“I want to be honest...but how much can I share?”:Sustainable Influencing and Experiences of Moral Residue

Transparency is the cornerstone of social media influencing. Research has explored how influencers disclose commercial interests, yet little is known about influencers’ self-disclosure of private consumption. Building on the transparency management and moral hypocrisy literatures, this paper explores how sustainable influencers navigate moral dilemmas as they communicate about sustainability. Through interviews and analysis of media articles, we find that sustainable fashion influencers experience persistent emotional baggage, which we frame as moral residue as well as moral hypocrisy, in navigating three moral dilemmas: (anti)consumption; (non)promotion; and (non)commercialization. To reconcile this, sustainable fashion influencers engage in transparency management, choosing between strategies of confessing, concealing, and/or conning. These strategies may inadvertently lock sustainable influencers in perpetual cycles of moral residue and moral hypocrisy. In explicating the process and potential outcomes of managing transparency around moral dilemmas, we provide an intrapersonal view of moral hypocrisy and offer implications for theory and practice

Firm Positionality and Strategic Communication:Analyzing the Value of Informativeness for Managers

This study explored the moderating effects of price informativeness on the relationship between brand value and firm positionality. We argued that major effects for price informativeness are not confined to its direct effects on a firm’s market position or value, but in its moderating effects on brand-firm relationship. At the same time, the analysis confirmed that price informativeness can negatively moderate brand value’s influence on firm positionality. This study has significant implications for firms to strategically position and implement their communication strategies in a better way as to rapidly respond to fluctuations in the firm’s positionality

Multi-Objective Optimisation of a Hydrogen Combustion Mechanism with Direct Kinetic Modelling:Application to Combustion Engines

Hydrogen combustion can decarbonise difficult-to-abate sectors. However, practical deployment depends on reliable prediction of combustion behaviour under transient conditions, which contrasts with the steady-state experiments typically used for combustion mechanism development. This study presents a fully optimised H2-NOx mechanism, calibrated against 118 fundamental combustion datasets containing 1695 datapoints, which shows significant improvements in the prediction of ignition onset in an internal combustion engine with nitric oxide injection into the intake system.In contrast to prior single-objective approaches, this study introduces a fundamentally new approach to chemical kinetic mechanism optimisation, which leverages a Multi-Objective Particle Swarm Optimisation framework on a High-Performance Computing platform. The framework simultaneously balances accuracy and consistency across datasets, explicitly incorporates experimental uncertainty, and evaluates all candidate mechanisms with full chemical simulations. Prediction accuracy is quantified using the normalised root mean square error (nRMSE) to experimental measurements and the proportion of predictions within experimental uncertainty limits. Relative to the best existing mechanism, the optimised model achieves a 35 % reduction in nRMSE and a 19 % increase in the number of predictions within uncertainty bounds, demonstrating improved predictive performance for fundamental combustion targets.When the optimised mechanism was applied to autoignition timing in a Homogeneous Charge Compression Ignition engine, significant improvements were found for data with nitric oxide. Nevertheless, the overall accuracy in autoignition prediction is insufficient for practical applications, indicating that transient engine conditions are not adequately represented by steady-state datasets. These findings underscore that even fully optimised mechanisms based solely on fundamental experiments will not deliver high-accuracy predictions under real-world, transient conditions and integration of transient combustion data into future development of chemical mechanisms is recommended.<br/

Novel adaptive sliding-mode control of digital hydraulic systems with nonlinear flow prediction and friction identification

