Efficient warehousing resources distribution is essential for reducing logistics costs and improving supply chain performance. With advancements in information technology and the rise of the sharing economy, many enterprises are adopting the product-service system (PSS) to support cleaner production (CP) and circular economy (CE) strategies. However, logistics stakeholders face many challenges in developing effective sharing strategies under dynamic markets and personalized demands. To address these challenges, an evolutionary game-based approach to warehousing resource sharing (WRS) under the PSS paradigm to maximize stakeholder benefits is proposed in this paper. By using double auction mechanisms, a utility functions for suppliers and demanders are designed, after which the replicator dynamics equations and Jacobian matrices are applied to identify the evolutionarily stable strategies (ESS). Finally, a case study with numerical simulations are carried out to confirm the feasibility of the proposed approach. The results highlighted three key findings: (1) low cloud platform operating costs are vital for enabling unsupervised management; (2) suppliers exhibit sensitivity to initial sharing probabilities and subsidy rates; and (3) demanders can achieve enhanced flexibility and redundancy reduction through high information resource saturation. These insights can inform the formulation of effective WRS strategies to foster sustainable and competitive logistics ecosystems.

Electricity is fundamental to national economic and social development, and its stable supply depends on accurate power load forecasting. Thus, developing precise forecasting models is essential for efficient power system operation. However, increasing global energy demand exacerbates the volatility, randomness, and intermittency of power loads, compromising forecasting accuracy. To address complex dynamic data characteristics, this study proposes a hybrid forecasting method integrating parallel decomposition and deep learning. The method first decomposes the original data into multiple modal components and stable feature items, iteratively generating optimal sub-feature sets. Subsequently, an optimization framework is constructed based on the Sparrow Search Algorithm (SSA). This framework integrates binary feature selection with hyperparameter tuning. The tuned hyperparameters belong to an advanced neural network combining a Bidirectional Temporal Convolutional Network (BiTCN) and a Long Short-Term Memory (LSTM) network. This achieves joint feature and parameter optimization. Compared with traditional methods, this method fully exploits the temporal and structural characteristics of the data by integrating feature selection and hyperparameter optimization. For 6-step forecasting on dataset1, the method achieves a mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) are 2.30 %, 845.8 and 606.2, respectively.

Time-series models, particularly CNN-BiLSTM architectures, have shown advances in the lithological interpretation of well-logging data. However, CNN and attention mechanisms face challenges in training efficiency and predicting precision. To improve this deficiency, a dynamic and lightweight attention mechanism and a strategy that combines geological/spatial information have been proposed. This study introduces two novel enhancements: the Spatial and Stratigraphic Information Processing (SSP, shorten as Spatial and Strat) method and the Dynamic Range Attention (DRA) mechanism. SSP integrates geological context by encoding depositional sequences as time series. DRA is a lightweight attention module that adaptively adjusts local attention range based on global context. Experiments on a collected dataset from eastern Sichuan Basin (13 wells, 14,587 labeled samples) demonstrate that the proposed DRA-BiLSTM model with SSP achieves excellent performance, achieving accuracies of 0.99 on the training set, 0.97 on the validation set, and 0.92 on the test set, with low error rates of 0.08 for Top-5 and 0.02 for Top-1. Ablation studies confirm the critical roles of SSP in capturing geological patterns and DRA in balancing computational efficiency by paying more attention to vertical sedimentary process. These innovations significantly advance automated lithological interpretation, offering a robust framework for geophysical applications.

Autism is a disorder of neural development characterized by impaired social interaction and verbal and non-verbal communication, and by restricted, repetitive or stereotyped behavior. Visual supports can alleviate these challenges by juxtaposing communication with visual cues. In this paper, a "Rehabilitation Training System (RTS)" for children with autism is developed. The "RTS" is a computer based training system that can be used for training children with autism to learn basic shapes, colors, plants, animals, and find the odd man out etc., from a given set of patterns. It not only eases the teacher's effort but also motivates the child to learn the patterns by providing incentives via audio signals.