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 info@ewapublishing.org.

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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
  • We champion change: we're working to increase the percentage of women, early career ...

July 6, 2023

Advances in Engineering Innovation - Disclaimer


  • The statements, opinions and data contained in the journal Advances in Engineering Innovation (AEI) are solely those of the individual authors and contributors and not of the publisher and the editor(s). EWA Publishing stays neutral with regard to jurisdictional claims in published maps and ...
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    Latest Articles

    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.

    Open Access | Article

    With the ever-increasing volume of data being generated and shared across various platforms, the challenge of maintaining privacy while extracting value from this data has become paramount. This paper delves into the realm of Privacy-Preserving Data Analysis (PPDA), examining its current landscape and the pivotal techniques shaping it. Using datasets from diverse domains, we evaluated four leading PPDA techniques—Differential Privacy, Homomorphic Encryption, Secure Multi-Party Computation (SMPC), and Data Obfuscation—to discern their efficacy and trade-offs in terms of data utility and privacy breach risk. Our findings underscore the strengths and constraints of each method, guiding researchers and practitioners in choosing the optimal approach for specific scenarios. As data continues to be an invaluable asset in the digital age, the tools and techniques to analyze it privately will play a critical role in shaping future data-driven decision-making processes.

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