Probabilistic modelling of deterioration of reinforced concrete structures

Reinforced concrete (RC) structures deteriorate over time which affects their strength and serviceability. To develop measures for protecting new RC structures against deterioration and assess the condition of existing RC structures subjected to deterioration an understanding of the deterioration processes and the ability to predict their development, including structural consequences, are essential. This problem has attracted significant attention from researchers, including those working in the area of structural reliability (in particular within the JCSS) since there are major uncertainties associated with the deterioration processes and their structural effects. The paper presents an overview of the probabilistic modelling of various deterioration processes affecting RC structures such as corrosion of reinforcing steel, freezing-thawing, alkali-aggregate reaction, sulphate attack and fatigue, and their structural implications, including the historical perspective and current state-of-the-art. It also addresses the issues related to the inspection/monitoring of deteriorating RC structures and the analysis of collected data taking into account relevant uncertainties. Examples illustrating the application of the presented probabilistic models are provided. Finally, the current gaps in the knowledge related to the problem, which require further attention, are discussed

Applying an ABM-LCA framework for analysing the impacts of shared automated electric vehicles across large-scale scenarios

Amidst the pressing need for sustainable transportation, Shared Automated Electric Vehicles (SAEVs) emerge as an increasingly explored solution with the potential to revolutionize mobility. Yet, understanding the environmental impacts of operating this mobility solution at different scales remains sparse. This study addresses this by integrating Agent-Based Modelling (ABM) and Life Cycle Assessment (LCA) to assess the environmental impacts of SAEVs at municipal, subregional and regional scales. ABM simulates travellers’ behaviour and SAEVs deployment strategies, yielding dynamic patterns along a typical day, while LCA provides a structured framework for assessing the life cycle environmental impacts. This process involves creating an ABM that reflects a representative mobility scenario, and a modified ABM scenario where private car and bus trips are replaced with SAEV services. The analysis extends the different scales, providing both short-term and long-term perspectives on LCA impacts. Findings revealed significant reductions in global warming potential (up to 91%), but challenges include increased operational intensity, human toxicity (up to 240%), and mineral resource scarcity (up to 229%). Vehicle kilometres travelled, and fleet replacement needs are key factors influencing long-term environmental impacts. Larger-scale implementation yields greater environmental benefits compared to smaller-scale deployment.</p

Evaluating reinforcement learning-based neural controllers for quadcopter navigation in windy conditions

Accurate quadcopter navigation under windy conditions remains challenging for traditional control methods, especially in the presence of unpredictable wind gusts and strict navigational constraints. This paper evaluates Deep Reinforcement Learning (DRL) based controllers under such conditions, analysing the impact of wind domain randomisation, multi-goal training, enhanced state representations with explicit wind information, and the use of temporal data to capture affecting dynamics over time. Experiments in the AirSim simulator across four trajectories — evaluated under both no-wind and windy conditions — demonstrate that DRL-based controllers outperform classical methods, particularly under stochastic wind disturbances. Moreover, we show that training a DRL agent with domain randomisation improves robustness against wind but reduces efficiency in no-wind scenarios. However, incorporating wind information into the agent’s state space enhances robustness without sacrificing performance in wind-free settings. Furthermore, training with stricter waypoint constraints emerges as the most effective strategy, leading to precise trajectories and improved generalisation to wind disturbances. To further interpret the learned policies, we apply Shapley Additive explanations analysis, revealing how different training configurations influence the agent’s feature importance. These findings underscore the potential of DRL-based neural controllers for resilient autonomous aerial systems, highlighting the importance of structured training strategies, informed state representations, and explainability for real-world deployment

Conceptualising Critical Thinking Skills: An Empirical Study of Malaysian Undergraduate Students and Academic Staff

Analytical and creative thinking are essential skills for employers today. Higher education should therefore be about learning how to think, not just what to think. Despite general agreement on the purpose of university, not all students master thinking skills, in part due to widespread difficulties in comprehending what critical thinking is. Two studies sought to make more widely accessible critical thinking descriptions from a common taxonomy by Facione and to explore perceptions of their relative importance. In study 1, 19 students and educators co-produced readable descriptions of critical thinking skills that were more understandable than the original set of descriptions, as measured by the Flesch-Kincaid metric. In study 2, 406 students rated all core skills in the taxonomy as important, with no meaningful differences in opinion across discipline or study year. Students’ insights from the interviews in study 1 supported this finding. Additionally, students expressed lower perceived self-efficacy in self-regulation skills which they recognised as under-developed within their university curriculum. These findings have broad implications for Asian higher education by breaking down barriers to understanding critical thinking concepts, so that educators can design more engaging and effective learning experiences in the classroom

Influence of Dilution Upon the Ultraviolet-Visible Peak Absorbance and Optical Bandgap Estimation of Tin(IV) Oxide and Tin(IV) Oxide-Molybdenum(IV) Sulfide Solutions

The study investigated the constraints associated with the dilution technique in determining the optical bandgap of nanoparticle dispersion and modified nanocomposites, utilizing ultraviolet-visible absorbance spectra and Tauc plot analysis. A case study involving SnO2 dispersion and SnO2-MoS2 nanocomposite solutions, prepared through the direct solution mixing method, was conducted to assess the implications of dilution upon the absorbance spectra and bandgap estimation. The results emphasize the considerable impact of the dilution technique on the measured optical bandgap, demonstrating that higher dilution factors lead to shift in bandgap values. Furthermore, the study highlights that dilution can induce variations in the average nanoparticle sizes due to agglomeration, thereby influencing bandgap estimation. In the context of nanocomposites, the interaction between SnO2 nanoparticles and exfoliated MoS2 nanosheets diminishes with increasing dilution, leading to the estimated optical bandgap being primarily attributable to SnO2 nanoparticles alone. These observations underscore the necessity for caution when employing the dilution technique for bandgap estimation in nanoparticles dispersion and nanocomposites, offering valuable insights for researchers and practitioners in the field

