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During the in-service use of porous asphalt pavement, debris can clog internal voids, impacting both its long-term performance and maintenance costs. Thus, understanding the clogging mechanism is crucial. This study introduces a novel approach to simulate clogging behavior by coupling the discrete element method (DEM) and computational fluid dynamics (CFD) to analyze various particulate clogging scenarios. The simulations included the effects of vehicle vibrations, the impact of rainwater seepage and their combination. The simulation results reveal that both vibration and rainwater significantly reduce clogging material mass, improving pavement permeability. Particles sized between 0.15 mm and 1.18 mm were identified as the most likely to cause clogging, with the tendency increasing alongside seepage velocity. This methodology provides a comprehensive framework for analyzing real-world clogging mechanisms and offers valuable insights for designing more durable and sustainable porous asphalt pavements. Highlights: The asphalt pavement was reconstructed based on CT 3D, and the calculation accuracy was improved by CFD-DEM coupling simulation. For the first time, the vibration load and rainfall effect of a porous asphalt pavement during service were numerically simulated. The blockage attenuation rate was proposed and derived to characterize the quality change of clogging. The maximum clogging attenuation rate of vehicle vibration load is 7.8%, and the maximum rainfall effect is 14.6%. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation and flutter (AF) is the most common sustained arrhythmia with an increasing prevalence in Western countries. However, little is known about AF among immigrants compared to non-immigrants. Aim: To examine the incidence of hospital-diagnosed AF according to country of origin. Method: Immigrants were defined as individuals born outside Denmark by parents born outside Denmark. AF was defined as first-time diagnosis of AF. All individuals were followed from the age of 45 years from 1998 to 2017. The analyses were adjusted for sex, age, comorbidity, contact with the general practitioner and socioeconomic variables. Adjustment was conducted using standardised morbidity ratio weights, standardised to the Danish population in a marginal structural model. Results: The study population consisted of 3,489,730 Danish individuals free of AF and 108,914 immigrants free of AF who had emigrated from the 10 most represented countries. A total of 323,005 individuals of Danish origin had an incident hospital diagnosis of AF, among the immigrants 7,300 developed AF. Adjusted hazard rate ratios (HRRs) of AF for immigrants from Iran (0.48 [95%CI:0.35;0.64]), Turkey (0.74 [95%CI:0.67;0.82]) and Bosnia-Herzegovina (0.42 [95%CI:0.22;0.79]) were low compared with Danish individuals. Immigrants from Sweden, Germany and Norway had an adjusted HRR of 1.13 [95%CI:1.03;1.23], 1.12 [95%CI:1.05;1.18] and 1.11 [95%CI:1.03;1.21], respectively (Danish individuals as reference). Conclusions: Substantial variation in the incidence of hospital-diagnosed AF according to country of origin was observed. The results may reflect true biological differences but could also reflect barriers to AF diagnosis for immigrants. Further efforts are warranted to determine the underlying mechanisms. [ABSTRACT FROM AUTHOR]

Objectives. The prevalence of patients with prior stroke is increasing globally. Accordingly, there is a need for up-to-date evidence of patient-related prognostic factors for stroke recurrence, post stroke myocardial infarction (MI) and death based on long-term follow-up of stroke survivors. For this purpose, the RIALTO study was established in 2004. Design. A prospective cohort study in which patients diagnosed with ischemic stroke (IS) or transient ischemic attack (TIA) in three Copenhagen hospitals were included. Data were collected from medical records and by structured interview. Data on first stroke recurrence, first MI and all-cause death were extracted from the Danish National Patient Registry and the Danish Civil Registration System. Results. We included 1215 patients discharged after IS or TIA who were followed up by register data from April 2004 to end of 2018 giving a median follow-up of 3.5–6.9 years depending on the outcome. At the end of follow-up 406 (33%) patients had been admitted with a recurrent stroke, 100 (8%) had a MI and 822 (68%) had died. Long-term prognostic predictors included body mass index, diabetes, antihypertensive and lipid lowering treatment, smoking, a sedentary lifestyle as well as poor self-rated health and psychosocial problems. Conclusions. Long-term risk of recurrent stroke and MI remain high in patients discharged with IS or TIA despite substantial improvements in tertiary preventive care in recent decades. Continued attention to the patient risk profile among patients surviving the early phase of stroke, including comorbidities, lifestyle, and psychosocial challenges, is warranted. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic disrupted global economies, social structures, and public health systems. However, Denmark stood out as an exception, maintaining steady life expectancy during this period. This raises important questions about the factors that strengthened the Danish healthcare system and society against the pandemic's challenges. Methods: The Danish healthcare system serves 5.8 million citizens with free care, advanced digital infrastructure, and comprehensive health registers. Under the auspices of the Danish Society for Patient Safety, insights from Denmark's response to COVID-19 were collected from the onset of the pandemic. This paper builds on these collected experiences, covering crucial areas such as strategies to reduce transmission, digitalization, management of non-COVID diseases, tracking adverse events, workplace well-being, development and use of predictive models, and maintaining public trust. Patient-level data on contacts, contact types, and clinical procedures were obtained from health administrative systems and clinical quality registries. All results were reported as raw counts, with no statistical analyses applied. Results: During COVID-19, Denmark's healthcare system demonstrated resilience by adapting swiftly, achieving a high vaccination rate, shifting to virtual care, enhancing response capacity through real-time adverse event tracking, and supporting healthcare workers through crisis teams minimizing prolonged sick leave. Predictive models accurately forecasted healthcare demands, while public health strategies focused on monitoring public behavior and trust in authorities. Discussion: A key lesson from Denmark's handling of COVID-19 is that much of the observed resilience stemmed from pre-existing structures that could be reused, further developed, and expanded. This resilience was further enhanced by an unprecedented readiness for change, cross-sectoral and interdisciplinary collaboration, and the removal of typical barriers. These experiences aim to further improve the quality and resilience of healthcare in Denmark and inspire other countries' healthcare systems. Moving forward, acknowledging chronic conflicts as the new normal, coupled with the reminder that "hope is not a strategy", could serve as a pivotal approach. [ABSTRACT FROM AUTHOR]

