Yongrui Xiao

This paper concerns chemical, biological and other experiments used in R&D labs, where the aim is to optimise some performance indicator by adjusting the materials and processing parameters. Such experiments require significant resources and time, motivating the use of early stopping strategies to terminate unpromising runs before completion. However, stopping an ongoing experiment based on incomplete observations carries the risk of incorrectly terminating runs that would have achieved satisfactory outcomes, a quantity we term the false stop rate (FSR). To address this, we propose Confidence-Bound Early Stopping with Sequential Calibration (CBES), a two-layer framework that employs Gaussian process regression to predict the final outcome from partial observations and combines a confidence-bound decision rule with a calibration procedure to ensure that the FSR remains below a user-specified level. We compare CBES against four baseline stopping criteria on two distinct domains: in vitro permeation testing (IVPT) for pharmaceutical formulation and LCBench for hyperparameter optimisation. The results demonstrate that CBES achieves reliable FSR control with substantial time savings. This work offers a flexible framework for experimental processes, with broad applicability in fields such as chemical engineering, biotechnology, and material science. •A confidence-bound early stopping framework with sequential calibration (CBES) is proposed.•Gaussian process regression provides probabilistic predictions of final experimental outcomes.•Sequential calibration controls the false stop rate at a user-specified level without manual tuning.•A Hoeffding-based bound provides a formal generalisation guarantee on the false stop rate.•CBES achieves reliable false stop rate control with substantial time savings across two domains.

Formulating effective skin products requires navigating complex chemical mixtures,skin biophysical and biochemical properties and manufacturing processes, all under budgetary and time constraints. Controlling dermal permeation, a key driver of efficacy, often presents the primary development bottleneck. Conventional development methods are slow, hampered by low-throughput, variable test assays (e.g., in vitro release and permeation testing) and limited access to biologically relevant in vitro skin models. This review argues for a shift towards autonomous, assay-aware formulation design, outlining a closed-loop framework that unifies intelligent candidate generation, automated experiment selection and robust analysis across a skin-specific multi-tiered assay strategy. The foundations of barrier transport and formulation behaviour are first synthesised. Key enabling technologies are then systematically surveyed, including automation technologies (e.g., microfluidic and modular platforms), automated analytics (e.g., chromatographic pipelines, auto-sampling for diffusion cells) and artificial intelligence (e.g., hybrid mechanistic/data-driven surrogates and constraint-aware active learning). Building upon this foundation, a practical framework is discussed that foregrounds cross-tier calibration between rapid screens and pivotal assay endpoints. Its workflow centres on model generalisation, uncertainty quantification and robust system orchestration. The goal is to provide a credible path towards faster, more reproducible and acceptance criteria-aligned decisions for skin product formulation efficacy.

This study reports the reaction mechanism and electrolyte optimisation aspects of a novel low-temperature water-splitting system developed for the efficient production of hydrogen based on the Mn–MnSO4 redox pair. The system incorporates an electrolysis step and an Mn2+ ion recovery step for splitting water in a cyclic operation. Two steps operate within similar temperature ranges, enabling tight integration and efficient heat exchange. The optimisation of electrolytes for the electrolysis step was first carried out in a proton-exchange membrane (PEM) H-cell. The experiments were figured out using a three-factor case study based on the factorial design approach, incorporating temperature, concentration, and pH value as the main variables. Subsequently, machine learning models were employed to analyse the data and predict the best pairing of electrolytes by systematically exploring the critical ratio of conductivity to potential. The results showed that at a cell voltage of 5.0 V and 40 °C, the ratio of importance between the conductivity and MEDR potential is 1:9 for the catholyte, while the anolyte ratio of importance between the conductivity and OER potential is 6:4. Accordingly, the optimal electrolyte composition was found to be a combination of MnSO4 solution (1.64 mol/L; pH 2.86) with H2SO4 (25.25 wt%). Also, a remarkable corresponding current efficiency of 99.25 % was achieved with an overall energy conservation efficiency of 40.15 %. The proposed cycle is the first of its kind developed based on the chemical looping principle and can be potentially applied for large-scale continuous green hydrogen production at a low-levelized cost.. [Display omitted]. •A novel hybrid water-splitting cycle was proposed and validated for H2 production.•A factorial design approach was applied to optimise the electrolyte properties.•The two-stage current and cell-voltage mechanism were identified.•An unprecedented current efficiency showed great potential for scale up.

Formulated topical drugs (and personal care products) contain diverse and varied mixtures. The experiments for formulation design can be time-consuming, especially those for optimising the delivery of active ingredients into the skin, the so-called in vitro permeation test (IVPT). A single IVPT typically takes 24 hrs and consumes significant resources for sample collection and chemical analysis. In this study, an early decision-making algorithm (EDMA) that can terminate unpromising experiments early, thereby prioritising resources on promising ones and potentially accelerating formulation design is proposed. The algorithm relies on a flexible Gaussian process regression (GPR) model for prediction during the experiments, while the prediction uncertainty is accounted for by a statistical measure, the probability of exceedance (PoE), to guide decision-making. This algorithm was applied to maximise ibuprofen permeation from a gel-like formulation through IVPT. The results show that it is feasible to determine whether a certain formulation has the potential to achieve higher permeation before the end of experiment, leading to significant savings on time and resources.

This paper investigates the formation problem for second-order Multi-Agent System (MAS). By introducing an impulsive protocol, the MAS can eventually reach a desired geometric formation. With a reliable theorem and some necessary Lemmas, the error system can asymptotically achieve consensus. A group of simulation is demonstrated in the end of this paper.

This paper mainly discusses the formation of second-order multi-agent systems with fixed and switching topologies by time-delayed impulsive control. The key issue is how to design an appropriate impulsive control algorithm to maintain a desired geometric formation with input delays and switching topologies between neighboring agents. First, according to the restriction of systems, the impulsive protocol is proposed for the follower agents. Then, by using the stability theory, some conditions are derived to ensure the formation consensus. Finally, the numerical examples illustrate the effectiveness and correctness of the designed impulsive control algorithm.

Topical skin products aim to address aesthetic, protective, and/or therapeutic needs through interaction with the human epidermal system. Traditionally, formulation development relies on empirical knowledge and trial-and-error experiments. In this paper, we introduced the Bayesian optimisation method and compared it with the traditional response surface methodology (RSM) for topical drug formulation. The objective was to optimise the formulation composition of ibuprofen gel-like to achieve a maximum flux through in vitro permeation tests (IVPTs). As a model system, poloxamer 407, ethanol, and propylene glycol (PG) were selected as the key excipients, whose concentrations were optimised. Strat-M membrane, serving as a surrogate for human skin, and Franz cell diffusion were employed in IVPTs. Two sets of experiments were conducted under identical conditions for 30 h. Under the RSM approach, the optimised ibuprofen gel-like formulation was identified with a poloxamer 407: ethanol: PG ratio of 20:20:10, achieving a measured permeation flux of 11.28 ± 0.35 μg cm−2h−1. In comparison, Bayesian optimisation, after four iterations, yielded an optimised formulation with a ratio of 20.95:19.44:12.14, resulting in a permeation flux of 14.15 ± 0.77 μg cm−2h−1. These findings highlight the potential of Bayesian optimisation as an effective tool for improving topical drug formulations.