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Dynamic loading makes the effective design of phase change material (PCM) coupled liquid-cooling battery thermal management systems a time-dependent optimization problem. The first step in system design is to select or configure PCM with appropriate thermophysical properties. Currently, no optimal PCM selection strategy exists for frequent thermal shock conditions, and research on the coupling relationships and substitutability among these parameters remains scarce. This study contributes two coupled parts. A methodology system integrating multi-method modelling with a six-layer interpretation and validation pipeline, and the quantitative substitution relationships among PCM thermophysical properties that this system discovers for material selection guidance. A machine learning surrogate was established to analyze the effects of C-rate, duration and four thermophysical parameters on liquid fraction, average temperature and maximum temperature, with the optimal model selected through systematic comparison. SHAP interpretation reveals that the dominant material parameter shifts with discharge stage. For liquid fraction, the governing factor shifts from specific heat capacity in the initial stage to thermal conductivity in the late stage, while for temperature responses, melting temperature gives way to thermal conductivity as the dominant parameter. Within the 0–210 s transient thermal control window, coupling relations among latent heat, melting temperature and thermal conductivity were clarified. A latent heat increase of 100 kJ/kg corresponds to a thermal conductivity rise of 0.34 W/(m·K) to maintain stable maximum temperature, whereas an opposite trend emerges when liquid fraction is considered for economic evaluation. Thermal conductivity increases by 0.1 W/(m·K) per 1 °C rise in melting temperature, providing guidance for melting thermal management design.
In this paper, we investigate the synergistic effect of a fixed punishment probability and a Q-learning-based update mechanism in the spatial public goods game (SPGG) to explore the effects of punishment intensity and punishment cost on the evolution of cooperation. In the model, individuals can choose among three strategies: cooperation, defection, and punishment. Punishers impose sanctions on defectors with a fixed probability and bear the corresponding costs. Strategy updates are governed by a Q-learning algorithm, in which individuals dynamically adjust their strategy preferences based on the payoff they receive as an immediate reward. Through large-scale Monte Carlo simulations, we found that increasing punishment intensity significantly enhances cooperation, whereas increasing punishment cost suppresses the viability of punishers and undermines the stability of cooperation. Furthermore, the range of immediate reward also modulates evolutionary outcomes: when individuals use only a single round of payoff as the learning feedback, the system tends to maintain a high level of cooperation; in contrast, when individuals use five rounds of payoffs as the learning feedback, the evolution of cooperation becomes more dependent on punishment intensity, requiring a higher intensity to suppress defection effectively. These findings may help us uncover the intrinsic mechanisms of cooperative evolution under the coupling of a fixed punishment probability and Q-learning, offering a new perspective on the effectiveness of punishment institutions in structured populations and providing insights into incentive design in multi-agent cooperative systems.
Context. Compton-thick active galactic nuclei (CT-AGNs), defined by a column density of NH >= 1.5 & times; 1024 cm-2, are so heavily absorbed that their X-ray emission is often feeble and can even be undetectable by X-ray instruments in some cases. Nevertheless, their radiation is expected to be a substantial contributor to the cosmic X-ray background (CXB), predicting that CT-AGNs would comprise at least similar to 30% of the total AGN population. Aims. Cosmological Evolution Survey (COSMOS) reported that the identified CT-AGN fraction falls far below theoretical expectations, indicating that a substantial population of CT-AGNs is hidden due to their low photon counts or due to their flux lying below the current flux limits of X-ray instruments. This work focuses on identifying CT-AGNs hidden among mid-infrared (MIR) selected AGNs. Methods. First, we selected a sample of 1,104 MIR-selected AGNs that were covered, but individually undetected by X-ray. Next, we reduced the X-ray data in the COSMOS and analyzed multiwavelength data in our sample to derive the key physical parameters required for the CT-AGN identification. Results. Using MIR diagnostics, we found 7 to 23 CT-AGN candidates. Their subsequent X-ray stacking analysis revealed a clear detection at > 3 sigma significance in the soft band and only a > 1 sigma significance in the hard band. We fit the stacked soft- and hard-band fluxes with a physical model and confirm that these sources are absorbed by Compton-thick material. However, CT-AGNs ultimately constituted only 2.1% (23/1104) of our sample, significantly below the fraction predicted by CXB synthesis models. This indicates that a considerable population of CT-AGNs remains missed by our selection. A comparison of host-galaxy properties between CT-AGNs and non-CT-AGNs reveals no significant differences.
Aqueous zinc-ion batteries (AZIBs) are safe and cost-effective, making them ideal for large-scale energy storage and wearable electronics. Nevertheless, the advancement of AZIB technology faces constraints due to limited energy storage capability and degraded cyclability, primarily attributed to the absence of optimal cathode materials. In this study, two structurally similar but chemically different covalent organic framework materials (Aza-COF-1L and Aza-COF-2) were synthesized. Notably, Aza-COF-1L is easier to synthesize and features abundant pyrazine redox-active sites, a well-defined porous structure, and intrinsic stability. Consequently, Aza-COF-1L exhibited an electrochemical performance superior to that of Aza-COF-2. Aza-COF-1L achieved initial capacities of 368.58, 345.96, and 327.82 mAh·g−1 at 0.1, 0.5, and 1 A·g−1, respectively. After 800 cycles at 1 A·g−1, Aza-COF-1L maintains a specific capacity of 136.38 mAh·g−1. In contrast, Aza-COF-2 exhibited an initial capacity of 117.79 mAh·g−1 at 0.1 A·g−1, and its capacity significantly decreased during cycling. Additionally, the contribution of the C=N pyrazine redox site in Aza-COF-1L to battery capacity during charging and discharging was experimentally analyzed. These results provide valuable guidance for the development of high-performance organic cathode materials for use in AZIBs.
This paper focuses on the frequency-amplitude relationship of nonlinear oscillators and proposes an improved ancient Babylonian algorithm. This algorithm constructs a solution framework based on linear and nonlinear operators in a unique iterative form, and cleverly selects the initial guess value and determines the frequency equation. Through in-depth exploration of several representative nonlinear oscillator examples (covering different forms of nonlinear terms and parameter settings), it fully demonstrates its specific operation and effectiveness verification process in the solution process. The results show that this algorithm performs well in weakly nonlinear oscillator problems, and the obtained results are highly consistent with existing methods or exact solutions. Moreover, it is equivalent to He's frequency formula under specific conditions, strongly supporting the effectiveness of the latter. At the same time, it clearly reveals the influence of the law of the nonlinear term coefficient and amplitude on the accuracy of the algorithm. However, in the case of strongly nonlinear systems, the algorithm has certain limitations. This study combines ancient numerical wisdom with modern nonlinear dynamics, providing a computationally simple and effective tool for oscillator engineering, while also indicating directions for improvement to enhance strong nonlinear performance.