The dynamic modulus (∣ E * ∣) is a fundamental mechanical parameter for studying the performance of hot mix asphalt and simulating its viscoelastic behaviour under different loading and thermal conditions. It is a primary tool to replicate road surface behaviour under vehicle traffic loading and temperature variations. Though, laboratory testing to determine this parameter is time-consuming and costly. Several predictive models have been developed to estimate the dynamic modulus, ranging from rheological to empirical regression models. This research was dedicated to studying two predictive models for determining the master curve of the dynamic modulus of hot mix asphalt used in a regular pavement binder course containing different reclaimed asphalt contents (0%, 30%, 40%, and 50%). Laboratory experiments were conducted to assess their accuracy. The results show that Witczak's sigmoid function provided the best accuracy for the master curves, while the Generalized Huet-Sayegh (2S2P1D) model showed less accurate predictions, particularly at the range of low and high frequencies. [ABSTRACT FROM AUTHOR]

Objective A patient-focused approach is advocated to embody risk of non-adherence to medication and subsequent adverse clinical outcomes following ischaemic heart disease (IHD). This study aimed to explore how patient perceived information on pharmacological prevention was associated with subsequent nonadherence to medication (measured by non-initiation, non-implementation and non-persistence) in patients with incident IHD. Design Cohort study. Setting Denmark. Participants Register-based cohort of 829 patients with incident IHD in 2013. Measures Perception covered whether patients' experienced being adequately informed about their pharmacological prevention. Information on such was obtained from a survey and divided into 'Well informed', 'Moderately informed' and 'Poorly informed'. Information on baseline characteristics, and reimbursed prescriptions of medication (antiplatelets, statins, ACE-inhibitors/ angiotensin receptor blockers and β-blockers) during follow-up were obtained by linkage to nationwide public registers. Non-initiation and non-implementation of medication, measured as proportion of days covered, were analysed by Poisson regression. Non-persistence to medication, measured as risk of discontinuation, was analysed by multivariable Cox proportional hazard regression. Primary and secondary outcome measures Nonimplementation and non-persistence to medication up to 365 days of follow-up were primary outcomes. Secondary outcomes included non-initiation as well as non-implementation and non-persistence to medication at 180 days of follow-up. Results A dose-response association was in general found between perception of pharmacological prevention and risk of non-implementation and non-persistence. For example, the hazard of non-persistence to antiplatelets was 1.18 (95% CI 0.71 to 1.96) times higher for patients reporting 'Moderately informed' and 1.89 (95% CI 1.10 to 3.25) times higher for patients reporting 'Poorly informed', compared with patients reporting 'Well informed of perception of pharmacological prevention' up to 365 days of follow-up. Conclusion Lower levels of perception of pharmacological prevention were associated with subsequent nonimplementation and non-persistence to medication in patients with incident IHD. [ABSTRACT FROM AUTHOR]

To successfully guarantee the proper durability and serviceability of asphalt pavements, it is crucial to investigate asphalt mixtures’ performance and to design an accurate model to predict their mechanical behaviour. In Machine Learning, Artificial Neural Networks (ANNs) consist of a set of layered and interconnected artificial neurons capable of learning a complex function that maps the input to the target output. This study is specifically aimed at their implementation within the field of pavement engineering. The paper thoroughly discusses the development of an ANN-based methodology capable of predicting the stiffness modulus of an asphalt mixture (AM). The AM under investigation was prepared with spilite aggregate, a 50/70 penetration grade bitumen, and limestone filler. The volumetric properties of each specimen were first determined, and then the sequence of a 4-Point Bending Test was carried out under different conditions. Four testing temperatures (0, 10, 20, 30 °C) and eleven loading frequencies (0.1, 1, 2, 3, 5, 10, 15, 20, 30 and 50 Hz) were selected to investigate the asphalt mixture's mechanical behaviour. The resulting stiffness moduli represented the output of the designed neural model. Prediction accuracy was evaluated utilizing several goodness-of-fit metrics, and the results of this feasibility study proved to be very encouraging. They are certainly limited to the asphalt mixture under investigation. However, the high level of accuracy suggests that trained on a larger dataset, the developed methodology could allow the AMs’ mechanical behaviour to be predicted without the need to carry out the conventional tests that are usually expensive and time-consuming.

Recently, environmental concerns have become a primary driving force in most countries and industries dealing with natural resources. As a part of this category, asphalt pavement industry is trying to implement more green and sustainable features in its products, while maintaining the mechanical and performance-based properties of the resulting asphalt mixtures. Among potential recycled materials, vehicle tires and aged asphalt pavement have been demonstrated to show economic, ecological, and behavioral improvements in the mixtures. However, mixtures with a high content of reclaimed asphalt (RA) and crumb rubber present some limitations. Therefore, using another group of additives, i.e., a warm mix asphalt (WMA) additive, has been considered. The presented paper investigates the use of an elevated content of RA with different crumb rubber modified binders and (in some mixtures) a warm mix additive in an asphalt concrete (AC) binder mix. Regular empirical tests have been conducted and more advanced performance or functional characteristics, i.e., stiffness, thermal induced cracking, resistance to permanent deformation, complex modulus have been determined and evaluated. Selected results are presented in the paper.