The current study offers a sentence-level natural language processing approach aimed at detecting competitive discourse and measuring equity orientation in education policy statements. The sample contains 800 policy statements published from 2015 to 2024 by national and provincial educational authorities. After being preprocessed and checked manually, 60,284 sentences from official education policies have been selected for further analysis. Competitive discourse is classified into five categories: selection competition, performance ranking, cultivation of elites, assessment pressures, and school competition. Meanwhile, equity orientation is measured on four levels: access, resource, process, and outcome equity. The BERT-Base model for Chinese was fine-tuned using a training set of 5,000 annotated sentences, and policy balance scores were obtained with the help of discourse and equity measurement indicators. Experiments based on simulated policy statements show that the accuracy of the classifier can be assessed as 0.884 ± 0.017. In addition, the findings confirm that policy balance scores improved starting from 2021 and concerning compulsory education and provincial-level policies.

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Image classifiers often report high accuracy on clean benchmark data, but in the real world, their inputs are not always clean. This paper tests how two convolutional models respond when part of the fashion-MNIST test set is deliberately corrupted. A custom four-layer convolutional neural network and an ImageNet-pretrained ResNet-18 were trained only on clean Fashion-MNIST images. A fixed corruption, Gaussian blur followed by contrast enhancement, was then applied to 0%, 10%, 30%, 50%, 70%, and 100% of the official test images. Both models used the same corrupted image set. Accuracy, mean predictive entropy, and t-distributed stochastic neighbor embedding plots were used to compare the runs. The custom model reached 91.31% accuracy on clean data but fell to 10.82% when every test image was corrupted. ResNet-18 started lower, at 88.10%, but reached 20.44% at 100% corruption. Entropy also rose with the corrupted percentage, from 0.1729 to 0.5379 for the custom model and from 0.1517 to 0.3073 for ResNet-18. These suggest that both clean-trained models were fragile under this specific corruption, while ResNet-18 held up better in this run. A possible explanation is that its deeper residual structure and ImageNet-pretrained layers preserved more coarse shape information after blur weakened fine local details.

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The rapid development of brain-computer interfaces has made memory uploading, brain controlling of some basic functions of computers, and the treatment of diseases or disabilities like paralysis and blindness possible. However, human consciousness cannot be fully replicated. This paper analyzes why brain-computer interface (BCI) cannot completely replicate human consciousness via a literature review method from three aspects: the limitations of BCI technology, the non-replicability of consciousness, and the challenges of interdisciplinary integration. Research shows that technical issues such as low signal precision and data scarcity restrict its reliability, subjective experiences and nonlinear neural activities of consciousness are difficult to simulate by programs, and that decoding neural signals is not equivalent to reproducing consciousness. Existing theories of consciousness lack engineerable equations, so human consciousness cannot be replicated completely under current situation.

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Dust storms develop rapidly and can affect transport, ecosystems, and public health over large areas. The integration of satellite remote sensing and artificial intelligence (AI) has significantly improved capabilities in dust storm identification, parameter retrieval, and short-term forecasting. This paper reviews how AI algorithms have been applied to remote sensing monitoring of dust storms. The review first summarizes the remote sensing response characteristics of dust aerosols and the primary data sources. It then focuses on machine learning-based pixel classification and parameter retrieval, deep learning-based dust mask segmentation and short-term forecasting, and the application of physics-guided hybrid methods in dust storm monitoring. The review also discusses the applications of these dust monitoring products and it points out several remaining problems, including identification instability in complex environments, lack of labeled samples, the underestimation of extreme dust events, and the lack of physical consistency in short-term forecasts. Future studies should focus on improving joint observations of multi-source data, building cross-regional datasets, expanding the pool of extreme event samples, and developing interpretable short-term forecasting models. Such progress will help improve the quantification, continuous observation and practical application of AI-based dust storm remote sensing monitoring.

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