Grasping the latest knowledge in any field has become increasingly challenging given the rapid pace of modern publishing. Recent advances in artificial intelligence (AI), particularly embedding models and large language models (LLMs), provide promising tools to aid researchers in the filtration and identification of the most relevant literature. However, there is little practical guidance on how to incorporate LLMs into literature reviews, and there are not yet direct comparisons of embedding models versus generative approaches. In this work, we compare these methods using small local LLMs for literature screening on both a simple dataset containing distinct categories and a more complex example with abstracts obtained from the Web of Science. We find that embedding models perform strongly for simple, well-separated queries due to their robustness and computational efficiency. For more complex filtering tasks, generative approaches based on token probabilities can achieve competitive or superior performance, but only when properly calibrated and when token-level outputs are interpreted as continuous scores rather than binary decisions. However, we show these methods are highly sensitive to prompt formulation, token conventions, and threshold selection.
Artificial intelligence (AI) is increasingly applied to digital art creation; however, its adoption in religious cultural heritage remains insufficiently understood because artistic production involves cultural symbolism, spiritual meaning, and human creativity. This study investigates artists’ perceptions of culturally sensitive AI adoption in Thai Buddhist digital art using survey data from 182 experienced digital Buddhist art designers in Thailand. An exploratory quantitative approach combining Principal Component Analysis (PCA) with Varimax rotation and multiple linear regression analysis was employed. PCA identified two empirically derived perceptual components: Perceived AI Capability and Perceived AI Benefits. Regression analysis revealed that Perceived AI Benefits was positively associated with artists’ evaluations of (a) Expectations for AI in Creating Buddhist Art, (b) Role of AI in Creating Buddhist Art, and (c) Technical Capability and Autonomous Functions of AI, whereas Perceived AI Capability was not significantly associated with any of these outcomes after controlling for perceived benefits. The findings indicate that perceived AI benefits are significantly associated with artists’ expectations and evaluations of AI in Thai Buddhist digital art. The findings also highlight broader considerations regarding cultural values, ethical responsibility, and human creative agency, providing a basis for human-centered and culturally sensitive AI applications in religious cultural heritage.
Combining large language models (LLMs) with structured domain knowledge representation (knowledge graph) and software agents appears to have potential for supporting domain-specific, open-ended situation awareness and scenario building. Inherent to these processes is that there is a complex and dynamic phenomenon under investigation, required input data is gathered from both structured and unstructured sources with varying confidence and quality levels, and process execution requires both human-human and human-AI collaboration. In this paper, we present a design, implementation, and partial validation for a data processing pipeline consisting of an LLM-based AI implementation and a collection of software agents, applied to a process of military intelligence. Our contributions are: 1) survey and eight-phase guidance of knowledge representation for LLMs, 2) survey and guidelines of trust in AI application adoption, and 3) implementation of agentic AI data processing pipeline for synthetic observation generation and processing. Our key findings indicate that through structured domain knowledge support, the foundation model is capable of generating outputs subject to domain expert evaluation, and that the utilization of software agents allows for experimenting with potential course-of-action (CoA) scenarios from a present situation. Future work requires more rigorous testing with domain experts to identify improvements needed in the data processing pipeline.
Accurate stratigraphic identification is essential for petroleum-development planning, but class imbalance and similar well-log responses between adjacent strata remain major challenges. This study uses well-log data from nine anonymized wells in the Ordos Basin, covering approximately 1,200–2,400 m and nine stratigraphic units from Yan 9 to Chang 10_1. Seven wells were used for training and hyperparameter optimization, while Wells H and I were reserved for blind evaluation. A two-stage framework was developed by decomposing the nine-class task into three-class and seven-class subtasks using an operational depth constraint within Yan 10_2. Savitzky–Golay filtering was applied, and XGBoost, CatBoost, LightGBM, Stacking, and Att-BiLSTM were evaluated. Three composite features were constructed before SHAP analysis based on petrophysical relationships and exploratory analysis of the training wells. SHAP was used for post-hoc interpretation rather than feature construction or selection. CatBoost achieved the strongest overall performance among the evaluated models, although no single model performed best for every stratum. With an adjacent-stratum boundary tolerance of approximately ±1 m, the framework achieved accuracies of 93.4% and 95.1% for Wells H and I, respectively, with macro-F1 scores of 0.867 and 0.835. These results demonstrate its potential for stratigraphic identification within the study area.
Federated learning in healthcare faces significant challenges under non-independent and identically distributed (non-IID) medical data, including communication inefficiency, unstable convergence, and privacy risks. To address these challenges, we propose MedSparseFL, a sparse and privacy-preserving federated learning framework for healthcare applications. On the client side, MedSparseFL introduces a gradient sparsification mechanism using a lightweight Gradient Score Network (GSN) to estimate the importance of gradient updates. High-contribution dimensions are preferentially selected through probabilistic sampling combined with residual feedback, thereby alleviating the adverse effects of non-IID data on global convergence. Before transmission, the sparse gradients are compressed using Count Sketch mapping and protected via sketch-domain obfuscation and pairwise masking to enhance privacy. Additive homomorphic encryption (AHE) is further applied to the compressed sketches to reduce transmission leakage risks while maintaining computational efficiency. On the server side, global gradient estimates are obtained from aggregated sketches, and the inferred sparse support is exploited to suppress anomalous updates. Experiments on two public medical datasets show that MedSparseFL improves classification performance by 2%, reduces communication overhead, and lowers empirical privacy leakage risks under the evaluated attack settings, with privacy protection positioned at the update level under the stated threat model.
