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Selected Papers on Sustainability from IMETI 2022
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Selected Papers on Sustainability from IMETI 2022

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3925

Special Issue Editors

Prof. Dr. Wen-Hsiang Hsieh

E-Mail Website
Guest Editor
Department of Automation Engineering, National Formosa University, Huwei, Yunlin 632, Taiwan
Interests: innovative applications of mechanisms; mechatronics; automatic machinery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jia-Shing Sheu

E-Mail Website
Guest Editor
Department of Computer Science, National Taipei University of Education, Taipei, Taiwan
Interests: signal processing; pattern recognition and image processing; real time face recognition; embedded system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 11th International Multi-Conference on Engineering and Technology Innovation (IMETI 2022) will be held in Kaohsiung, Taiwan, from 28 October to 1 November 2022. It aims to bring together engineering technology expertise. Professionals from industry, academia, and government with interests in the discourse regarding research and development, professional practice, and business and management in the science and engineering fields are welcome to attend the event. IMETI 2022 consists of four sub-conferences (ICATI 2022, ICBEI 2022, ICSI2022, and ICECEI 2022) and more than 30 regular and special sessions (http://imeti.org/IMETI2022/).

The main goal of this Special Issue, “Selected Papers from IMETII 2022”, is to present the latest advancements in the original research and novel applications of sustainability science and technologies. Potential topics include, but are not limited to:

  • Air/water/noise pollution;
  • Carbon footprint;
  • Physical and chemical treatment processes for pollution control;
  • Green materials for sustainability;
  • Sustainability planning and management;
  • Energy harvesting;
  • Power management;
  • Green logistics;
  • Environment monitoring;
  • Water and wastewater treatment;
  • Global climate change on sustainability;
  • Measuring and monitoring sustainability;
  • Sustainability science and applications;
  • Sustainable buildings and infrastructure;
  • Sustainable chemistry;
  • Population explosion and urbanization;
  • Education and awareness of sustainability;
  • Renewable sources of energy;
  • Life cycle management;
  • Health-related aspects of sustainability;
  • Sustainability policies and laws;
  • Other sustainability-related topics.

Prof. Dr. Wen-Hsiang Hsieh
Prof. Dr. Jia-Shing Sheu
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. Sustainability 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 2400 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

  • sustainability
  • pollution
  • renewable energy
  • ICECEI
  • IMETI
  • ICSI
  • ICATI

Published Papers (4 papers)

