Inland bodies of water, such as lakes, play a crucial role in sustaining life and supporting ecosystems. However, with the rapid development of socio-economics, water resources are facing serious pollution problems, such as the eutrophication of water bodies and degradation of wetlands. Therefore, the monitoring, management, and protection of inland water resources are particularly important. In past research, empirical models and machine learning models have been widely used for the water quality assessment of inland lakes. Due to the complexity of the optical properties of inland lake water bodies, the performance of these models is often limited. To overcome the limitations of these models, this study uses in situ water quality data from 2017 to 2018 and multispectral (MS) remote sensing data from Sentinel-2 to construct experimental samples of Poyang Lake. Based on these experimental samples, we constructed a spatio-temporal ensemble model (STE) to evaluate four common water quality parameters: chlorophyll-a (Chl-a), total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). The model adopts an ensemble learning strategy, improving the model’s performance by merging multiple advanced machine learning algorithms. We introduced several indices related to water quality parameters as auxiliary variables, such as NDCI and Enhanced Three, and used band data and these auxiliary variables as predictive variables, thereby greatly enhancing the predictive potential of the model.The results show that the inversion accuracy of these four inversion models is high ($R^2$ of 0.94, 0.88, 0.92, and 0.93; RMSE of 1.15, 0.01, 0.02, and 0.02; MAE of 0.81, 0.01, 0.09, and 0.10), indicating that the STE model has good evaluation accuracy. Meanwhile, we used the STE model to reveal the spatio-temporal distribution of Chl-a, TP, TN, and COD from 2017 to 2018, and analyzed their seasonal and spatial variation rules. The results of this study not only provide an effective and practical method for monitoring and managing water quality parameters in inland lakes, but also provide water security for socio-economic and ecological environmental safety.

In a practical development environment, the completion of a job often necessitates the coordinated allocation of multiple resources. However, resource constraints such as molds are seldom taken into account in the multi-resource job shop scheduling problem. These molds have sequence-dependent setup times and are fully occupied during processing. Therefore, this study focuses on the Flexible Job Shop Scheduling Problem with Mold Resource Setup Time Optimization (FJSP-MRST), which aims to minimize setup time and makespan. Considering the characteristics of mold setup time, this paper introduces a Mold-Machine Double Fusion Insertion Method to optimize the mold-machine usage order and enhance the mold changeover time calculation method. This innovative approach improves the overall scheduling process by efficiently managing the utilization of molds and machines. Furthermore, we propose a Mold-Operation Layer Loading Method to compress setup time, offering a novel strategy for reducing the setup time for mold resources. These two pioneering methods are integrated with an enhanced multi-objective differential evolution algorithm to obtain the optimal solution for FJSP-MRST. Experimental results demonstrate that the proposed methods can render the arrangement of machines and molds under the current process sequence more reasonable and effectively eliminate implicit setup time.

It is crucial to ensure the privacy and authenticity of the owner’s information in car insurance claims. However, the current traditional car insurance claims scenario suffers from inefficiency, complex service, unreliable data, and data leakage. Therefore, considering the privacy and sensitivity of insurance information and car owner data, we can use blockchain, smart contracts, and zero-knowledge proof technology to improve the current problems. This paper proposes a novel car insurance claim scheme based on smart contracts, blockchain, and zero-knowledge proof. Our scheme focuses on preserving privacy in the car insurance authorization and claim process. We design a private smart contract for the creation and revocation of car insurance and public smart contract for the authorization and validation of car insurance. By using ZoKrates, generating zero-knowledge proofs off chain and verifying the proofs on chain reduces the amount of data storage and computation on chain and provides privacy protection for sensitive information. Experimental results confirm the efficacy of our scheme in terms of security and performance.

Urban rivers are faced with serious water quality deterioration, and an efficient, comprehensive and accurate dynamic monitoring method for urban water quality is urgently needed. Remote sensing water quality inversion model based on spectral features has been widely used in water quality monitoring. However, traditional water quality inversion models ignore the complex optical properties of water bodies, resulting in the performance of models that are often limited and cannot be reused across time and space. Therefore, this study applies the idea of integrated modeling to remote sensing inversion of water quality parameters(WQPs), and proposes an adaptive dynamic weighted ensemble learning (DWE) model, aiming to integrate the advantages of different machine learning models, improve the accuracy of WQP inversion, and adaptively assign dynamic weights to different machine learning models, thereby achieving the scalability of the model in time and space. In addition, to address the limitations of using drone data to infer small-scale water WQP due to spatial resolution limitations and limited input features, we propose an ASO-GSA feature selection method for selecting the optimal feature combination from potential feature set data. The results show that the DWE model based on optimal features has high accuracy and robustness in WQP prediction, with coefficients of determination (R²) of 0.8472, 0.8547, 0.8973, and 0.8393 for chlorophyll a (Chl-a), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), respectively, which improved the prediction accuracy by nearly 30% compared with the traditional inversion model. In order to evaluate the spatiotemporal scalability of the model, we applied the model to unmanned aerial vehicle (UAV) remote sensing data acquired from the Nanxintang River during the first half of the year from 2024 to 2025 (nearly one year later) and the Shangheng River between 2023 and 2024 to assess the scalability of the model. The results show that our model can be extended across time and space(R²of 0.8012, 0.8387,0.8462 and 0.7863; R²of 0.7892, 0.8153,0.7587 and 0.7664). The results of this paper can further inform water quality monitoring and water resource management in urban rivers.