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Welcome to Allied Sciences and Engineering Journals

Allied Sciences and Engineering Journals (ASEJ) is an international, single-blind, editorial-reviewed bi-monthly online academic research journal publication dedicated to promoting the advancement of science and engineering. ASEJ encourages innovative ideas and research across all fields and publishes high-quality original papers, theory-based empirical studies, review articles, case reports, conference proceedings, technology reports, book reviews, commentaries, and news.

Scope and Areas of Interest ASEJ covers a wide range of disciplines, making it a truly multidisciplinary Sciences and Engineering platform. The journal invites submissions in the following areas: Engineering and Technology, Agriculture and Environmental Sciences and Health and Medicine Read Full Scope

Submission and Publication Authors are cordially invited to submit full-length, original, and unpublished research articles.

  • Submission Date: Open
  • Publication Date: Last day of each month

REVOLUTIONIZING PDE SOLUTIONS: ANNEALING ALGORITHM AND POLYNOMIAL REGRESSION INTEGRATION

Published by: Qiang Zhang , Lingyun Li

Pages: 1-11 |

This paper presents a novel approach for efficiently solving global solutions to partial differential equations (PDEs) using a combination of the Annealing Algorithm and Polynomial Regression tailored specifically for the Feynman-Kac formulation. By integrating the Annealing Algorithm with Polynomial Regression techniques, the proposed method offers enhanced accuracy and computational
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BEYOND TRADITIONAL APPROACHES: ENHANCING PDE SOLUTIONS WITH K-NEAREST NEIGHBOR APPROACH

Published by: Hui Wang , Lei Zhang

Pages: 12-23 |

This paper introduces a novel approach, utilizing a modified method based on the K-nearest neighbor approach, for solving global solutions to partial differential equations (PDEs) through the Feynman-Kac formula. By integrating the K-nearest neighbor approach with the Feynman-Kac formula, this method offers enhanced accuracy and efficiency in obtaining global solutions to PDEs across various
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PREDICTIVE ANALYTICS IN DEMOGRAPHY: BIRTH POPULATION FORECASTING WITH GRAY VERHULST MODEL

Published by: Mingwei Liu , Yuxuan Wang

Pages: 24-31 |

Forecasting birth population is integral to population studies, aiding policy formulation, development planning, resource allocation, economic growth, and social issue research. Accurate predictions serve as a foundation for sustainable development and population health enhancement. This paper addresses birth population prediction through mathematical modeling, a vital endeavor undertaken by
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