Yijun Pan

Molecular inventories are essential for exploring chemical networks and physical conditions, yet their systematic analysis is often hindered by severe line confusion and heterogeneous excitation conditions in spectral surveys. In this paper, we propose a graph-based framework for molecular inventory analysis in Sgr B2(N) that integrates six complementary descriptors into a heterogeneous molecular graph, including source size, rotational temperature, line width, velocity offset, vibrational state, and a Self-Referencing Embedded Strings-based molecular structure representation. Molecular embeddings are learned via multiscale, metapath-guided random walks that jointly encode chemical structure and astrophysical co-occurrence, enabling relationally consistent inference of molecular column densities across 20 subregions of Sgr B2(N) with R ² values ranging from 0.64–0.91. Furthermore, we introduce an interpretability framework combining attribution analysis, prototype retrieval, contrastive molecular analysis, and an embedding-based abundance-informed prioritization metric, showing that the learned embedding preserves molecular structural similarity while capturing environmental drivers of abundance variation. By unifying prediction, interpretation, and abundance-informed prioritization within a single pipeline, this work provides a scalable, data-driven approach to astrochemical inventory analysis in spectral surveys.

To address the high computational cost associated with molecular identification in spectral lines, we propose a graph-based feature learning framework to determine the molecular composition of spectral lines that incorporates rule-driven and node-level constraints to enhance identification accuracy. The approach begins with a sliding-window technique to extract localized spectral features that are then represented as nodes within a heterogeneous graph. To maintain semantic consistency across chemically diverse molecular species, rule-based constraints are introduced. Additionally, node-level constraints are employed to prevent the random walk process from becoming trapped in local cycles. The random walk mechanism is further refined through the integration of 2 parameters that guide node selection. Specifically, we design a frequency-aware sampling strategy in which transition probabilities are dynamically updated according to the number of times each node is used, combined with a stochastic modulation factor to enhance exploration and reduce sampling bias. Furthermore, a dynamic rule selection mechanism is employed to adaptively guide the random walk process in response to the evolving topological structure of the graph. Finally, the effectiveness of the proposed algorithm for molecular identification is validated using simulated data, achieving a recall rate of 94.63%. The method is then applied to the real G327 region, where molecular spectral lines are first identified and then used as inputs for XCLASS fitting to further verify the performance of the proposed algorithm. Moreover, the spatial map of molecular counts within spectral cubes is generated, demonstrating the capability for detailed chemical structure analysis.

Since a large amount of data can be obtained in the oil production process nowadays and the operation environment is increasingly complicated, it is necessary to research unsupervised and robust fault detection methods for improving safety. In this paper, an online Bayesian-based technique with a novel decision fusion algorithm is proposed for unsupervised fault detection in the pumping unit. First, a new strategy to detect the working condition of the pumping unit by dynamometer card as well as five process measured variables is proposed. To deal with high-dimension data and outliers in dynamometer card, a robust Douglas-Peucker algorithm is developed for obtaining compressed data. A chord ratio index evaluating deviation degree of observations is defined, which can be used for removing outliers during approximation. Two norms are introduced for choosing the threshold in the proposed Douglas-Peucker algorithm. Moreover, a Bayesian-based online change point detection model is attempted for detecting univariate faults in the pumping unit. A decision fusion method derived from Bayesian probability formula is proposed for fusing univariate fault detection results. At last, the power of the proposed method is evaluated by numerical simulations and a real oil production process.