Fracture behaviour assessment of high-performance fibre-reinforced concrete at high strain rates using interpretable modelling approaches.

High-performance fibre-reinforced concrete (HPFRC), a type of cementitious composite material known for its exceptional mechanical performance, has widespread applications in structures exposed to severe dynamic loading conditions. However, understanding nonlinear HPFRC fracture behaviour, particularly under high strain rates, remains challenging given the complexities of assessment procedures and cost-intensive nature of experiments. This study presents an interpretable framework for modelling and analysing HPFRC fracture strength at high strain rates. A wide range of machine learning methods, including ensemble techniques, were employed to capture multivariate effects of eight essential input features (e.g., mortar compressive strength, fibre physical and mechanical properties, cross-sectional area, and strain rate) on fracture strength response. To assess the derived models, a novel evaluation procedure was proposed involving a data-based analysis, employing established metrics (i.e., coefficient of determination, root mean squared error, and mean absolute error via K-fold cross-validation) and a domain experts-involved evaluation utilising global sensitivity analysis to discern first-order and higher-order interactions among input factors. The proposed approach efficiently yielded both quantitative and qualitative insights into crucial input factors governing HPFRC fracture strength with limited experimental data. The obtained findings highlight the significance of multivariate effects, such as the interaction between strain rate and fibre tensile strength, and between fibre volume and fibre diameter, on fracture behaviour. The proposed interpretable framework aims to provide a powerful tool for proactive material failure analysis by understanding fracture behaviour and identifying potential weak and strong interactions among input factors of HPFRC-based samples. Moreover, the utilisation of the proposed approach enables researchers and civil engineers to efficiently focus on the most critical input parameters during the early design stage and ensuring the structural integrity and safety of HPFRC-based constructions.

Measuring unequal distribution of pandemic severity across census years, variants of concern and interventions.

BACKGROUND: The COVID-19 pandemic stressed public health systems worldwide due to emergence of several highly transmissible variants of concern. Diverse and complex intervention policies deployed over the last years have shown varied effectiveness in controlling the pandemic. However, a systematic analysis and modelling of the combined effects of different viral lineages and complex intervention policies remains a challenge due to the lack of suitable measures of pandemic inequality and nonlinear effects. METHODS: Using large-scale agent-based modelling and a high-resolution computational simulation matching census-based demographics of Australia, we carried out a systematic comparative analysis of several COVID-19 pandemic scenarios. The scenarios covered two most recent Australian census years (2016 and 2021), three variants of concern (ancestral, Delta and Omicron), and five representative intervention policies. We introduced pandemic Lorenz curves measuring an unequal distribution of the pandemic severity across local areas. We also quantified pandemic biomodality, distinguishing between urban and regional waves, and measured bifurcations in the effectiveness of interventions. RESULTS: We quantified nonlinear effects of population heterogeneity on the pandemic severity, highlighting that (i) the population growth amplifies pandemic peaks, (ii) the changes in population size amplify the peak incidence more than the changes in density, and (iii) the pandemic severity is distributed unequally across local areas. We also examined and delineated the effects of urbanisation on the incidence bimodality, distinguishing between urban and regional pandemic waves. Finally, we quantified and examined the impact of school closures, complemented by partial interventions, and identified the conditions when inclusion of school closures may decisively control the transmission. CONCLUSIONS: Public health response to long-lasting pandemics must be frequently reviewed and adapted to demographic changes. To control recurrent waves, mass-vaccination rollouts need to be complemented by partial NPIs. Healthcare and vaccination resources need to be prioritised towards the localities and regions with high population growth and/or high density.

Persistence of the Omicron variant of SARS-CoV-2 in Australia: The impact of fluctuating social distancing.

We modelled emergence and spread of the Omicron variant of SARS-CoV-2 in Australia between December 2021 and June 2022. This pandemic stage exhibited a diverse epidemiological profile with emergence of co-circulating sub-lineages of Omicron, further complicated by differences in social distancing behaviour which varied over time. Our study delineated distinct phases of the Omicron-associated pandemic stage, and retrospectively quantified the adoption of social distancing measures, fluctuating over different time periods in response to the observable incidence dynamics. We also modelled the corresponding disease burden, in terms of hospitalisations, intensive care unit occupancy, and mortality. Supported by good agreement between simulated and actual health data, our study revealed that the nonlinear dynamics observed in the daily incidence and disease burden were determined not only by introduction of sub-lineages of Omicron, but also by the fluctuating adoption of social distancing measures. Our high-resolution model can be used in design and evaluation of public health interventions during future crises.

A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures.

The COVID-19 pandemic created enormous public health and socioeconomic challenges. The health effects of vaccination and non-pharmaceutical interventions (NPIs) were often contrasted with significant social and economic costs. We describe a general framework aimed to derive adaptive cost-effective interventions, adequate for both recent and emerging pandemic threats. We also quantify the net health benefits and propose a reinforcement learning approach to optimise adaptive NPIs. The approach utilises an agent-based model simulating pandemic responses in Australia, and accounts for a heterogeneous population with variable levels of compliance fluctuating over time and across individuals. Our analysis shows that a significant net health benefit may be attained by adaptive NPIs formed by partial social distancing measures, coupled with moderate levels of the society's willingness to pay for health gains (health losses averted). We demonstrate that a socially acceptable balance between health effects and incurred economic costs is achievable over a long term, despite possible early setbacks.

Bundling of Collagen Fibrils Using Sodium Sulfate for Biomimetic Cell Culturing.

