Advances in Engineering Innovation
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Advances in Engineering Innovation (AEI) is a peer-reviewed, fast-indexing open access journal co-published by EWA Publishing and Tianjin University Research Centre on Data Intelligence and Cloud-Edge-Client Service Engineering. AEI is a comprehensive journal focusing on multidisciplinary areas of engineering and at the interface of related subjects, including, but not limited to, Artificial Intelligence, Biomedical Engineering, Electrical and Electronic Engineering, Materials Engineering, Traffic and Transportation Engineering, etc. For the details about the journal's scope, please refer to the Aims and Scope page. For more information about the journal, please refer to the FAQ page or contact

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July 21, 2023

Advances in Engineering Innovation - Gender and Diversity pledge

We pledge to our journal community:

  • We're committed: we put diversity and inclusion at the heart of our activities
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July 6, 2023

Advances in Engineering Innovation - Disclaimer

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    Latest Articles

    Open Access | Article

    The compilation and preprocessing of electromagnetic shielding material databases using machine learning techniques are pivotal in contemporary materials science and engineering. These materials play a crucial role in diverse applications such as electronics, telecommunications, aerospace, and automotive industries, necessitating effective attenuation of electromagnetic interference (EMI). This paper underscores the significance of comprehensive databases in organizing material properties systematically, facilitating the identification of novel materials with enhanced shielding effectiveness. It explores the role of machine learning algorithms in predictive modeling and data analysis, expediting the screening process of candidate materials and uncovering hidden correlations within complex datasets. However, the efficacy of machine learning techniques relies heavily on the quality of input data and preprocessing steps. Thus, this paper discusses methodologies for collecting material data, challenges in data curation and integration, and common preprocessing techniques such as data cleaning, feature extraction, and normalization. The integration of electromagnetic shielding material databases with machine learning preprocessing holds great promise for FeCo-based alloy laser cladding manufacturing and design, leading to innovative solutions for electromagnetic interference mitigation across various technological domains.

    Open Access | Article

    This paper addresses the issues of replenishment and pricing of vegetable commodities in fresh food supermarkets. By analyzing sales data and loss rate data, mathematical models are established to solve the following problems. Spearman correlation analysis and cluster analysis methods are employed. Firstly, data preprocessing is conducted by excluding returned vegetable products from a certain supermarket’s distribution, and then integrating sales transaction details and wholesale price-related data. Secondly, monthly data is used to succinctly present the sales distribution patterns of various categories and individual items of vegetables through line charts. Finally, the models of Spearman correlation analysis and cluster analysis are respectively applied to illustrate the relationship between the sales volume of 6 major categories of vegetables and 251 individual items. Linear regression models, time series analysis, and XG-Boost regression analysis models are used. This paper requires replenishment plans to be made by category, analyzing the relationship between the total sales volume and cost-plus pricing of various vegetable categories. Firstly, a linear regression model is applied to fit historical sales volume and cost-plus pricing, resulting in a linear functional relationship, indicating that only the sales volume of tomatoes is negatively correlated with cost-plus pricing. Based on historical sales data, the daily replenishment total for each vegetable category within the next week is predicted. Predictive models and time series analysis are employed. Meanwhile, for pricing strategies, the XGBoost algorithm is utilized to provide reasonable pricing strategies. Based on the optimal solution, replenishment plans and pricing strategies for the next week are formulated to ensure the maximization of sales revenue objectives.

    Open Access | Article

    In order to address the issues of predefined adjacency matrices inadequately representing information in road networks, insufficiently capturing spatial dependencies of traffic networks, and the potential problem of excessive smoothing or neglecting initial node information as the layers of graph convolutional neural networks increase, thus affecting traffic prediction performance, this paper proposes a prediction model based on Adaptive Multi-channel Graph Convolutional Neural Networks (AMGCN). The model utilizes an adaptive adjacency matrix to automatically learn implicit graph structures from data, introduces a mixed skip propagation graph convolutional neural network model, which retains the original node states and selectively acquires outputs of convolutional layers, thus avoiding the loss of node initial states and comprehensively capturing spatial correlations of traffic flow. Finally, the output is fed into Long Short-Term Memory networks to capture temporal correlations. Comparative experiments on two real datasets validate the effectiveness of the proposed model.

    Open Access | Article

    The evolution of software architecture has witnessed the transition from monolithic to microservices, offering enhanced scalability, maintainability, and flexibility. With the rise of Microservices Architecture (MA), containerization has emerged as a pivotal technology to encapsulate microservices in isolated environments, ensuring consistent deployment. This paper delves into the intricate relationship between Microservices Architecture and containerization, focusing on the benefits, challenges, and practical implications of integrating both. Through a comprehensive experimental setup simulating an e-commerce platform, we quantitatively evaluate the performance metrics of a containerized microservices system versus a traditional monolithic setup. Our findings accentuate the performance gains achieved through MA and containerization, while also shedding light on areas that demand caution and further research. The insights presented serve as a beacon for organizations aiming to transition to or optimize their microservices and containerization practices.

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