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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 20933

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Prof. Dr. Peng-Yeng Yin

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Guest Editor

Information Technology and Management Program, Ming Chuan University, Taoyuan City 333, Taiwan
Interests: artificial intelligence; evolutionary computation; wind and solar energy; metaheuristics; pattern recognition; image processing; machine learning; software engineering; computational intelligence; operations research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable development is the core value of human society to create a better world for living. The rapid industrialization and urbanization has significantly changed human activities and left great impacts to the natural environment. Major populations are densely resorting to metropolitan cities, people are trailing on the streets by cars, industry production emits high volume of pollutants into air and rivers. These common everyday activities spoil our environment, contaminate the farms, change the mountain landscape and forest ecosystem. All these have exceeded the capacity of our mother earth. For the past decade, artificial intelligence (AI) has made a great progress in processing the data intelligently and efficiently. The applications of AI, especially deep learning, seep into every piece of our lives, from unmanned aerial vehicle, autonomous driving, resource-constrained production, humanoids and unmanned factory, to green logistics, circular economy, technology agriculture, air quality forecasting, personal digital assistant, and playing chess. We anticipate a next big thing by exploring the possible crossovers between sustainability and AI. The purpose of the special issue is to call attention to the marriage between “sustainability” and “artificial intelligence” and to boost the synergy between the two streams. The publication of our special issue will position itself at the research frontier for promoting sustainability by applying artificial intelligence algorithms. To augment the readership of this special issue, we work in collaboration with the Information Technology and Applications Conference 2022 - ITAC 2022 (https://itac2022.gm.cute.edu.tw/english) to be held at China University of Technology, Taipei, Taiwan on March 16, 2022. The best papers selected from this conference will be recommended to submit an extended version for possible publication in this special issue. We also welcome contributions (research and review articles) covering a broad range of topics on sustainability and artificial intelligence, including (though not limited to) the following:

Artificial intelligence
Internet of things
Industry 4.0
Knowledge management
Human-Computer Interaction
Image processing
Mobile computing
Sustainable development
Network management
Big data
E-commerce
Data mining
Wisdom of life
Cloud computing
Information security
Applied industrial technology
Renewable energy
Fintech
sustainability and AI
sustainability and IoT
sustainability and information security
sustainability and network management
sustainability and data mining, machine learning, and artificial intelligence
sustainability and heuristic and metaheuristic
sustainability and E-commerce
sustainability and Human-Computer Interaction

Prof. Dr. Peng-Yeng Yin
Guest Editor

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
artificial intelligence
unmanned factory
green logistics
resource-constrained production
circular economy
technology agriculture
air quality forecasting
security

Published Papers (6 papers)

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Research

12 pages, 3938 KiB

Open AccessArticle

Concentration-Temporal Multilevel Calibration of Low-Cost PM_2.5 Sensors

by Rong-Fuh Day, Peng-Yeng Yin, Yuh-Chin T. Huang, Cheng-Yi Wang, Chih-Chun Tsai and Cheng-Hsien Yu

Sustainability 2022, 14(16), 10015; https://doi.org/10.3390/su141610015 - 12 Aug 2022

Cited by 1 | Viewed by 1076

Abstract

Ambient aerosols have a significant impact on plant species mortality, air pollution, and climate change. It is critical to monitor the concentrations of aerosols, especially particulate matter with an aerodynamic diameter ≤ 2.5 μm (PM_2.5), which has a direct relationship with human respiratory diseases. Recently, low-cost PM_2.5 sensors have been deployed to provide a denser monitoring coverage than that of government-built monitoring supersites, which only give a macro perspective of air quality. To increase the measurement accuracy, low-cost sensors need to be calibrated. In current practice, regression techniques are used to calibrate sensors. This paper proposes a concentration-temporal multilevel calibration method to cope with the varying regression relation in different concentration and temporal domains. The performance of our method is evaluated with real field data from a supersite sensor and a low-cost sensor deployed in Puli, Taiwan. The experimental results show that our calibration method significantly outperforms linear regression in terms of R², Root Mean Square Error, and Normalized Mean Error. Moreover, our method compares favorably with a machine learning calibration method based on gradient regression tree boosting. Full article

(This article belongs to the Special Issue Sustainability and Artificial Intelligence)

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17 pages, 1909 KiB

Open AccessArticle

The Effect of Artificial Intelligence on End-User Online Purchasing Decisions: Toward an Integrated Conceptual Framework

by Hasan Beyari and Hatem Garamoun

Sustainability 2022, 14(15), 9637; https://doi.org/10.3390/su14159637 - 5 Aug 2022

Cited by 8 | Viewed by 8509

Abstract

This study was an investigation into the effect of selected artificial intelligence tools and the consideration set on the end-user purchasing intentions of convenient and shopping products of Saudi Arabian customers. The consideration set was the factor that the researcher sought to associate directly with the online purchasing intention variable. The selected AI tools and approaches were machine learning, purchase duration, social product recommendation, and social media dependency. The four served as the indirect factors, as their effect was measured against the consideration set variable. The theoretical framework employed in this study comprised the unified theory of acceptance and use of technology (UTAUT) and the theory of reasoned action. The researchers used an online survey with a sample of 148 customers. In analyzing the findings, the researchers opted for the structural equation modeling (SEM) approach. The findings indicated evidence of association with a consideration set of three independent variables, namely, machine learning, purchase duration, and product recommendation. The study also established that customer consideration sets influence end-user purchase decisions for online customers. Full article

(This article belongs to the Special Issue Sustainability and Artificial Intelligence)

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22 pages, 2675 KiB

Open AccessArticle

An Empirical Study for Senior Citizens Using a Customized Medical Informatics System for Dementia Diagnosis and Analysis

by Hsu-Hua Ho, Jien-Jou Lin, Jia-Qiao Gong and Tzu-Yi Yu

Sustainability 2022, 14(15), 9064; https://doi.org/10.3390/su14159064 - 24 Jul 2022