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Research

22 pages, 2619 KiB  
Article
Innovative Approaches to Sustainable Computer Numeric Control Machining: A Machine Learning Perspective on Energy Efficiency
by Indrawan Nugrahanto, Hariyanto Gunawan and Hsing-Yu Chen
Sustainability 2024, 16(9), 3569; https://0-doi-org.brum.beds.ac.uk/10.3390/su16093569 - 24 Apr 2024
Viewed by 283
Abstract
Computer Numeric Control (CNC) five-axis milling plays a significant role in the machining of precision molds and dies, aerospace parts, consumer electronics, etc. This research aims to explore the potential of the machine learning (ML) technique in improving energy efficiency during the CNC [...] Read more.
Computer Numeric Control (CNC) five-axis milling plays a significant role in the machining of precision molds and dies, aerospace parts, consumer electronics, etc. This research aims to explore the potential of the machine learning (ML) technique in improving energy efficiency during the CNC five-axis milling process for sustainable manufacturing. The experiments with various machining parameters, forms of toolpath planning, and dry cutting conditions were carried out, and the data regarding energy consumption were collected simultaneously. The relationship between machine parameters and energy consumption was analyzed and built. Subsequently, a machine learning algorithm was developed to classify test methods and identify energy-efficient machining strategies. The developed algorithm was implemented and assessed using different classification methods based on the ML concept to effectively reduce energy consumption. The results show that the Decision Tree and Random Forest algorithms produced lower Root Mean Square Error (RMSE) values of 4.24 and 4.28, respectively, compared to Linear, Lasso, and Ridge Regression algorithms. Verification experiments were conducted to ascertain the real-world applicability and performance of the ML-based energy efficiency approach in an operational CNC five-axis milling machine. The findings not only underscore the potential of ML techniques in optimizing energy efficiency but also offer a compelling pathway towards enhanced sustainability in CNC machining operations. The developed algorithm was implemented within a simulation framework and the algorithm was rigorously assessed using machine learning analysis to effectively reduce energy consumption, all while ensuring the accuracy of the machining results and integrating both conventional and advanced regression algorithms into CNC machining processes. Manufacturers stand to realize substantial energy savings and bolster sustainability initiatives, thus exemplifying the transformative power of ML-driven optimization strategies. Full article
(This article belongs to the Special Issue Selected Papers on Sustainability from IMETI 2022)
16 pages, 5073 KiB  
Article
Comprehensive Safety Index for Road Safety Management System
by Ki-Han Song, Kyung Hyun Kim, Solsaem Choi, Sabeur Elkosantini, Seongkwan Mark Lee and Wonho Suh
Sustainability 2024, 16(1), 450; https://0-doi-org.brum.beds.ac.uk/10.3390/su16010450 - 04 Jan 2024
Cited by 1 | Viewed by 1058
Abstract
A safety-index-based road safety management system (RSMS) is a tool to help identify locations where safety intervention is needed. To date, various safety indices have been developed and utilized, but it is rare to consider the plan–do–check–act structure in an RSMS when studying [...] Read more.
A safety-index-based road safety management system (RSMS) is a tool to help identify locations where safety intervention is needed. To date, various safety indices have been developed and utilized, but it is rare to consider the plan–do–check–act structure in an RSMS when studying the decision-making methodology. In this study, 36 indicators and a system of evaluation indicators were selected based on the major classifications of performance, effect, and improvement. Performance was categorized by safety system components and effect was reflected in the safety status, such as the number of injuries. The indicators were validated, and a classification methodology for safety groups was proposed through cluster analysis. It was found that there was no correlation between the indicators and the population, budget, or road area by administrative district. It was also found that no particular indicators had a significant impact on the overall result in the major category or the overall index. It was determined that the developed indicators were suitable for administrative district-specific safety monitoring. It is expected that these indicators will be continuously utilized and enhanced in the national evaluation of road traffic safety. Full article
(This article belongs to the Special Issue Selected Papers on Sustainability from IMETI 2022)
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12 pages, 2538 KiB  
Article
Determining Factors Influencing Short-Term International Aviation Traffic Demand Using SHAP Analysis: Before COVID-19 and Now
by Ki-Han Song, Solsaem Choi, Sabeur Elkosantini and Wonho Suh
Sustainability 2023, 15(20), 14924; https://0-doi-org.brum.beds.ac.uk/10.3390/su152014924 - 16 Oct 2023
Viewed by 757
Abstract
Due to the COVID-19 outbreak, international aviation travel has declined globally to the level it was 30 years ago. Influencing factors are explored to understand the difference in short-term international aviation travel demand before and after the COVID-19 pandemic. SHapley Additive exPlanations (SHAP), [...] Read more.
Due to the COVID-19 outbreak, international aviation travel has declined globally to the level it was 30 years ago. Influencing factors are explored to understand the difference in short-term international aviation travel demand before and after the COVID-19 pandemic. SHapley Additive exPlanations (SHAP), an exploratory data analysis methodology, is applied to identify the factors affecting aviation demand. Daily international aviation passenger volume data (1462 in total) between 2018 and 2021 are analyzed with 10 socioeconomic variables and the number of daily confirmed COVID-19 cases in Korea. It was found that the number of confirmed cases did not have the greatest direct influence on the short-term demand for international demand, but it has a strong correlation with socioeconomic factors. This study’s findings on the factors influencing short-term international air passenger demand from a macro perspective will contribute to demand forecasting after COVID-19. It is expected that this research can be applied to other countries or other pandemic data to investigate the post-pandemic demand changes. Full article
(This article belongs to the Special Issue Selected Papers on Sustainability from IMETI 2022)
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14 pages, 2875 KiB  
Article
Road-Section-Based Analysis of Vehicle Emissions and Energy Consumption
by Sunhee Jang, Ki-Han Song, Daejin Kim, Joonho Ko, Seongkwan Mark Lee, Sabeur Elkosantini and Wonho Suh
Sustainability 2023, 15(5), 4421; https://0-doi-org.brum.beds.ac.uk/10.3390/su15054421 - 01 Mar 2023
Cited by 2 | Viewed by 1384
Abstract
To monitor air pollution on roads in urban areas, it is necessary to accurately estimate emissions from vehicles. For this purpose, vehicle emission estimation models have been developed. Vehicle emission estimation models are categorized into macroscopic models and microscopic models. While the calculation [...] Read more.
To monitor air pollution on roads in urban areas, it is necessary to accurately estimate emissions from vehicles. For this purpose, vehicle emission estimation models have been developed. Vehicle emission estimation models are categorized into macroscopic models and microscopic models. While the calculation is simple, macroscopic models utilize the average speed of vehicles without accounting for the acceleration and deceleration of individual vehicles. Therefore, limitations exist in estimating accurate emissions when there are frequent changes in driving behavior. Microscopic emission estimation models overcome these limitations by utilizing the trajectory data of each vehicle. In this method, the total emissions in a road segment are calculated by adding together the emissions from individual vehicles. However, most research studies consider the total vehicle emissions in a road section without considering the difference in vehicle emissions at different locations of a selected road section. In this study, a road segment between two intersections was divided into sub-sections, and energy consumption and emission generation were analyzed. Since there are unique driving behaviors depending on the section of the road segment, energy consumption and emission generation patterns were identified. The findings of this study are expected to provide more detailed and quantitative data for better modeling of energy consumption and emissions in urban areas. Full article
(This article belongs to the Special Issue Selected Papers on Sustainability from IMETI 2022)
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