Intracellular compartments are functional units that support the metabolism within living cells, through spatiotemporal regulation of chemical reactions and biological processes. Consequently, as a step forward in the bottom-up creation of artificial cells, building analogous intracellular architectures is essential for the expansion of cell-mimicking functionality. Herein, we report the development of a droplet laboratory platform to engineer complex emulsion-based, multicompartment artificial cells, using microfluidics and acoustic levitation. Such levitated models provide free-standing, dynamic, definable droplet networks for the compartmentalisation of chemical species. Equally, they can be remotely operated with pneumatic, heating, and magnetic elements for post-processing, including the incorporation of membrane proteins; alpha-hemolysin; and mechanosensitive channel of large-conductance. The assembly of droplet networks is three-dimensionally patterned with fluidic input configurations determining droplet contents and connectivity, whilst acoustic manipulation can be harnessed to reconfigure the droplet network in situ. The mechanosensitive channel can be repeatedly activated and deactivated in the levitated artificial cell by the application of acoustic and magnetic fields to modulate membrane tension on demand. This offers possibilities beyond one-time chemically mediated activation to provide repeated, non-contact, control of membrane protein function. Collectively, this expands our growing capability to program and operate increasingly sophisticated artificial cells as life-like materials.

The long-term development of the manufacturing industry relies on sustainable tax management, which plays a key role in optimizing production costs. While artificial intelligence models have been applied to tax-related predictions, research on their application for predicting tax management levels is quite limited, with no studies focused on the manufacturing industry in China. To enhance digital innovation in corporate management, this study applies interpretable artificial intelligence models to predict the tax management level, which helps decision-makers maintain it within a sustainable range. The ratio of total tax expense to total profits (ETR) is used to represent the tax management level, which is predicted using decision trees, random forests, linear regression, support vector regression, and artificial neural networks with eight input features. Comparisons among the developed models indicate that the random forest model exhibits the best performance in terms of prediction accuracy and generalization capability. Additionally, the Shapley additive explanations (SHAP) technique is integrated with the developed model to enhance the interpretability of its predictions. The SHAP results reveal the importance of the input features and also highlight the dominance of certain features. The results show that the ETR from the previous year holds the greatest importance, being more than twice as significant as the second most important factor, whereas the effect of board size is negligible. Moreover, benefiting from the local interpretations using SHAP values, this approach aids managers in making rational tax management decisions.

With the aim of supporting future traffic needs, an account of how to reconstruct an existing cyclic timetable by inserting additional train services will be given in this paper. The Timetable-based Extra Train Services Inserting (TETSI) problem is regarded as an integration of railway scheduling and rescheduling problem. The TETSI problem therefore is considered involving many constraints, such as flexible running times, dwell times, headway and time windows. Characterized based on an event-activity graph, a general Mixed Integer Program model for this problem is formulated. In addition, several extensions to the general model are further proposed. The real-world constraints that concerning the acceleration and deceleration times, priority for overtaking, allowed adjustments, periodic structure and frequency of services are incorporated into the general model. From numerical investigations using data from Shanghai-Hangzhou High-Speed Railway in China, the proposed framework and associated techniques are tested and shown to be effective.

Accepted by: Prof. Aris Syntetos Understanding consumer attitudes towards electric vehicle (EV) purchasing is essential for addressing the slow adoption rate. Traditional aggregated models of EV adoption employ a top-down approach, yet often fail to capture individual-level attitudes. In contrast, agent-based modelling (ABM) enables a bottom-up approach that reflects the heterogeneity in consumer decision-making and simulates social interactions. This study introduces an integrated model to analyze consumer attitudes towards EV adoption, incorporating empirical data and synthesized social interactions through ABM. The model undergoes micro-validation and optimization through parameter variation experiments and supervised machine learning (SML) methods. Results indicate that consumer attitudes towards EV purchasing are positively influenced by early adopters and environmental factors. These attitudes are further shaped by observing EVs in residential areas and receiving positive feedback from social circles. Perceptions of EVs as an environmentally friendly alternative also significantly enhance these attitudes. These findings suggest that marketers should develop targeted strategies for specific consumer segments, and policymakers should prioritize environmental awareness campaigns to drive positive public EV attitudes in the UK. This study emphasizes the importance of incorporating consumer heterogeneity and social interactions in attitude formation, which offers insights into EV promotion within Rogers’s Diffusion of Innovations Theory.

Understanding consumer heterogeneity is crucial for analysing attitude formation and its role in innovation diffusion. Traditional top-down models struggle to reflect the nuanced characteristics and activities of the consumer population, while bottom-up approaches like agent-based modelling (ABM) offer the ability to simulate individual decision-making in social networks. However, current ABM applications often lack a strong theoretical foundation. This study introduces a novel, theory-driven ABM framework to examine the heterogeneity of consumer attitude formation, focusing on electric vehicle (EV) adoption across consumer segments. The model incorporates non-linear decision-making rules grounded in established consumer theories, incorporating Rogers’s Diffusion of Innovations, Social Influence Theory, and Theory of Planned Behaviour. The consumer agents are characterised using UK empirical data, and are segmented into early adopters, early majority, late majority, and laggards. Social interactions and attitude formation are simulated, micro-validated, and optimised using supervised machine learning (SML) approaches. The results reveal that early adopters and early majority are highly responsive to social influences, environmental beliefs, and external events such as the pandemic and the war conflict in performing pro-EV attitudes. In contrast, late majority and laggards show more stable or delayed responses. These findings provide actionable insights for targeting segments to enhance EV adoption strategies.

Investigating consumer attitudes towards electric vehicle (EV) purchasing is crucial for understanding their slow adoption rate. Traditional aggregated models evaluate EV market penetration with a top-down approach but fail to reflect individual attitudes. Agent-based modelling (ABM) captures consumer heterogeneous decision-making and simulates social interactions in a bottom-up approach. Our work represents a novel integrated model to study consumers’ attitudes towards EV adoption, using empirical data and synthesised social interactions with ABM. The developed model was micro-validated and optimised using parameter variation experiments and supervised machine learning (SML) methods. The results show that consumers’ attitudes towards EV purchasing are influenced by early adopters and environmental factors. This work concludes that capturing consumers’ heterogeneity plays an important role in investigating their attitude formation under social interactions, providing new insights into EV promotion as an application of Rogers’s Diffusion of Innovations Theory at an early stage.