Cited by 1 | Viewed by 1491

Abstract

The treatment of dementia-related diseases is a global issue. Taiwan is facing a more serious dementia problem due to the combination of an aging society and a declining birthrate. A great portion of healthcare resources has been utilized for dementia among the aged population. In order to understand how dementia develops in rural areas in Taiwan, a cooperated effort between the university and a regional hospital was formed to develop a customized medical information system to collect and track dementia patients. This efficient customized system compiled information on 768 patients with dementia-released diseases. Big data technology and data mining approaches were then applied to analyze the relevant information. Using statistical analysis, we then extracted useful medical findings from the large amounts of collected medical data. Some of the findings indicate that the patients’ education level and care practices have a major effect on the dementia severity in these local senior populations. Full article

(This article belongs to the Special Issue Sustainability and Artificial Intelligence)

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18 pages, 2077 KiB

Open AccessArticle

A Framework of the Value Co-Creation Cycle in Platform Businesses: An Exploratory Case Study

by Feng-Shang Wu and Chia-Chang Tsai

Sustainability 2022, 14(9), 5612; https://doi.org/10.3390/su14095612 - 6 May 2022

Cited by 5 | Viewed by 4487

Abstract

Platform businesses, linking producers and consumers, have emerged as a very important industry. Meanwhile, value co-creation has become one of the critical issues concerning the operation of platform enterprises and the focus of researchers in this area. Platform businesses usually need to strengthen the interactions between all participants to maximize the commercial value. However, the majority of the literature has concentrated on the “platform business–consumer” interaction only, i.e., both “platform business–producer” and “platform producer–consumer” interactions have been almost completely neglected. Consequently, this study aims to fill the research gap by investigating “all-around interactions” and the relationships between the interaction with the value co-creation performance. A holistic framework of the value co-creation cycle is developed and validated. One of the largest platform businesses in Taiwan was examined, and Google Analytics (GA) code was embedded into its information system for data generation. The results confirmed the proposed framework and hypotheses. The study concludes that platform businesses need to gain insight into producers and consumers through data tracking and analysis as well as to provide innovative services that elevate satisfaction, user loyalty, and usage frequency, with a final goal of establishing a cycle of value co-creation. Full article

(This article belongs to the Special Issue Sustainability and Artificial Intelligence)

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18 pages, 2411 KiB

Open AccessArticle

Minimizing the Makespan in Flowshop Scheduling for Sustainable Rubber Circular Manufacturing

by Peng-Yeng Yin, Hsin-Min Chen, Yi-Lung Cheng, Ying-Chieh Wei, Ya-Lin Huang and Rong-Fuh Day

Sustainability 2021, 13(5), 2576; https://doi.org/10.3390/su13052576 - 28 Feb 2021

Cited by 4 | Viewed by 1788

Abstract

It is estimated that 1 billion waste tires are generated every year across the globe, yet only 10% are being processed, and much rubber waste is yielded during manufacturing. These waste tires and rubber scraps are poisonous to the environment when processed via incineration and landfill. Rubber circular manufacturing is an effective solution that reduces not only rubber waste but also raw material costs. In this paper we propose a two-line flowshop model for the circular rubber manufacturing problem (CRMP), where the job sequence of two production lines is appropriately aligned to obtain the shortest makespan while guaranteeing that sufficient rubber waste yielded in the first line is ready to be reused for circular production in the second line. A genetic algorithm (GA) is developed, and the design of its genetic operations is customized to the CRMP context to achieve efficient and effective evolution. The experimental results with both real and synthetic datasets show that the GA significantly surpasses two heuristics in the literature by delivering the minimum makespan, which is 3.4 to 11.2% shorter than those obtained by the two competing methods. Full article

(This article belongs to the Special Issue Sustainability and Artificial Intelligence)

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22 pages, 2624 KiB

Open AccessArticle

Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy

by Chun-Che Huang, Wen-Yau Liang, Shian-Hua Lin, Tzu-Liang (Bill) Tseng, Yu-Hsien Wang and Kuo-Hsin Wu

Sustainability 2020, 12(19), 8057; https://doi.org/10.3390/su12198057 - 29 Sep 2020

Cited by 1 | Viewed by 2179

Abstract

More and more people are involved in sustainability-related activities through social network to support/protect their idea or motivation for sustainable development. Understanding the variety of issues of social pulsation is crucial in development of social sustainability. However, issues in social media generally change overtime. Issues not identified in advance may soon become popular topics discussed in society, particularly controversial issues. Previous studies have focused on the detection of hot topics and discussion of controversial issues, rather than the identification of potential controversial issues, which truly require paying attention to social sustainability. Furthermore, previous studies have focused on issue detection and tracking based on historical data. However, not all controversial issues are related to historical data to foster the cases. To avoid the above-mentioned research gap, Artificial Intelligence (AI) plays an essential role in issue detection in the early stage. In this study, an AI-based solution approach is proposed to resolve two practical problems in social media: (1) the impact caused by the number of fan pages from Facebook and (2) awareness of the levels for an issue. The proposed solution approach to detect potential issues is based on the popularity of public opinion in social media using a Web crawler to collect daily posts related to issues in social media under a big data environment. Some analytical findings are carried out via the congregational rules proposed in this research, and the solution approach detects the attentive subjects in the early stages. A comparison of the proposed method to the traditional methods are illustrated in the domain of green energy. The computational results demonstrate that the proposed approach is accurate and effective and therefore it provides significant contribution to upsurge green energy deployment. Full article

(This article belongs to the Special Issue Sustainability and Artificial Intelligence)

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