio Santa basin to disentangle the role of the cryosphere in the Andean water cycle. Only at the highest elevations (\u3e5000 m a.s.l.) is the snow cover continuous; at lower elevations, the snowpack is thin and ephemeral, with rapid cycles of snowfall and melt. Due to the large catchment area affected by ephemeral snow, its contribution to catchment inputs is substantial (23% and 38% in the wet and dry season, respectively). Ice melt is crucial in the mid-dry season (up to 44% of inputs). Our results improve estimates of water fluxes and call for further process-based modelling across the Andes. (Figure presented.
Mapping the Numbers of Dementia in Brazil: A Delphi Consensus Study
No full textOBJECTIVES: As the global dementia crisis intensifies, especially in low-and middle-income countries (LMICs), there is a pressing need for comprehensive prevalence data across diverse regions, including Brazil, where studies have been predominantly limited to affluent urban centers. This study aimed to conduct an expert consensus to determine the prevalence of all-cause dementia in Brazil, considering various age groups, sexes, and geographical areas.
METHODS: A Delphi consensus process with clinical and academic experts from across Brazil was conducted to provide dementia prevalence estimates in people aged ≥ 60 years living throughout Brazil for 2019. Each round consisted of answering structured questionnaires that incorporated information from the literature. A priori criteria were used to ascertain the point in which consensus was achieved for \u3e 70% of the 15 prevalence estimates-for (1) total, (2) women and men, and (3) the five Brazilian macro-regions. The current and projected dementia cases in Brazil were calculated based on age and sex population distributions.
RESULTS: Fifteen experts, with a mean professional experience of 25 ± 10 years, reached a consensus in the fourth round. Experts agreed with a mean all-cause dementia prevalence of 8.5% among Brazilians aged ≥ 60 years, which comprised 2.46 million people in 2019 in this age. They reported higher dementia rates in women (9.1%) than men (7.7%); the highest total prevalence was in those over 80 where it exceeds 20%. Regional variations were also noted, with lower prevalence in the South (7.3%) and higher in the North (8.9%) and Northeast (10.1%). Projections estimate that considering Brazil\u27s rapidly aging population, dementia cases will rise to 8.89 million by 2060.
CONCLUSIONS: This Delphi study estimated that dementia already affects roughly 1 in 12 older Brazilians aged 60 and above, with slightly higher prevalence in women and significant geographical variations. These results underscore the urgency for targeted public health strategies in Brazil and offer a framework for similar challenges in other LMICs, especially given that dementia cases are projected to increase by approximately 3.6 times in 4 decades
A Multiethnic Approach of Stakeholder Involvement to Set Priorities for Oral Health Research
Get PDFSetting research priorities is a key component of an effective research system. Stakeholder involvement, both formal and informal, is crucial in this process. Only few studies focus on the challenges of engaging diverse populations, particularly those from different ethnic and linguistic backgrounds, or how their values and priorities may align or differ.In Malaysia, a multiethnic and multilingual country, significant oral health disparities have been reported across ethnic groups. For example, the Malaysian National Oral Health Survey (2017) documented a higher prevalence of dental caries among Chinese children (30.7%), the highest oral cancer rates among Indians (46.2%), and the lowest prevalence of oral cancer among Malays (14.7%). These disparities are influenced by various environmental, societal, and lifestyle factors, which differ across communities with distinct priorities, ethnicities, cultural contexts, and socioeconomic conditions. Disparities may stem from differences in access to healthcare, oral health priorities, health behaviours, or beliefs. In my twelve years of dental practice in Malaysia, I have witnessed the substantial impact of local cultural contexts on oral health outcomes.Recognising the importance of inclusive research priority setting (RPS), my thesis explores the role of ethnicity and the involvement of diverse linguistic groups in establishing oral health priorities. Given the diverse underlying lifestyle and contextual factors across communities, it is crucial to implement methods that facilitate their meaningful participation. Furthermore, understanding how these differences shape individuals\u27 priorities and values is vital for equitable research.This PhD research investigates the similarities and differences in oral health research priorities between dentists and adult community members from various ethnic backgrounds in Malaysia. It highlights how the values and beliefs of these groups influence the RPS process, stakeholder engagement, and research topics. Through semi-structured qualitative interviews and focus group discussions conducted in participants\u27 native languages, with the aid of translators, I gained valuable insights.I introduce a novel three-step approach to translate the ideas and insights shared by RPS participants into actionable research questions. To enhance transparency, I propose making the full list of identified research questions available in open-access data repositories.The findings of this research reveal both commonalities and differences in research priorities between dentists and community members, as well as within ethnic groups themselves. Based on these observations, I developed a toolkit to facilitate multiethnic stakeholder engagement in RPS exercises. I recommend modifying the existing \u27equity lens\u27 guidance to incorporate this toolkit. Adopting this approach in future RPS initiatives can reshape research priorities, improve health services, and ultimately reduce health disparities, leading to better oral and general health outcomes across diverse ethnic groups