Reactions of TpRu(CO)(NCMe)(Ph) with electron-rich olefins: examples of stoichiometric C-S, C-O and C-H bond cleavage

Stoichiometric reactions of TpRu(CO)(NCMe)(Ph) with electron-rich olefins result in metal-mediated cleavage of C-S and C-O bonds.     

The Dehn Function of Stallings’ Group

We prove that the Dehn function of a group of Stallings that is finitely presented but not of type F₃{\mathcal{F}_3} is quadratic.     

Simulation modelling is 50! Do we need a reality check?

Simulation modelling is a fascinating research field. The techniques and tools of simulation modelling have been used to research and investigate the behaviour of various systems in a wide range of areas such as commerce, computer networks, defence, health, manufacturing and transportation. Indeed, the study of the use of these techniques and tools, and the development of new forms of these, are a rich source of research in their own right. Simulation modelling is about to reach the 50th anniversary of the development of GSP (General Simulation Program), the first simulation modelling language (Tocher and Owen, 1960). There have been several historical accounts of simulation modelling research. To complement these, we have performed a review of the recent history of simulation modelling. This study targeted three leading journals dedicated to this field. These are the ACM Transactions of Modeling and Computer Simulation, Simulation: Transactions of The Society for Modeling and Simulation International and Simulation Modelling Practice and Theory (formerly Simulation Practice and Theory). The study covered the first 6 years of this century (2000–2005) and included 576 papers. The key observation of this work was the relative lack of ‘real world’ involvement in simulation modelling research and an even greater lack of evidence of ‘real world’ benefit, arguably very alarming outcomes for an applied field. To further investigate this observation two additional surveys were carried out, one to study if real world papers appeared in the more widely known OR/MS literature (837 papers in 12 journals) and one to study if such papers appeared in Manufacturing and Logistics, an application area closely associated with simulation modelling (1077 papers in 10 journals). The results of these surveys confirmed our observations. We ask if this is the natural evolution of a field that has existed for half a century or an indication of a worrying problem? This paper reports on our findings and discusses whether or not simulation modelling research urgently needs to face a ‘reality check.’     

Mal'cev nilpotent algebras

We show that the Mal'cev semigroup identity x_n = y_n holds in the circle semigroup of an associative algebra over an infinite field precisely when the algebra is Lie nilpotent of class at most n. The Mal'cev semigroup law x_n = y_n holds in a group if and only if the group is nilpotent of class at most n.     

Golem: an algorithm for robust experiment and process optimization

Numerous challenges in science and engineering can be framed as optimization tasks, including the maximization of reaction yields, the optimization of molecular and materials properties, and the fine-tuning of automated hardware protocols. Design of experiment and optimization algorithms are often adopted to solve these tasks efficiently. Increasingly, these experiment planning strategies are coupled with automated hardware to enable autonomous experimental platforms. The vast majority of the strategies used, however, do not consider robustness against the variability of experiment and process conditions. In fact, it is generally assumed that these parameters are exact and reproducible. Yet some experiments may have considerable noise associated with some of their conditions, and process parameters optimized under precise control may be applied in the future under variable operating conditions. In either scenario, the optimal solutions found might not be robust against input variability, affecting the reproducibility of results and returning suboptimal performance in practice. Here, we introduce Golem, an algorithm that is agnostic to the choice of experiment planning strategy and that enables robust experiment and process optimization. Golem identifies optimal solutions that are robust to input uncertainty, thus ensuring the reproducible performance of optimized experimental protocols and processes. It can be used to analyze the robustness of past experiments, or to guide experiment planning algorithms toward robust solutions on the fly. We assess the performance and domain of applicability of Golem through extensive benchmark studies and demonstrate its practical relevance by optimizing an analytical chemistry protocol under the presence of significant noise in its experimental conditions.

Numerous challenges in science and engineering can be framed as optimization tasks. Golem is an uncertain-input algorithm that ensures the reproducible performance of optimized experimental protocols and processes.