Purpose: The purpose of this paper is to present the knowledge structure based on the articles published in Library Hi Tech. The research hotspots are expected to be revealed through the keyword co-occurrence and social network analysis. Design/methodology/approach: Data sets based on publications from Library Hi Tech covering the time period from 2006 to 2017 were extracted from Web of Science and developed as testbeds for evaluation of the CiteSpace system. Highly cited keywords were analyzed by CiteSpace which supports visual exploration with knowledge discovery in bibliographic databases. Findings: The findings suggested that the percentage of publications in the USA, Germany, China, and Canada are high. Further, the most popular keywords identified in Library Hi Tech were: “service,” “technology,” “digital library,” “university library,” and “academic library.” Finally, four research issues were identified based on the most-cited articles in Library Hi Tech. Originality/value: While keyword plays an important role in scientific research, limited studies paid attention to the keyword analysis in librarian research. The contribution of this study is to systematically explore the knowledge structure constructed by the keywords in Library Hi Tech.

The risk of insufficient petroleum resources has forced human beings to emphasize the acquisition and storage of energy. To avoid such situation, this study tends to explore the effective management of lead‐acid batteries for effective utilization conforming to the industrial requirements.

Ensuring the authenticity and quality of food products is a critical challenge in food science and technology, particularly when compositional differences are subtle and not visually distinguishable. Fourier-transform infrared (FTIR) spectroscopy provides a promising analytical tool for food authentication, yet practical spectra often exhibit baseline drift, scattering effects, and overlapping absorbance bands that complicate reliable discrimination. As a result, these nonlinear and overlapping spectral signals can limit the performance of traditional chemometric approaches such as principal component analysis and partial least squares regression, which are widely used for dimensionality reduction and regression but often fail to generalize to nonlinear spectral data. To address these limitations, this study introduces a framework that integrates sliding-window embeddings with topological data analysis (TDA) to capture local, meso-scale, and global spectral structures, followed by bagging-based ensemble learning for robust prediction using transparent base learners. Six food-related FTIR benchmark datasets from the UCR Time Series Classification Archive were comprehensively evaluated under the official fixed train–test splits, without additional dataset-specific preprocessing. Results show that the proposed method demonstrates consistently robust performance relative to 20 widely used time-series classifiers across distance-based, feature-based, ensemble, and deep learning families under the standardized UCR benchmark setting, achieving high classification accuracy and high Macro-F1 across diverse food categories, based on reported results in the literature. The analysis further illustrates how multi-scale topological descriptors enhance interpretability and provide insights into spectral variability. Overall, the framework balances transparency, robustness, and computational efficiency, providing a reproducible benchmark-based reference for subsequent studies that evaluate measurement-specific effects under controlled acquisition settings.

This study examines the semantic dynamics and thematic shifts in artificial intelligence (AI) ethics over time, addressing a notable gap in longitudinal research within the field. In light of the rapid evolution of AI technologies and their associated ethical risks and societal impacts, the research integrates the theory of chance discovery with the KeyGraph algorithm to conduct topic detection through a keyword network built through iterative semantic exploration. ChatGPT is employed for semantic interpretation, enhancing both the accuracy and comprehensiveness of the detected topics. Guided by the double helix model of human–AI interaction, the framework incorporates a dual-layer validation process that combines cross-model semantic similarity analysis with expert-informed quality checks. An analysis of 24 authoritative AI ethics reports published between 2022 and 2024 reveals a consistent trend toward semantic stability, with high cross-model similarity across years (2022: 0.808 ± 0.023; 2023: 0.812 ± 0.013; 2024: 0.828 ± 0.015). Statistical tests confirm significant differences between single-cluster and multi-cluster topic structures (p < 0.05). The thematic findings indicate a shift in AI ethics discourse from a primary emphasis on technical risks to broader concerns involving institutional governance, societal trust, and the regulation of generative AI. Core keywords, such as bias, privacy, and ethics, recur across all years, reflecting the consolidation of an integrated governance framework that encompasses technological robustness, institutional adaptability, and social consensus. This dynamic semantic analysis framework contributes empirically to AI ethics governance and offers actionable insights for researchers and interdisciplinary stakeholders.