http://hdl.handle.net/10453/35217 2026-05-01T08:55:10Z http://hdl.handle.net/10453/194820 Title: Cyclist Near-Miss Detection Using Lightweight Deep Temporal Neural Networks Authors: Saleh, K; Grigorev, A; Mihaita, A-S Abstract: Near-miss incidents, where cyclists narrowly avoid collisions, are critical for understanding and improving urban cycling safety but are often underreported in official statistics. This paper introduces XceptionCycle, a lightweight and efficient deep neural network designed to detect cyclist near-miss incidents using time-series data from Inertial Measurement Units (IMUs) and GPS sensors. Building on the XceptionTime architecture, our model incorporates inverted bottlenecks and multi-scale depth-wise separable convolutions to extract rich temporal features while maintaining a low computational footprint. We benchmark XceptionCycle on the large-scale SimRa dataset, demonstrating superior discriminative performance with an 81.99% Area Under the ROC Curve and strong robustness with a 48.33% Matthews correlation coefficient, outperforming state-of-the-art models, while requiring less than half the number of trainable parameters. These results highlight XceptionCycle's potential for real-time near-miss detection and its suitability for resource-constrained environments such as mobile safety applications. 2025-11-21T00:00:00Z http://hdl.handle.net/10453/194819 Title: A Novel Mixed-Integer Linear Programming Model for the Capacitated Arc Routing Problem Adapted to Roadside Management Operations Authors: Castañeda-Rodríguez, I; Mihaita, A-S; Marche, B; Dávila-Gálvez, S; Camargo, M Abstract: Roadside management is an issue in many territories worldwide. Decision-makers face multiple challenges in finding the right combination in scheduling roadside maintenance activities while meeting several objectives, such as minimising travelled distance and time while collecting the mowed biomass for future valorisation. This work addresses the problem of planning optimal configurations for Roadside Management Operations (RMO) under tactical and operational decisions. At the tactical decision level, the allocation of resources must be carried out for each technical centre operating within the territory. At an operational level, the routing of maintenance vehicles must be scheduled. For this purpose, a Mixed-Integer Linear Programming (MILP) model is proposed to formulate a new Capacitated Arc Routing Problem (CARP) adapted to RMO (which we denote CARP-RMO). We further evaluate our proposed model with benchmark instances and well-known literature heuristics and show that our proposed optimisation approach is better performing, especially when scaling up to larger areas and multiple constraints. A case study is also presented for the area of Neufchateau, France, based on real data collected from the local operation technical centres, for which we showcase that our optimisation method can achieve good operational performance for both an optimal travel time and efficient biomass collection. 2025-11-21T00:00:00Z http://hdl.handle.net/10453/194767 Title: Machine learning-based prediction model construction for type 2 diabetes mellitus: A comparison of algorithms and multi-level risk factor analysis Authors: Xu, Q; Ball, J; Sun, J Editors: Ye, W Abstract: BACKGROUND: Against the backdrop of the global high incidence of Type 2 diabetes mellitus (T2DM), existing prediction models are largely confined to single-dimensional risk factors, suffering from a core limitation of lacking multilevel integrated analysis. Given the severe impact of T2DM on individual health and healthcare systems, the construction of a comprehensive and accurate prediction model is of great significance. OBJECTIVE: This study is aimed at constructing a T2DM prediction model, identifying multilevel risk factors, and enabling early screening, so as to help clinicians identify high-risk individuals and provide targets for public health interventions. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) 2021-2023 were used, including 6337 participants aged 18 years and older. Missing values were handled using Monte Carlo multiple imputation, collinearity was reduced via principal component analysis (PCA), and feature selection was performed using random forest (RF) and recursive feature elimination (RFE). The adaptive synthetic sampling (ADASYN) method was applied to address class imbalance. The performance of seven machine learning models, including decision tree, random forest, extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), was compared. RESULTS: The AdaBoost model exhibited the optimal performance, with an area under the curve (AUC) of 0.85 (95% confidence interval: 0.85-0.86), an accuracy of 0.71 (95% confidence interval: 0.70-0.72), and an F1 score of 0.71; its performance was further improved after parameter optimization. A total of 24 key risk factors were identified, including 19 at the individual trait level, 3 at the individual behavior level, and 2 related to working and living conditions. CONCLUSIONS: Machine learning models integrating multidimensional risk factors based on the health ecology framework can more accurately predict T2DM risk, providing a scientific basis for multilevel interventions. The innovation of this study lies in the first integration of the health ecology model with machine learning technology to systematically identify cross-level risk factors. Compared with traditional models, it is more comprehensive, breaks through the limitations of previous studies, and provides a new and effective tool for the precise prevention of T2DM and public health interventions. 2026-01-19T00:00:00Z http://hdl.handle.net/10453/194766 Title: Challenging history taking encounters: a systematic review, meta-analysis and phenomenological framework. Authors: Hossain, S; Murray, K; Zhang, L; Jassem, AM; Clarke, J; Somers, J; Dias, R; Uridge, A; Sharma, S; Teodorczuk, A; Keijzers, G; McConnell, H; Sun, J; Amey, L; Broadley, SA Abstract: OBJECTIVES: All physicians will experience challenging history taking encounters, where communication is impaired and negatively impacts the diagnostic process. The aims of this systematic review were to (1) undertake a meta-analysis of the frequency of challenging encounters; (2) collate adverse outcomes of challenging encounters; (3) identify underlying causes of challenging encounters; (4) identify strategies to deal with different challenges; and (5) align these strategies with our published phenomenological framework of history taking challenges. DESIGN: This was a systematic review and meta-analysis of prevalence data adhering to the Preferred Reporting Items for Systematic reviews and Meta-Analyses and the Meta-analyses of Observational Studies in Epidemiology guidelines. DATA SOURCES: A literature search in MEDLINE, Embase and Cochrane databases was performed on 12 July 2020, and updated on 4 August 2025, focusing on challenging history taking encounters in any clinical setting. ELIGIBILITY CRITERIA: Articles reporting on the frequency, adverse outcomes, causative factors or strategies used to address challenges in the history taking process in any clinical area of medicine. DATA EXTRACTION AND SYNTHESIS: Factors associated with challenging history encounters (causative or consequential) were categorised using inductive coding and referenced to a phenomenological framework. Meta-analysis was used to estimate the prevalence of history taking encounters using a restricted maximum likelihood model with τ 2 and I 2 as tests for heterogeneity and funnel plot with Egger's test for publication bias. RESULTS: 73 articles were included in the analysis. The overall prevalence of challenging history taking encounters was 19.5% (95% CI 14.2% to 24.7%). Adverse outcomes of patient dissatisfaction (level 1 evidence) and diagnostic uncertainty (level 3 evidence) were identified. Factors associated with (n=22) and strategies to mitigate challenging encounters (n=13) were categorised. Correlation of factors and strategies with a phenomenological approach created a framework to assist novice history takers in approaching such circumstances. CONCLUSIONS: Challenging history taking encounters are common. Little is known of the relative importance of factors associated with challenging history taking encounters or the impact of suggested strategies. Many of the suggested strategies to facilitate meaningful communication in these situations involve a departure from standard history taking. More research is required to better define the nature of challenges encountered in history taking with a view to develop better educational models for trainee physicians. 2026-04-13T00:00:00Z