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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Spike sorting is a fundamental step in extracellular neural signal analysis, but educational implementations are often difficult to inspect and reproduce because processing assumptions, parameter choices, and evaluation procedures are distributed across multiple stages. This paper constructs a transparent and reproducible baseline workflow for simulated neural signals rather than proposing a new sorting algorithm. The workflow integrates band-pass filtering, threshold-based spike detection, fixed-window waveform extraction, principal component analysis, KMeans clustering, one-to-one temporal matching, and automated metric export in a configurable Python implementation. Experiments are conducted on repository-generated synthetic datasets with easy, medium, and hard noise settings and on the Wave_Clus Easy1 noise series. Detection F1-score decreases from 0.812 to 0.457 as the synthetic setting becomes harder, while Wave_Clus Easy1 results range from 0.340 to 0.907 across noise conditions. The low clustering scores observed in several settings further show that accurate event detection does not necessarily imply reliable unit separation. The study contributes an auditable baseline, a reproducible evaluation process, and a teaching-oriented example that makes the distinction between detection and clustering performance explicit.

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Financial technology is playing an increasingly vital role in loan decision-making, and financial institutions are increasingly relying on machine learning techniques to support credit decisions. The purpose of this review is to provide a critical overview of analysis comparing the practical applicability of deep neural network (DNN) and logistic regression (LR) models within the credit scoring domain. This paper systematically collects existing studies on the application of DNN and LR models in credit scoring. The research objects include DNN and LR models, as well as their improved variants developed on the original model frameworks. On this basis, it integrates theoretical research findings with comprehensive analyses to investigate and evaluate the practicality of DNN models. The research results indicate that DNN models still exhibit significant limitations in credit scoring applications. Further model improvements or hybrid integration with other models are therefore required to enhance their practical applicability in real-world scenarios.

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