Mahalanobis-Taguchi System (MTS), as a pattern recognition method by constructing a continuous measurement scale, has a very good performance on classification and feature selection for real-valued data. However, the record of symbolic interval data has become a common practice with the recent advances in database technologies. Kernel methods not only are powerful statistical nonlinear learning methods, but also can be defined over objects as diverse as graphs, sets, strings, and text documents. In this paper, we derive kernel Mahalanobis distance (KMD) to extend MTS to symbolic interval data. To evaluate the proposed method, four experiments with synthetic symbolic interval data sets and seven experiments with real symbolic interval data sets are performed and we have compared our method with MTS based on interval Mahalanobis distance (IMD). The experimental results show our method has a better classification performance than MTS based on IMD on Accuracy, Specificity, Sensitivity, and G-means. However, MTS based on IMD has a stronger dimension reduction rate than our method.

Purpose: To maintain the bottom line of food import risk in China, this paper proposes a novel risk state evaluation model based on bottom-line thinking after analyzing the decision-making ideas embedded in the bottom-line thinking method. Design/methodology/approach: First, the order relation analysis method (G1 method) and Laplacian score (LS) are applied to calculate the constant weights of indexes. Then, the worst-case scenario of food import risk can be estimated to strive for the best result, so the penalty state variable weight function is introduced to obtain variable weights of indexes. Finally, the study measures the risk state of China's food import from the overall situation using the set pair analysis (SPA) method and identifies the key factors affecting food import risk. Findings: The risk states of food supply in eight countries are in the state of average potential and partial back potential as a whole. The results indicate that China's food import risks are at medium and upper-medium risk levels in most years, fluctuating slightly from 2010 to 2020. In addition, some factors are diagnosed as the primary control objects for holding the bottom line of food import risk in China, including food output level, food export capacity, bilateral relationship and political risk. Originality/value: This paper proposes a novel risk state evaluation model following bottom-line thinking for food import risk in China. Besides, SPA is first applied to the risk evaluation of food import, expanding the application field of the SPA method.

Smart microgrid is promising in providing a more affordable, efficient, and sustainable energy solution with increasing energy production from distributed renewable sources and diverse household electricity usage with large amounts of connected smart devices. Accurate prediction of the household electricity load and renewable energy production plays a significant role in achieving optimized efficiency of the microgrid. Meanwhile, the privacy and security of data sharing over the smart grid are crucial. This paper proposes a blockchain-enabled microgrid Internet of Things (MIoT) with accurate predictions of renewable energy production and household electricity load. The blockchain framework can guarantee the privacy and security of data sharing over the microgrid. An improved model by stacking long short-term memory (LSTM) and gated recurrent units (GRUs) is proposed for energy generation and electricity load predictions using historical data in the microgrid and the weather forecasting data. The sparrow search algorithm optimized by Levy flights (LevySSA) is used to optimize the hyperparameters of the stacked LSTM-GRU method. The experimental results verify the accuracy and robustness of the proposed method in the prediction of electricity load and renewable energy production for effective smart microgrid operation. For PV forecasting, the proposed LevySSA-LSTM-GRU achieves nRMSE = 0.0535, nMAE = 0.0455, and R² = 0.9898, outperforming the strongest baseline. For load forecasting, averaged over four test intervals, it yields nRMSE = 0.1034, nMAE = 0.0836, with R² = 0.9340, demonstrating consistent superiority compared with conventional baseline models. Overall, the proposed framework enables secure data sharing and high-accuracy forecasting, offering strong potential to support real-time energy management and operational optimization in smart microgrids.

To promote global sustainable development, this paper focuses on the identification of relative poverty. On the one hand, based on the sustainable livelihoods framework, a multi-dimensional relative poverty identification index system is constructed, covering six dimensions—human capital, financial capital, natural capital, physical capital, social capital, and livelihood environment—with a total of 18 indexes. On the other hand, addressing the limitations of traditional relative poverty identification methods in handling dynamic three-dimensional data, the multiway Mahalanobis–Taguchi system (MMTS) is proposed to identify dynamic relative poverty. This method first unfolds dynamic three-dimensional data into two-dimensional data along the sample direction through multiway statistical analysis techniques, then constructs multiway Mahalanobis distances to measure sample differences, and finally uses a Taguchi orthogonal experimental design for dimensionality reduction and noise reduction to optimize the model. Experiments using tracking survey data from 2020 to 2024 in three poverty-stricken counties in China’s Dabie Mountain area show that MMTS performs better than the Two-Way Fixed Effects (Two-way FE) model and Dynamic LSTM. MMTS shows a higher specificity, stronger noise resistance, smaller result fluctuations, better G-means performance, and a better balance between sensitivity and specificity. This proves its scientific validity and practical applicability.