In the context of new power systems featuring large-scale integration of high-proportion distributed energy resources, the overall flexibility level of virtual power plants directly impacts system stability and economic efficiency, further constraining the formulation of dispatch strategies. Addressing the issues of existing evaluation methods in multi-resource coordination, time-varying characteristic representation, and economic constraint integration, this paper establishes a comprehensive flexibility indicator system encompassing regulation capacity, ramp rate, response time, and regulation cost. This work proposes a comprehensive virtual power plant flexibility evaluation method based on combined subjective and objective weighting. First, a hierarchical aggregation evaluation framework is constructed spanning the user layer, resource aggregation layer, and virtual power plant layer. Next, the Extreme Gradient Boosting (XGBoost) algorithm is employed for feature contribution analysis to identify key flexibility influencing factors, establishing a multi-resource aggregation flexibility assessment model. Combining the Analytic Hierarchy Process (AHP), entropy weighting, and game theory for composite weighting, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is applied for comprehensive evaluation and ranking. Finally, case studies validate that the proposed method accurately characterizes the flexibility boundaries and time-varying characteristics of virtual power plants, providing effective support for their optimized operation and market decision-making.
This study synthesized the empirical literature published between January 2020 and May 2026 on cultural adaptation of Mainland Chinese migrants. Drawing on the PRISMA framework, 69 empirical studies were identified and synthesized thematically. Methodological quality was appraised using the Mixed Methods Appraisal Tool, and confidence in the synthesized findings was assessed using the GRADE-CERQual approach. Five analytical themes emerged: acculturation strategy, social media use, family networks, acculturative stress and psychological adjustment, and identity-based subgroups. The results indicate acculturation strategy and continuous cross-cultural negotiation are the factors affecting the dynamic and complex process of cultural adaptation of migrants from Mainland China. Methodological appraisal further identified systematic deficiencies in sample representativeness within the quantitative descriptive literature, and in the handling of divergence in mixed methods designs. This synthesis emphasizes the importance of subgroup heterogeneity, digital communication, and post-pandemic reception environments as productive avenues for future intercultural research.
Surrogate models are central to scientific machine learning, where they enable fast prediction, simulation, inference, and control for complex physical systems. For time-dependent problems, however, accurate interpolation of training trajectories is not sufficient: reliable surrogates should also respect the conservation laws, invariants, admissibility conditions, and dissipative structures that give those trajectories physical meaning. We introduce Physics-conforming Latent Twins, a framework for learning latent surrogate solution operators whose dynamics satisfy selected physical principles by design. The method builds on the Latent Twin formulation by jointly learning an encoder, a decoder, and a latent flow map between arbitrary time-indexed states, while constraining the latent dynamics to preserve or dissipate prescribed structural quantities. We develop a constraint-transfer viewpoint that separates pullback compatibility from latent conformity, connecting physical structure in the original state space with enforceable constraints in latent space, and prove structure-preservation bounds showing how latent enforcement improves control of physical defects after decoding. We also derive algebraic conditions for latent flow maps that preserve linear and quadratic invariants or enforce dissipative inequalities. Numerical experiments on representative ODE and PDE benchmarks demonstrate improved constraint satisfaction, structural fidelity, and qualitative long-time behavior while maintaining accurate surrogate prediction.
Testing the ΛCDM model requires cosmological probes spanning the wide redshift interval between Type Ia Supernovae (SNe Ia, z ≲ 2.9) and the Cosmic Microwave Background (CMB, z ≈ 1100). Gamma-Ray Bursts (GRBs), observed up to redshift z=9.2, offer the opportunity to explore part of this regime. Here, we investigate how many GRBs are needed to become a useful cosmological probe capable of independently testing deviations from ΛCDM suggested by the recent DESI BAO observations. We develop forecasts based on the two-dimensional X-ray and optical Dainotti relations, between the luminosity at the end of the plateau phase and its rest-frame duration. Using simulated GRB samples constructed from the observed population, we evaluate the constraining power of GRBs on cosmological parameters within the wCDM and w0waCDM models, both independently and in combination with CMB observations. Our results show that GRB samples containing several tens to hundreds of well-characterized plateaus can already approach the precision currently achieved by CMB measurements on the Dark Energy (DE) Equation of State (EoS) parameter w. Particularly, a sample of ∼ 66 optical GRBs can reach a precision σw ≈ 0.47, comparable to that obtained from Planck within the wCDM framework. Such sample sizes are already attainable through machine-learning techniques that double the number of GRBs using inferred redshifts. These forecasts indicate that future GRB observations, when combined with next-generation transient missions and improved statistical techniques, will provide an independent high-redshift probe of cosmic expansion and will play an important role in testing the robustness of potential Dynamical DE signals suggested by other cosmological datasets.
Dimethyl ether (DME) is a high cetane, oxygenated fuel that exhibits strong auto-ignition propensity and inherently low soot formation. However, its physical properties (e.g., density, viscosity, volatility) and chemical kinetics differ substantially from conventional diesel, resulting in spray evolution and combustion behavior that can deviate from typical diesel combustion. In this study, three-dimensional CFD simulations are used to decouple and quantify the roles of liquid-phase physical properties and gas-phase chemical kinetics by systematically swapping diesel and DME liquid fuel properties and reaction mechanisms to determine their effects on combustion. In addition, DME chemistry is replaced with surrogate mixtures of n-heptane and N2, as well as nheptane and O2, to further isolate kinetic versus mixing effects. Under comparable injection pressure and injection duration, the DME cases exhibit earlier ignition and faster chemical heat release rates compared to diesel. These differences are linked to the combined influence of injected jet momentum rate and the fuel's physical/ thermochemical characteristics, which primarily govern ignition delay and early heat release. After ignition, the main (mixing-controlled) burn rate for both fuels is dominated by air-fuel mixing, which is mainly driven by the injected jet momentum, and shows comparatively weaker sensitivity to the detailed chemical mechanism or other physical properties like the fuel's volatility. Nevertheless, the chemical kinetics mechanism remains critical for accurately predicting engine-out emissions. A key takeaway is that the oxygenation of the fuel results in higher injected momentum at matched injection pressure, and the higher momentum results in high mixing rates and heat release rates in mixing-controlled compression ignition combustion.
