Nanjing University of Posts & Telecommunications

In recent years, the problem of microplastics (MPs) pollution has become increasingly serious, and there is an urgent need to develop highly sensitive and efficient microplastic detection and adsorption technologies. Based on surface-enhanced Raman scattering (SERS) technology, a new type of modified hydrogel (M-Hydrogel) substrate was prepared by compounding chitosan, polydopamine and polyvinyl alcohol with nano-silver as the enhancing unit. Taking polyvinyl chloride, polymethyl methacrylate and polyvinylidene fluoride as typical MPs, the adsorption behavior and mechanism of M-Hydrogel on MPs were systematically studied, and the surface morphology, chemical composition and thermal stability of M-Hydrogel were revealed through characterization and analysis. The experimental results show that the adsorption capacity of M-Hydrogel for the same MPs in different actual water bodies does not change much. After six cycles, the adsorption capacity for MPs decreases by less than 46%. Further single-component and mixed sample detection experiments were conducted to verify the high sensitivity and selectivity of M-Hydrogel combined with micro-Raman spectroscopy in the detection of MPs. This study provides a new strategy for the efficient detection and adsorption of MPs in the environment, which has potential practical application value.

Sustainable transboundary water management is increasingly compromised by climate-induced deep uncertainty, as traditional open-loop strategies fail to adapt to non-stationary hydrological shifts and physical propagation time-lags. To overcome these structural rigidities, this study proposes a Physics-Informed Multi-Agent Reinforcement Learning (PI-MARL) framework. Operating as a closed-loop controller, it maps real-time basin states-explicitly accounting for physical routing delays-to optimal release decisions. Empirical validation in the Yarlung Tsangpo-Brahmaputra (YTB) River Basin reveals that AI agents autonomously learn cooperative, pre-emptive release strategies to achieve spatio-temporal risk substitution. Crucially, this robust coordination emerges through decentralized real-time execution, provided riparians agree upon a hybrid reward structure that internalizes transboundary risks. This effectively shifts the hydro-political challenge from operational micro-management to objective macro-negotiation. Rigorous stress testing under calibrated stochastic extreme events confirms that this AI-driven approach significantly outperforms traditional static baselines, reducing downstream flood peaks by 16.3% and elevating system reliability to 99.2%. By quantifying the value of information flow underpinning this mechanism, our findings provide a scientific foundation for transitioning transboundary governance from rigid quotas to adaptive, algorithmic real-time control.

This paper proposes a probabilistic shaping modulation scheme based on index-driven constellation partitioning. The 32QAM constellation is divided into eight non-overlapping energy-level patterns, with every three consecutive subcarriers grouped as a mapping unit controlled jointly by index bits through a predefined lookup table. By assigning more subcarriers to low-energy constellation modes located in the inner circle, the activation probability of low-energy symbols is increased, thereby reducing the average transmit power. Experimental validation is conducted over a 2 km seven-core fiber optic system. Experimental results demonstrate that at a bit error rate (BER) of 3.8 × 10 −3 , the proposed index-driven constellation-shaped signal achieves up to 0.53 dB gain compared to uniform 32QAM signals. Meanwhile, the BER performance variation among different modes remains below 0.5 dB. With low implementation complexity, the proposed scheme exhibits promising potential in multi-user optical access scenarios.