Digital hydraulics has emerged as a novel technology widely utilized in engineering equipment, heavy-duty manipulators, and new energy vehicles. However, the high-frequency discrete fluid generated by high-speed on/off valves (HSVs) exacerbates the nonlinear characteristics of digital hydraulic systems (DHSs), thereby limiting control accuracy during fluid transmission. To address this issue, a model-based adaptive sliding-mode control method (ASMC) is proposed, which incorporates two soft measurement methods that integrate friction identification for the DHS with nonlinear flow prediction for the HSV to accurately describe the kinetic model. Subsequently, the coupling parameters in the Stribeck friction model are precisely identified using the particle swarm optimization-least squares algorithm, replacing previous empirical values. Additionally, a high-precision output flow prediction model for the HSV is constructed utilizing a back propagation neural network to address the drawbacks associated with mechanical inertia in the flowmeter. A second-order integral sliding-mode surface is designed to eliminate steady-state error. By incorporating a boundary layer saturation function, the error jitter can be effectively suppressed, allowing the DHS to converge rapidly to a quasi-sliding mode. Furthermore, the stability of the controlled system is validated by the Lyapunov theory. Results indicate that ASMC significantly enhances the dynamic-static performance of the DHS compared to the traditional integral sliding-mode control method, which overlooks the nonlinear behaviors of output flow and friction force. The response characteristic’s setting time is dramatically reduced from 0.86 s to 0.36 s, while the maximum average steady-state error under various loads greatly decreases from 112.4 μm to 23.4 μm. Therefore, the proposed ASMC with the two soft measurement methods presents an innovative solution for the high-precision motion control of the DHS and holds significant engineering application value

Vortex-carrying solitary gravity waves of large amplitude

In this paper, we study two-dimensional traveling waves in finite-depth water that are acted upon solely by gravity. We prove that, for any supercritical Froude number (non-dimensionalized wave speed), there exists a continuous one-parameter family C of solitary waves in equilibrium with a submerged point vortex. This family bifurcates from an irrotational uniform flow, and, at least for large Froude numbers, extends up to the development of a surface singularity or blowup of the circulation. These are the first rigorously constructed gravity wave-borne point vortices without surface tension, and notably our formulation allows the free surface to be overhanging. We also provide a numerical bifurcation study of traveling periodic gravity waves with submerged point vortices, which strongly suggests that some of these waves indeed overturn. Finally, we prove that at generic solutions on C—including those that are large amplitude or even overhanging—the point vortex can be desingularized to obtain solitary waves with a submerged hollow vortex. Physically, these can be thought of as traveling waves carrying spinning bubbles of air.</p

Multi-Objective Optimisation of a Hydrogen Combustion Mechanism with Direct Kinetic Modelling:Application to Combustion Engines

Hydrogen combustion can decarbonise difficult-to-abate sectors. However, practical deployment depends on reliable prediction of combustion behaviour under transient conditions, which contrasts with the steady-state experiments typically used for combustion mechanism development. This study presents a fully optimised H2-NOx mechanism, calibrated against 118 fundamental combustion datasets containing 1695 datapoints, which shows significant improvements in the prediction of ignition onset in an internal combustion engine with nitric oxide injection into the intake system.In contrast to prior single-objective approaches, this study introduces a fundamentally new approach to chemical kinetic mechanism optimisation, which leverages a Multi-Objective Particle Swarm Optimisation framework on a High-Performance Computing platform. The framework simultaneously balances accuracy and consistency across datasets, explicitly incorporates experimental uncertainty, and evaluates all candidate mechanisms with full chemical simulations. Prediction accuracy is quantified using the normalised root mean square error (nRMSE) to experimental measurements and the proportion of predictions within experimental uncertainty limits. Relative to the best existing mechanism, the optimised model achieves a 35 % reduction in nRMSE and a 19 % increase in the number of predictions within uncertainty bounds, demonstrating improved predictive performance for fundamental combustion targets.When the optimised mechanism was applied to autoignition timing in a Homogeneous Charge Compression Ignition engine, significant improvements were found for data with nitric oxide. Nevertheless, the overall accuracy in autoignition prediction is insufficient for practical applications, indicating that transient engine conditions are not adequately represented by steady-state datasets. These findings underscore that even fully optimised mechanisms based solely on fundamental experiments will not deliver high-accuracy predictions under real-world, transient conditions and integration of transient combustion data into future development of chemical mechanisms is recommended.<br/