Fractional quantum dynamics provides a natural framework to describe nonlocal temporal behavior and memory effects in quantum systems. Building on this idea, we develop a fractional Green’s function formulation for the time-dependent Schrödinger equation based on the Caputo fractional derivative, which enables a systematic treatment of fractional-order quantum evolution. When applied to an extended two-level Rabi model, a paradigmatic setting for coherent quantum control, we find that even the static Hamiltonian term leads to non-trivial spin dynamics with damping features directly linked to fractional temporal nonlocality. Upon introduction of a periodic driving field, the fractional regime produces controllable damping and dephasing governed by the order of the fractionality, which could be observed through the Loschmidt echo and autocorrelation function, offering potential routes to probe fractional quantum dynamics experimentally. Our findings open pathways to explore memory-induced dynamical phenomena in other systems effectively described by two-level approximations, such as graphene-like materials and topological SSH chains, where non-integer-order evolution may reveal novel topological or relaxation regimes.
Narrowband near-infrared (NIR) emissive materials are indispensable for advancing optical communications, biological imaging, and precision sensing technologies. Despite their potential, achieving precise control over the emission profile while ensuring the long-term stability of the material remains a significant challenge. In this study, we utilized a diaminomaleonitrile derivative to demonstrate an approach that combines geometric (cis-trans) isomerism and crystal size to control the ultra-narrowband NIR emission of an organic crystalline material. By systematically optimizing key parameters, including light exposure, temperature, and solvent diffusion dynamics, we established a robust platform for controlled and preferential growth of its two isomers with specific optical properties. Remarkably, the crystals of the cis-isomer exhibited size-dependent narrowing of the NIR emission band, with an impressive narrow full-width-at-half-maximum of 40 nm (0.096 eV), while the trans-isomer exhibited ultranarrowband NIR emission with outstanding photostability attributed to its rigid, planar molecular structure. Comprehensive theoretical and experimental analyses revealed the critical role of intermolecular interactions and vibrational relaxation in modulating their emission characteristics. This study establishes a robust methodology for the preparation of narrowband organic NIR-emissive materials.
Wave-farm power prediction is central to large-scale deployment but remains challenging due to hydrodynamic coupling, inter-WEC interactions, and the highly nonlinear layout-control design space. Existing surrogate models are typically calibrated with a single accuracy objective, which often fails to ensure robustness, low bias, and stable performance in high-power regimes. To address this limitation, this study proposes a Cooperative Multi-objective Covariance Matrix Adaptation Gradient Ensemble model, CMA-SXSCE, for high-fidelity prediction of total absorbed power in 16-WEC wave farms. The framework combines heterogeneous cooperative learning with graph-based representations of spatial and hydrodynamic interactions, while hyperparameter tuning is formulated as a constrained multi-objective optimisation problem to jointly balance accuracy, relative error, high-power robustness, and distributional consistency. Comprehensive experiments are conducted on large-scale, high-resolution datasets representing two contrasting Australian wave climates (Perth and Sydney). Relative to advanced deep and ensemble baselines, including TabNet, voting ensembles (VCGD), and bagged decision trees (Bag-DT), the proposed CMA-SXSGE surrogate delivers substantial and consistent gains. For the Perth wave site, CMA-SXSGE achieves up to an 86.8% reduction in MAE, a 26.6% improvement in R2, and a 61.0% reduction in TDA ratio compared with TabNet, while also reducing MAE by 76.5% and 53.0%, and lowering the TDA ratio by 68.0% and 28.6%, relative to VCGD and Bag-DT, respectively. These results demonstrate that the proposed prediction model substantially enhances accuracy, robustness, and high-power reliability beyond state-of-the-art neural and ensemble surrogates.
The energy transition has evolved beyond the search for individual low-carbon technologies, moving instead toward the integration of these solutions into complex infrastructures in urban and industrial settings. This Special Issue synthesizes key research from the 2024 SDEWES conferences - 4th Latin American, 2nd Asia Pacific and 19th SDEWES, highlighting a fundamental shift from technology-centric assessments toward integrated system thinking. Central to this collection is the recognition that flexibility and resilience are now just as vital as cost-efficiency, particularly as energy systems face increasing pressure from climate-driven volatility and infrastructural constraints. The research presented covers several critical pillars of deep decarbonization. It explores urban transformation through integrated planning and energy communities, while advancing power-to-X pathways to address "hard-to-abate" sectors such as heavy industry and maritime transport. Technologically, the contributions evaluate the practical application of direct and indirect electrification, waste heat utilization, carbon-managed waste conversion, wind, wave and hydrogeneration. Methodologically, a clear trend emerges: a move toward high-resolution, dynamic modeling that treats climate-driven variability, spatial and temporal uncertainty as central variables rather than outliers. By addressing the physical, economic, and institutional bottlenecks of the transition, these papers provide an evidence-based framework for real-world implementation.
The scholarly publishing system is adapting to many changes, including open access and open data mandates, artificial intelligence, and other new technologies. Members of the research and publishing communities are working to establish a more equitable, fair, and rigorous system that serves researchers' evolving needs. Early career researchers (ECRs) are drivers of change, and publishers may wonder why and how they should involve ECRs in shaping the future of scholarly publishing. We held a virtual unconference to explore this issue with publishers and ECRs who were working to improve publishing. Some participants sought to improve peer reviewer or editor performance, whereas others sought to improve the publishing system itself through iterative or transformative change. Strategies for collaborating with ECRs to shape the future of scholarly publishing included peer review programmes, editorial programmes, ECR-led journals, ECR boards and committee representatives, and other ECR-initiated activities. ECRs particularly wanted to see three things improved: (1) Sharing research outputs other than publications, (2) addressing technological limitations to create systems that meet the research community's needs and facilitate knowledge advancement, and (3) fostering diversity, equity, inclusion, and accessibility. We offer tips for publishers on how to collaborate with ECRs to enhance scholarly publishing, appeal to and learn from younger researchers, and better meet researchers' needs.
