University of Queensland

Large language models (LLMs) are increasingly deployed in politically sensitive contexts, raising concerns about their susceptibility to ideological biases. In this work, we examine how synthetic persona conditioning shapes ideological expression across seven open-weight instruction-tuned models (7B-72B parameters) using the Political Compass Test (62 statements) as a standardized behavioral probe. Across three studies involving 200,000 synthetic personas and more than 260 million model responses, we analyze implicit and explicit malleability, as well as theme-associated variation. We find that: (i) larger models exhibit broader implicit ideological coverage, increasing from 14%-35% for 7-8B models to up to 49% for 70B+ models; (ii) explicit ideological priming induces large and statistically significant shifts, with right-authoritarian cues moving all models in the intended direction and generally producing larger shifts than left-libertarian cues across model-axis comparisons; (iii) left-libertarian priming produces more heterogeneous responses, with counter-directional economic shifts in three of four 7-8B models and intended-direction shifts across all 70B+ models; and (iv) theme-associated semantic content in persona descriptions is linked to systematic and interpretable directional shifts in ideological space. While our results identify an upstream mechanism through which persona conditioning can alter model responses under a standardized ideological probe, we do not test whether such shifts affect users' beliefs, decisions, or political behavior. Our findings are best understood as evidence of ideological malleability at the generation layer, highlighting the need to account for interactional factors when evaluating political neutrality, fairness, and safety in English-prompted, persona-conditioned language models.

The fast uptake of distributed energy resources (DERs) presents increasing challenges for managing hosting capacity in distribution networks. Existing solutions include direct load control, operating envelopes, and price-based control through dynamic energy prices. Despite their effectiveness, these methods often rely on assumed prosumer behavioural patterns and overlook prosumers' desire to retain control over their devices. Additionally, current fixed or Time-of-Use (ToU) prices are based on spatial and temporal averages, having limited impact on network conditions and DER operation. To address these limitations, this paper proposes a bilevel optimisation framework that explicitly models prosumer decision-making in the design of dynamic network prices. The upper level represents the distribution system operator (DSO), setting network prices under cost-recovery and network constraints, while the lower level models prosumers optimising DER operation in response. The proposed framework preserves customer prerogative, enhances DER flexibility, and offers actionable insights for network hosting capacity management and the evolution of network tariff structures under high DER penetration.

We show that every N >= 2 can be written as the sum of positive integers a and b where Omega(ab) <= 40. The result is obtained through the direct application of an explicit lower bound Selberg sieve along with some computation and optimisation. (c) 2026 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

The finite preview of energy price forecasts or the pre-dispatch price has potential to improve performance of batteries participating in energy market and therefore must be included as a key control design parameter. Fixed preview and reactive (one-step ahead) preview strategies are often suboptimal and computationally inefficient because, in such approaches, the batteries are exposed to the uncertain pre-dispatch issued by the market operator. In addition, energy market price volatility changes across seasons and times of day, so a finite preview that is optimal in one period may perform poorly in another. This paper proposes a dynamically adjusted preview length for proactive operation of grid-scale batteries, particularly Community Battery Storage (CBS). A supervisory layer uses Bayesian Online Change-Point Detection (BOCPD) to track real-time pre-dispatch prices, detect regime shifts, and tune the preview horizon. A lower-layer RHC then updates dispatch decisions every five minutes using the adjusted horizon, while enforcing time-varying dynamic operating envelopes for network-aware operation. By adapting its preview length to market volatility, the proposed method significantly increases battery revenue across representative daily scenarios and achieves an overall 9.23% year-round revenue improvement compared with the conventional fixed-preview approach, while also maintaining lower computational burden.

The incorporation of biomass-derived char into coking coal is attracting increasing interest as a decarbonisation strategy; however, its influence on coal behaviour during carbonisation and the resulting coke structure remains insufficiently understood. This study investigates the role of surface energy in coal-char interactions during pyrolysis and examines the effect of char addition on the pore structure of coke. The rheological behaviour of two Australian bituminous coals of differing rank, a medium-volatile coal (MVC) and a high-volatile coal (HVC), with vitrinite contents of 73.6 % and 77.2 %, respectively, was analysed to map their viscoelastic pathways and understand bubble evolution during pyrolysis. MVC exhibited a relatively high viscosity (>10(5)Pa & centerdot;s) within its liquid-like state, enabling stable bubble growth before coalescence. In contrast, HVC exhibited a lower viscosity (3 & times; 10(4)Pa & centerdot;s), resulting in unstable bubbles that collapsed shortly after nucleation. Surface energy measurements obtained using the film flotation method revealed that MVC possessed a surface energy of 57 mJ m(-)(2)during the bubble growth stage, whereas HVC exhibited a substantially lower value of 41 mJ m(-)(2). Notably, bagasse char produced at 800 degrees C exhibited a surface energy of 57 mJ m(-)(2), matching that of MVC. Optical microscopy confirmed that approximately 95 % of this char was incorporated into MVC, compared with only 64 % incorporation in HVC. These observations suggest that surface energy matching promotes wetting and interfacial bonding between coal and char during coking. Micro-CT analysis and pore size characterisation further demonstrated that char addition substantially reduced the pore size distribution and overall porosity of both coals, resulting in denser coke structures. These findings demonstrate that surface energy compatibility and coal viscoelastic behaviour play important roles in coal-char interactions, pore development, and ultimately coke strength, thereby advancing the substitution of fossil coal with renewable carbon in ironmaking applications.