Demonstrating the effect of solvent aging on the volatile and aerosol-based emissions of the AMP/PZ-based solvent CESAR1 after 1,000 h and 30,000 h operation

A prerequisite for establishing reasonable guidelines for environmental, technical, and economically effective control of the emissions from amine-based capture plants is real-life testing of individual emission mitigation technologies in an industrial environment for different technical use cases and solvents. The operational data is also essential for validating and optimizing process models that predict volatile and aerosol-based emissions. The effect of solvent aging on the emissions of the capture plant is complex and affected by contrary trends. It is expected that emissions of volatile degradation products should increase with increasing degradation of the solvent and that the vapor pressure of the volatile solvent amines may be unaffected or even decrease by solvent aging due to increasing concentration of ionic non-volatile components in the solvent and water wash liquid based on salting out effects. To analyse the effect of solvent aging on volatile and aerosol-based emissions, two testing campaigns have been executed at the capture pilot plant in Niederaussem, Germany, using CESAR1 solvent, an aqueous solution of 3.0 M 2-amino-2-methylpropan-1-ol (AMP) and 1.5 M piperazine (PZ). The comparison of the levels of AMP, PZ, and NH3 emissions after 30,000 h operation without exchange of the solvent inventory and with a relatively fresh solvent inventory after 1,000 h operation shows a significant difference in the emission behavior of AMP and PZ when just a water wash is used to control the emissions of the capture plant. The AMP emissions of the fresh solvent are by a factor of two higher than for the aged solvent, while the emissions of PZ show no significant change with solvent aging or accumulation of contaminants. Two possible explanations for this unexpected strong effect are discussed in this work. However, there are effective emission mitigation technologies that can control emissions irrespective of solvent aging

Temperature impact on thermo-electrochemical behavior of silicon-based photoelectrochemical flow cells

Increased attention has been focused on photoelectrochemical redox flow cell systems as a potential integrated technology for simultaneously converting and storing intermittent solar energy. Photoelectrochemical voltammetry and impedance spectroscopy tests were conducted using a single-junction c-Si photoelectrode immersed in Fe(CN)63−/4− under thermal load to evaluate the temperature effect on the thermo-electrochemical performance of silicon-based photoelectrochemical cells. It was observed that the current density significantly increased with temperature as a consequence of enhanced kinetics and electrolyte characteristics, while a detriment to the potential output was identified and predominantly attributed to variations of photovoltaic characteristics. Moreover, it was demonstrated that mass transport enhancement reaches its maximum contribution at 45 °C, followed by a slowdown in the observed trends at higher temperatures, which may lead to improved design development and optimized working conditions

Impact of Surface Modification on Performance of Solar Concentrators

This study analyzes the impact of powder-blasted surface modification on the performance of non-imaging solar concentrators and evaluates a ray-tracing simulation approach to virtual solar power measurements. Powder blasting was applied to poly(methyl methacrylate) (PMMA) sheets to create a rough, Lambertian-like scattering surface, enhancing light trapping and total internal reflection. The effects of this modification were systematically assessed using optical transmission spectroscopy, angular scattering measurements, and solar cell efficiency characterization under standard AM1.5 illumination. The results show that surface roughening significantly improves light redirection toward the concentrator’s edge, enhancing solar cell performance. OptisWorks ray-tracing simulations were employed to model the concentrator’s optical behavior, demonstrating strong agreement (within 5–10% deviation) with experimental data. These findings confirm that surface modification is crucial in optimizing concentrator efficiency and establishing ray tracing as a reliable tool for virtual performance evaluation in photovoltaic applications

Environmentally-relevant hydrogen peroxide exposure induces DNA damage and elevates coelomocyte concentrations in the sea urchin Paracentrotus lividus

Hydrogen peroxide (H2O2) is an antiparasitic sea lice treatment in Atlantic salmon aquaculture and considered to be environmentally-friendly due to its rapid degradation. However, degradation rates have not been widely tested in seawater. The objectives of this study were to determine the degradation rates of different H2O2 stocks in aquarium-filtered seawater and assess its impact on adult sea urchins (Paracentrotus lividus). H2O2 stocks (stabilised, pure, and industry-sourced EndoSan50) combined degradation rate was 1.92 %/day, and the half-life was 26.4 days. Environmentally-relevant concentrations of H2O2 (50 and 500 µM) were selected for testing. Adult sea urchins were exposed to H2O2 for 3 and 24 h. Total coelomocyte and red cell concentrations increased by 50 ± 22 % and 122 ± 48 %, respectively, after 3 h, and by 59 ± 21 % and 88 ± 44 % after 24 h. DNA damage was analysed by the modified fast micromethod for quantification of strand breaks, oxidised purines (FPG), and oxidised pyrimidines (EndoIII). DNA damage in coelomocytes was increased to 0.05 ± 0.02 strand scission factor (SSF) in sea urchins exposed to 500 µM for 1 h. This study indicates presence of DNA damage in sea urchins from environmentally-relevant concentration of H2O2. Further testing of degradation rates of H2O2 in different sources of natural sea water is required to fully assess and model wider ecosystem exposure and ecological impacts of H2O2 release into coastal marine waters

Controlling Multiple Quantum Dots using Structured Light

We demonstrate the control of multiple, distinct quantum dots on one cryogenic sample via spatial light modulation, allowing us to observe cooperative emission from up to five dots and Hong-Ou-Mandel interference from two dots enabled by multi-plane optical circuits.</p