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Mathematics
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Advances and Applications in Intelligent Computing
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Advances and Applications in Intelligent Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 548

Special Issue Editors

Dr. Zhiqing Tang

E-Mail Website
Guest Editor
Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai 519087, China
Interests: edge intelligence; artificial intelligence; Internet of Things
Dr. Jiong Lou

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: edge AI
Dr. Jianxiong Guo

E-Mail Website
Guest Editor
Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai 519087, China
Interests: edge intelligence; online & federated learning; IoT; combinatorial optimization

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, “Advances and Applications in Intelligent Computing”, through submitting original research articles or comprehensive reviews that advance theoretical, methodological, or application-driven developments in the expanding field of intelligent computing.

The rapid evolution of artificial intelligence and machine learning, powered by advances in computational resources and mathematical modeling, is transforming a multitude of domains. This Special Issue emphasizes recent progresses in artificial intelligence algorithms, deep learning and reinforcement learning techniques, mathematical modeling, and optimization algorithms for intelligent systems. Contributions addressing novel machine learning algorithms and systems—including those leveraging cloud, edge, or distributed architectures—are particularly encouraged.

We especially welcome submissions exploring intelligent control and decision-making, as well as innovative applications in areas such as smart cities, education, and industrial systems. Studies that bridge theoretical advances and their practical deployment or that introduce new algorithms, frameworks, or computational tools to enhance intelligent applications are highly valued.

Your contributions will play a crucial role in highlighting emerging trends and future directions in intelligent computing and its impact on complex, real-world challenges.

Dr. Zhiqing Tang
Dr. Jiong Lou
Dr. Jianxiong Guo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence algorithms and applications
  • deep learning and reinforcement learning
  • mathematical modeling and optimization algorithms
  • machine learning algorithms and systems
  • cloud/edge/distributed AI and systems
  • intelligent control and decision-making
  • applications in smart cities, education, industry, etc.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

45 pages, 2671 KB  
Article
Mathematical Model for Economic Optimization of Tower-Type Solar Thermal Power Generation Systems via Coupled Monte Carlo Ray-Tracing and Multi-Mechanism Heat Loss Equations
by Juanen Li, Yao Chen and Huanhao Su
Mathematics 2025, 13(19), 3132; https://doi.org/10.3390/math13193132 - 30 Sep 2025
Viewed by 303
Abstract
With the global energy transition and decarbonization goals, tower-type solar thermal power generation is increasingly important for dispatchable clean energy due to its high efficiency, thermal storage capacity, and regulation performance. However, current research focuses on ideal conditions, ignoring real geographical constraints on [...] Read more.
With the global energy transition and decarbonization goals, tower-type solar thermal power generation is increasingly important for dispatchable clean energy due to its high efficiency, thermal storage capacity, and regulation performance. However, current research focuses on ideal conditions, ignoring real geographical constraints on heliostat layout and environmental impacts on receiver performance. More practical scene modeling and performance evaluation methods are urgently needed. To address these issues, we propose a heliostat field simulation algorithm based on heat loss mechanisms and real site characteristics. The algorithm includes optical performance evaluation (cosine efficiency, shading, truncation, atmospheric transmittance) and heat loss mechanisms (radiation, convection, conduction) for realistic net heat output estimation. Experiments revealed the following: (1) higher central towers improve optical efficiency by increasing solar elevation angle; (2) radiation losses dominate at high power and tower height, while convection losses dominate at low power and tower height. Using the Economic-Integrated Score (EIS) optimization algorithm, we achieved optimal tower and receiver configuration with 40.22% average improvement over other configurations (maximum 3.9× improvement). This provides a scientific design basis for improving tower-type solar thermal systems’ adaptability and economy in different geographical environments. Full article
(This article belongs to the Special Issue Advances and Applications in Intelligent Computing)
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