In recent years, research on improving the energy consumption ratio of pumping equipment through control algorithms has improved. However, the actual behavior of pump equipment and pump characteristic information do not always correspond, resulting in deviations between the calculated energy consumption operating point and the actual operating point. This eventually results in wasted power. To solve this problem, the data from circulating pumping equipment in a large pumping facility are analyzed, and the necessary characteristics of pumping equipment electrical energy consumption are analyzed through a subset of mechanism expansion feature engineering using the Pearson correlation coefficient algorithm. Based on this, a pump energy consumption prediction method based on LightGBM is constructed and compared with other algorithm models. To improve the generalization ability of the model, rules applicable to pump power energy consumption prediction are proposed, and the model features and processes are reduced. Based on the mechanistic model, 18 features related to electric energy consumption are selected, and 6 necessary features of pump electric energy consumption are screened by feature engineering. The experimental results show that the LightGBM regression algorithm has a significant prediction effect with $R^2=0.94$. After the importance analysis, three features that are strongly related to pump energy consumption are finally screened out. According to the prediction results, the feature engineering dataset was selected and the pump electrical energy consumption was predicted based on the LightGBM algorithm, which can significantly reduce the problem of deviation in the prediction of the electrical energy consumption of pumping equipment.

Imbalanced classification tasks are widespread in many real-world applications. For such classification tasks, in comparison with the accuracy rate (AR), it is usually much more appropriate to use nondecomposable performance measures such as the area under the receiver operating characteristic curve (AUC) and the $F_β$ measure as the classification criterion since the label class is imbalanced. On the other hand, the minimax probability machine is a popular method for binary classification problems and aims at learning a linear classifier by maximizing the AR, which makes it unsuitable to deal with imbalanced classification tasks. The purpose of this article is to develop a new minimax probability machine for the $F_β$ measure, called minimax probability machine for the $F_β$ -measures (MPMF), which can be used to deal with imbalanced classification tasks. A brief discussion is also given on how to extend the MPMF model for several other nondecomposable performance measures listed in the article. To solve the MPMF model effectively, we derive its equivalent form which can then be solved by an alternating descent method to learn a linear classifier. Further, the kernel trick is employed to derive a nonlinear MPMF model to learn a nonlinear classifier. Several experiments on real-world benchmark datasets demonstrate the effectiveness of our new model.

In contextual multi-armed bandits, the relationship between contextual information and rewards is typically unknown, complicating the trade-off between exploration and exploitation. A common approach to address this challenge is the Upper Confidence Bound (UCB) method, which constructs confidence intervals to guide exploration. However, the UCB method becomes computationally expensive in environments with numerous arms and dynamic contexts. This paper presents an adaptive noise exploration framework to reduce computational complexity and introduces two novel algorithms: EAD (Exploring Adaptive Noise in Decision-Making Processes) and EAP (Exploring Adaptive Noise in Parameter Spaces). EAD injects adaptive noise into the reward signals based on arm selection frequency, while EAP adds adaptive noise to the hidden layer of the neural network for more stable exploration. Experimental results on recommendation and classification tasks show that both algorithms significantly surpass traditional linear and neural methods in computational efficiency and overall performance.

This article provides a new framework to evaluate the status of Renminbi internationalization. It proposes that the trading patterns of a currency in the global foreign exchange market embody the currency's position in the international monetary system. Based on foreign exchange trading data provided by CLS Group, the article constructs a ranking of major international currencies including the Renminbi. It finds that the Renminbi shares more similarities in foreign exchange trading patterns with the established global currencies like the US dollar and the Euro than with those regional currencies. The article also explores the policy implications that the new evaluation approach provides.

Long-horizon spatio-temporal forecasting under strong seasonal distribution drift, mixed temporal periodicity, and time-varying inter-node dependency remains a challenging problem, with ozone concentration prediction serv ing as a representative and demanding instance. We propose TriAtt-STNet, a channel–temporal–spatial tri-attention network with seasonality-aware em bedding that targets this general class of problems and is instantiated here for long-horizon ozone forecasting. The framework integrates hybrid-statistics reversible normalization to stabilize distribution drift by fusing instance-level and climatological priors, seasonality-aware embedding to preserve temporal context and spatial identity, and stacked spatio-temporal attention blocks to jointly capture dominant periodic structures and dynamic inter-node cou pling. Experiments on gridded ozone data over East China show that TriAtt STNet achieves the best overall performance among the compared baselines, while cross-horizon, ablation, and visualization analyses further demonstrate its robustness and interpretability. Beyond ozone, the design is broadly ap plicable to spatio-temporal forecasting tasks that exhibit seasonal drift and multi-scale periodicity, such as air-quality, traffic, hydrological, and energy demand prediction, providing both an effective data-driven basis for envi ronmental decision support and a general modeling perspective for the time series and spatio-temporal learning community.