Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences

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The proceedings series Applied and Computational Engineering (ACE) is an international peer-reviewed open access series that publishes conference proceedings from various methodological and disciplinary perspectives concerning engineering and technology. The series contributes to the development of computing sectors by providing an open platform for sharing and discussion. The series publishes articles that are research-oriented and welcomes theoretical and applicational studies. Proceedings that are suitable for publication in the ACE cover domains on various perspectives of computing and engineering.

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December 21, 2022

Applied and Computational Engineering - Gender and Diversity pledge

We pledge to our series community:

  • We're committed: we put diversity and inclusion at the heart of our activities
  • We champion change: we're working to increase the percentage of women, early career ...

December 6, 2021

Applied and Computational Engineering - Disclaimer

  • The statements, opinions and data contained in the series Applied and Computational Engineering (ACE) are solely those of the individual authors and contributors and not of the publisher and the editor(s). Applied and Computational Engineering stays neutral with regard to jurisdictional claims in published maps and ...
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  • February 22, 2023, Good News! Welcome Dr. Marwan Omar from Illinois Institute of Technology to give a speech at CONF-CDS 2023!
  • February 21, 2023, Good News! Welcome Dr. Roman Bauer from University of Surrey to give a speech at CONF-CDS 2023!
  • January 18, 2023: Prof. Festus Adedoyin from Bournemouth University was invited to deliver a keynote speech at CONF-MSS 2023.
  • February 15, 2023: Good News! Welcome Dr. Ioannis Spanopoulos from University of South Florida to give a speech at CONF-MCEE 2023!
  • February 14, 2023, Good News! Welcome Dr. Roman Bauer from University of Surrey to give a speech at CONF-SPML 2024!
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    Latest articles

    Open Access | Article

    This paper embarks on a detailed exploration of the integration of glass as a pivotal architectural element within museum structures, with a specific focus on the renowned Kroller Muller Museum as a prominent case study. The Kroller Muller Museum stands as a beacon of a transformative shift in museum architecture, boldly veering away from conventional reinforced concrete edifices to embrace a contemporary architectural approach predominantly anchored in glass. The deliberate use of glass in its design symbolizes an alignment with the tenets of modernity, reflecting the museum’s evolution in harmony with societal progress and contemporary architectural trends. In line with the philosophy of renowned architect Le Corbusier, who emphasized the necessity of architectural adaptation to cater to evolving societal needs, this study argues that maintaining synchrony with the contemporary era is vital not just for museums but also for the broader realm of architecture and urban planning. Drawing from these principles, this research critically scrutinizes whether the prevalent shift towards transparency, particularly in the form of glass integration, truly aligns with the specific and nuanced requirements of museums within their contextual milieu. By thoroughly analyzing this aspect, the research is to offer beneficial perspectives on the efficiency and suitability of incorporating glass elements in modern museum architecture.

    Open Access | Article

    Currently, cities in China have entered the development stage of stock renewal. Urban renewal has become the focus of urban development, and optimization of stock space has become the main aspect of urban design. At the same time, applying the concept of “micro-renewal” of historic blocks in recent years has also put forward new requirements for the renewal mode of old blocks. This paper discusses the possibility of micro-renewal and transformation of historic Chinese blocks in the new era from the perspective of catalyst theory. Through streetscape recognition technology and crawling of Point of Information (POI) data, the existing problems of blocks and the potential vitality points of blocks are determined, which changes the traditional research that often lacks quantitative judgment and only uses qualitative judgment. Through the intervention of quantitative technology, the problems existing in the perspective rate of commercial streets and the business model of historical blocks were found. Analyzing from the perspective of space, function, and culture, and then, selecting catalyst elements, which promotes the transformation of the historic blocks from a “top-down” transformation pattern to a “bottom-up” transformation pattern.

    Open Access | Article

    With the digital transformation of the logistics industry, smart logistics algorithms have become a core technology to improve efficiency and reduce costs. This paper reviews the development history of traditional logistics technology and discusses the key role of technologies such as the Internet of Things, big data analysis, artificial intelligence, and automation in logistics technology innovation. It focuses on the application of intelligent logistics algorithms in path optimization, intelligent scheduling, data mining and prediction, and intelligent warehousing. To solve the problem of inconsistency between training and testing objectives, this paper proposes DRL4Route, a deep reinforcement learning-based path optimization framework, and designs the DRL4Route-GAE model. These research results provide important support to further promote the intelligent development of the logistics industry.

    Open Access | Article

    This paper explores the integration of large language models (LLMs) into collaborative filtering algorithms to enhance recommendation systems in the e-commerce domain. The proposed approach combines user-based and item-based collaborative filtering with LLMs to improve recommendation accuracy and personalization. Specifically, the study introduces a novel framework called PALR, which leverages LLMs to refine user-item interactions and enrich item representations. PALR utilizes historical user behavior data, such as clicks, purchases, and ratings, to guide candidate retrieval and generate recommended items. This study highlights the importance of integrating LLMs into recommendation systems to deliver more accurate and personalized suggestions, ultimately improving user satisfaction and driving sales in e-commerce platforms.

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