Application of Mathematical Theory in Data Science

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D: Statistics and Operational Research".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 241

Special Issue Editors


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Guest Editor
Faculty of Data Science, City University of Macau, Taipa, Macao
Interests: data science; privacy preserving; game theory

E-Mail Website
Guest Editor
Faculty of Data Science, City University of Macau, Taipa, Macao
Interests: blockchain privacy; cyber security; AI security; data mining and visualization

Special Issue Information

Dear Colleagues,

The rapid development of data science has led to an increasing need for robust mathematical frameworks to tackle the complex challenges posed by large-scale data analysis. This Special Issue aims to explore how mathematical theories can be applied to enhance various aspects of data science, including machine learning, statistical modeling, data mining, and artificial intelligence (AI). We seek contributions that demonstrate how mathematical approaches can improve key processes such as data preprocessing, feature extraction, model development, and performance evaluation.

The Special Issue will also explore the role of mathematics in ensuring the security, privacy, and robustness of data-driven systems. As AI models and data applications are increasingly deployed in sensitive areas, ensuring the integrity and reliability of these systems is crucial. Contributions that address the mathematical foundations behind secure data processing, model transparency, and the mitigation of adversarial risks are highly encouraged.

By bringing together insights from mathematics and data science, this Special Issue will provide a platform for advancing the state of the art in developing more accurate, efficient, and secure data-driven technologies. We invite researchers to share innovative solutions that tackle real-world challenges in both academic and industrial settings.

Dr. Lefeng Zhang
Dr. Minfeng Qi
Guest Editors

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Keywords

  • big data analytics
  • computational statistics
  • statistical data analysis
  • data science
  • statistical learning
  • advanced data analytics
  • data preprocessing
  • feature extraction
  • data-driven systems

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Published Papers (1 paper)

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Research

18 pages, 4489 KiB  
Article
Influence of Regional PM2.5 Sources on Air Quality: A Network-Based Spatiotemporal Analysis in Northern Thailand
by Khuanchanok Chaichana, Supanut Chaidee, Sayan Panma, Nattakorn Sukantamala, Neda Peyrone and Anchalee Khemphet
Mathematics 2025, 13(15), 2468; https://doi.org/10.3390/math13152468 - 31 Jul 2025
Viewed by 40
Abstract
Northern Thailand frequently suffers from severe PM2.5 air pollution, especially during the dry season, due to agricultural burning, local emissions, and transboundary haze. Understanding how pollution moves across regions and identifying source–receptor relationships are critical for effective air quality management. This study investigated [...] Read more.
Northern Thailand frequently suffers from severe PM2.5 air pollution, especially during the dry season, due to agricultural burning, local emissions, and transboundary haze. Understanding how pollution moves across regions and identifying source–receptor relationships are critical for effective air quality management. This study investigated the spatial and temporal dynamics of PM2.5 in northern Thailand. Specifically, it explored how pollution at one monitoring station influenced concentrations at others and revealed the seasonal structure of PM2.5 transmission using network-based analysis. We developed a Python-based framework to analyze daily PM2.5 data from 2022 to 2023, selecting nine representative stations across eight provinces based on spatial clustering and shape-based criteria. Delaunay triangulation was used to define spatial connections among stations, capturing the region’s irregular geography. Cross-correlation and Granger causality were applied to identify time-lagged relationships between stations for each season. Trophic coherence analysis was used to evaluate the hierarchical structure and seasonal stability of the resulting networks. The analysis revealed seasonal patterns of PM2.5 transmission, with certain stations, particularly in Chiang Mai and Lampang, consistently acting as source nodes. Provinces such as Phayao and Phrae were frequently identified as receptors, especially during the winter and rainy seasons. Trophic coherence varied by season, with the winter network showing the highest coherence, indicating a more hierarchical but less stable structure. The rainy season exhibited the lowest coherence, reflecting greater structural stability. PM2.5 spreads through structured, seasonal pathways in northern Thailand. Network patterns vary significantly across seasons, highlighting the need for adaptive air quality strategies. This framework can help identify influential monitoring stations for early warning and support more targeted, season-specific air quality management strategies in northern Thailand. Full article
(This article belongs to the Special Issue Application of Mathematical Theory in Data Science)
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