Mathematical information retrieval remains fragmented as readers commonly view math-heavy PDFs in standard viewers while relying on separate specialized formula search tools. This separation restricts exploration of documents where meaning arises from the combination of symbolic notation and natural language. MathMex-PDF is an interactive PDF reader that unifies visual mathematics extraction and multimodal retrieval within a single workflow. The system processes raw PDF pages to detect and recognize mathematical expressions, grounds those expressions in the document layout, and provides unified search over both text and formulas. Users may submit natural-language, LaTeX, or hybrid queries and perform query-by-example interactions such as click-to-search Reverse Formula Lookup directly from rendered pages. Retrieval uses a late-fusion pipeline that combines structure-aware formula matching with dense text embeddings and applies reciprocal rank fusion to adopt modality-specific rankings. Lightweight client-side rendering is paired with cached server-side processing to support responsive interaction on standard hardware. The demo provides interactive formula grounding, hybrid math–text querying, visual navigation of mathematical content, and spoken-math annotations to improve accessibility.

This study proposes a model based on machine learning(ML) for predicting the thickness of a coil in a steel rolling process. To achieve this objective, it is necessary to establish an engineering strategy based on data collected from actual rolling sites and to develop multiple models. Five models are trained on preprocessed process data, and the optimal model is determined through experiments.

This paper analyzes a deep learning model technique that diagnoses the normal/abnormal status of rotating machinery by transforming vibration data and extracting features. In the case of vibration data, it was collected from a rotating shaft alignment machine, and its normal/abnormal status was defined by the degree of axis alignment twist. Due to the characteristics of the vibration data, it is difficult to distinguish between two instances (normal and abnormal). It focuses on various data transformation techniques and feature extraction methods that explain the distinction between instances.In the phase of data preprocessing, the data is transformed to the frequency domain using Fast Fourier Transform and Wavelet Transform. The data then utilized Auto-encoding for feature extraction and MLP and CNN as models for predicting whether a machine is normal or abnormal. Consequently, this paper compares and evaluates the results of each process utilizing two data transformation techniques, one feature extraction method, and two models. Finally, it evaluates the performance of the model by using accuracy for the classification of rotational machine failure.