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The importance of Safety Management Systems (SMS) to the railway industry is underlined by the fact that all organisations operating on UK railways are required by law to have one. Analysing SMSs can provide a reliable systemic tool to identify hazards and weaknesses within complex systems like the railway, making it possible to significantly increase safety, reducing the odds of near misses and accidents. However, there is little empirical research evidence to determine the impact on safety of a structured SMS. The current paper describes two studies which use Bayesian Belief Networks (BBN) to conceptualise SMSs and their impact on front-line performance. The paper presents the usefulness of BBNs to compare complex systems and reconcile cultural differences within the railway industry, identifying factors that are deemed vital within Italy and Britain. The two studies allowed us to identify the most influential factors within a SMS and how they interact with each other, as well as the strength of the identified relationships. A BBN is particularly useful in estimating how changing some of the node states (e.g., by making safety leadership present) affected the other factors. The current study showed that safety leadership has an impact on the SMSs of the British and Italian railway industries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Electroless deposition on patterned silicon substrates enables the formation of metal nanomaterials with tight control over their size and shape. In the technique, metal ions are transported by diffusion from a solution to the active sites of an autocatalytic substrate where they are reduced as metals upon contact. Here, using diffusion limited aggregation models and numerical simulations, we derived relationships that correlate the cluster size distribution to the total mass of deposited particles. We found that the ratio ξ between the rates of growth of two different metals depends on the ratio γ between the rates of growth of clusters formed by those metals through the linearity law ξ = 14( γ - 1). We then validated the model using experiments. Different from other methods, the model derives k using as input the geometry of metal nanoparticle clusters, decoded by SEM or AFM images of samples, and a known reference., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)