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The study of star cluster evolution necessitates modeling how their density profiles develop from their natal gas distribution. Observational evidence indicates that many star clusters follow a Plummer-like density profile. However, most studies have focused on the phase after gas ejection, neglecting the influence of gas on early dynamical evolution. We investigate the development of star clusters forming within gas clouds, particularly those with a centrally concentrated gas profile. Simulations were conducted using the Torch framework, integrating the FLASH magnetohydrodynamics code into AMUSE. This permitted detailed modeling of star formation, stellar evolution, stellar dynamics, radiative transfer, and gas magnetohydrodynamics. We study the collapse of centrally concentrated, turbulent spheres with a total mass of 2.5 × 103 M⊙, investigating the effects of varying numerical resolution and star formation scenarios. The free-fall time is shorter at the center than at the edges of the cloud, with a minimum value of 0.55 Myr. The key conclusions from this study are: (1) the final stellar density profile is more centrally concentrated than was analytically predicted, reflecting the role of global gas collapse and feedback; (2) subclusters can initially form even in centrally concentrated gas clouds; (3) gas collapses globally toward the center on the central free-fall timescale, contradicting the assumption in analytical models of local fragmentation and star formation; and (4) the mass of the most massive star formed is directly correlated with the cluster effective radius and inversely correlated with the velocity dispersion, while the duration of star formation correlates with the star formation efficiency.
Sustainable plastic waste management is essential for net zero trajectory, potentially transforming the sector from an emissions source to a circular asset. MPWs (Municipal Plastic Wastes) that are not mechanically recycled can go through pyrolysis-based chemical recycling to produce hydrogen and diesel. There is limited understanding about the optimal configuration and design of pyrolysis-based chemical recycling of plastic waste. Associated attempts to optimise the recycling is rare. In this study, a reliable optimisation framework incorporating machine learning, life cycle assessment and cost-benefit analysis was developed for the design of the pyrolysis of Non-Recycled Municipal Plastic Waste (NMPW). Specifically, the global warming potential (GWP) and net-present value (NPV) of 900 diesel and hydrogen-producing scenarios for the pyrolysis of NMPW were calculated. Associated transportation and pyrolysis process were modelled using ArcGIS Pro and Aspen Plus, respectively. The long short-term memory recurrent neural network (LSTM-RNN) was applied to define temporal dependencies and dynamics of the system, which was integrated with Monte Carlo simulations to expand scenarios from 900 to 700,000. A Pareto curve was derived from the GWPs and NPVs, from which the optimal scenario in terms of environmental and economic performance was identified based on the comparison of two multi-criteria decision-making approaches, i.e., TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and LINMAP (Linear Programming Technique for Multidimensional Analysis of Preference). The solutions by TOPSIS and LINMAP achieved GWPs of -2,570.42 and -1,025.28 kg CO2-eq per tonne NMPW, and NPVs of £300.32 and £-1,402.92 per tonne NMPW, respectively. Thus, the TOPSIS scenario is preferable to the LINMAP scenario due to its lower carbon footprint and higher economic feasibility. This study showed that the proposed optimisation framework has the capacity to facilitate the design of pyrolysis-based processing of NMPW that is profitable and carbon-saving. Such systems could be deployed widely across the UK, where a large share of NMPW is currently either landfilled or incinerated.
This research is dedicated to developing an integrated mathematical model for the comprehensive assessment of Microservice Architecture (MSA) dependability, accounting for both technical failures and cyber threats. 1 The traditional separate analysis of fault tolerance and security is insufficient for adequately evaluating the overall resilience of distributed systems. The methodology employs the apparatus of stochastic modeling (Markov chains), combining reliability metrics (failure rate, restoration rate) and security parameters (attack probability, defense effectiveness). The model calculates the steady-state probability of a single service being operational and the overall system being operational considering architectural complexity. Numerical modeling demonstrated that systemic resilience is exponentially sensitive to security factors and architectural sprawl. Investment in increasing defense effectiveness is identified as a critical multiplicative factor for ensuring system availability in scalable MSA. The proposed model is a practical tool for Site Reliability Engineering (SRE) and cyber resilience assurance.