Collagen is the most abundant extracellular matrix protein. The concentrations, structural arrangement, and directionality of collagen depend on the type of tissue. Thick fibril bundles of collagen are observed in most collagenous tissues, including connective tissues, bones, and tendons, indicating that they play a critical role in many cell functions. In this study, we developed a new method to regulate collagen bundling without altering the protein concentration, temperature, or pH by using sodium sulfate to replicate bundled collagen fibrils found in vivo. Microstructure analysis revealed that both the thickness of the fibril bundles and the pore size of the matrix increased with the amount of sodium sulfate. In contrast, there was no significant change in the bulk mechanical stiffness of the collagen matrix. The modified collagen bundle matrix was used to investigate the responses of human cervical cancer cells by mimicking the extracellular environments of a tumor. Compared to the normal collagen matrix, cells on the collagen bundle matrix exhibited significant changes in morphology, with a reduced cell perimeter and aspect ratio. The cell motility, which was analyzed in terms of the speed of migration and mean squared displacement, decreased for the collagen bundle matrix. Additionally, the critical time taken for the peak turning angle to converge to 90° decreased, indicating that the migration direction was regulated by geometric cues provided by collagen bundles rather than by the intrinsic cell persistence. The experimental results imply that collagen bundles play an important role in determining the magnitude and direction in cancer cell migration. The proposed method of extracellular matrix modification can be applied to investigate various cellular behaviors in both physiological and pathological environments.

Structure-Preserving Imitation Learning With Delayed Reward: An Evaluation Within the RoboCup Soccer 2D Simulation Environment.

We describe and evaluate a neural network-based architecture aimed to imitate and improve the performance of a fully autonomous soccer team in RoboCup Soccer 2D Simulation environment. The approach utilizes deep Q-network architecture for action determination and a deep neural network for parameter learning. The proposed solution is shown to be feasible for replacing a selected behavioral module in a well-established RoboCup base team, Gliders2d, in which behavioral modules have been evolved with human experts in the loop. Furthermore, we introduce an additional performance-correlated signal (a delayed reward signal), enabling a search for local maxima during a training phase. The extension is compared against a known benchmark. Finally, we investigate the extent to which preserving the structure of expert-designed behaviors affects the performance of a neural network-based solution.

Effect of tip shape on nanomechanical properties measurements using AFM.

Atomic force microscopy (AFM)-based indentation has been widely used to understand mechanical properties in conjunction with surface topography and structure at the nano-scale. In this work, nanomechanical properties of three different specimens were determined using four different AFM probes with spherical and flat-ended tips and conical tips with rounded apexes, to provide useful information for probe selection and for better interpretation of force-indentation data. These probes were modeled as a sphere, flat punch, and hyperboloid, respectively, after careful characterization to determine the elastic modulus based on contact models. Polyacrylic acid, polyvinylidene fluoride, and styrene-butadiene rubber were used as specimens. The results showed that the probe with a flat-ended tip was prone to misalignment between the flat end of the tip and specimen surface, which caused that the force-indentation data could not be interpreted using the contact model due to imperfect contact. Also, hysteresis was consistently observed in force-indentation data for the probes with a spherical tip and conical tips with rounded apexes, likely due to friction. This further resulted in significant differences in the elastic moduli of the specimens as large as 22-100% as determined from extension and retraction curves. The elastic moduli approximated by the mean of those from the extension and retraction curves generally agree with those from the instrumented indentation. Considering the uncertainties associated with the modeling of tip shape and force and indentation measurements, the probe with relatively large tip radius (e.g., ∼30 nm) was recommended for more accurate measurement for the specimen with a few GPa elastic modulus. Furthermore, the difference between elastic moduli determined from extension and retraction curves was found to increase as the ratio of contact stiffness to flexural stiffness of the AFM probe decreased. The outcome of this work is expected to provide useful information for obtaining accurate mechanical property measurements using an AFM based on a better understanding of the interaction between AFM probe and specimen.

Immunogenicity and safety of a single dose of a live attenuated Japanese encephalitis chimeric virus vaccine in Vietnam: A single-arm, single-center study.

OBJECTIVE: To describe the immunogenicity and safety of the Japanese encephalitis chimeric virus vaccine (JE-CV) in children and adults in Vietnam. METHODS: In this prospective, open-label, single-center, single-arm study, 250 healthy participants aged 9 months to 60 years received a single dose of JE-CV (IMOJEV®). JE neutralizing antibody titers were assessed at baseline and 28days after vaccination using the 50% plaque reduction neutralization test (PRNT50). Safety and reactogenicity were assessed through solicited and unsolicited adverse events. FINDINGS: Seroconversion (titer ≥10 [1/dil] in participants JE seronegative [titer <10] at baseline [per protocol analysis], or a 4-fold rise from a baseline titer ≥10) and seroprotection (titer ≥10 [1/dil]) rates 28days after vaccination were both 98.5% (132/134) in the per protocol analysis, and 82.4% (201/244) and 98.8% (242/245), respectively, in the full analysis set. Geometric mean titers (GMTs) increased in all age groups from Day 0 to Day 28; Day 28/Day 0 GMT ratios were 55.3 (95% confidence interval [CI] 38.4-79.8), 348 (95% CI 211-572), 296 (95% CI 152-576) and 194 (95% CI 13.1-2870) in those aged 9 months to 4 years, 5-11 years, 12-17 years and 18-60 years, respectively, in the per protocol analysis. There were no safety concerns during the study. CONCLUSION: A single dose of JE-CV in children and adults aged 9 months to 60 years in Vietnam elicited a protective immune response and was well tolerated with no safety concerns. Registered at www.clinicaltrials.gov (NCT02492165).