Special Issue "Innovations and Applications in Smart Sustainable Cities and Communities"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (10 January 2022) | Viewed by 6120

Special Issue Editors

Prof. Dr. Miltiadis D. Lytras
E-Mail Website
Guest Editor
School of Business, Deree—The American College of Greece, 6 Gravias Street, GR-153 42 Aghia Paraskevi Athens, Greece
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Visvizi
E-Mail Website
Guest Editor
1. Institute of International Studies (ISM), SGH Warsaw School of Economics, Al. Niepodległości 162, 02-554 Warsaw, Poland
2. Effat College of Business, Effat University, Jeddah 21551, Saudi Arabia
Interests: smart cities; smart villages; international political economy (IPE); information and communication technology (ICT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

What makes a city and community smart sustainable? This question not only is believed to have numerous potential answers, but it also provides opportunities for a wide range of applications within today’s cities and communities, for instance, urban planning, policy making, governance, migration, education, energy management, health science, transportation, and logistics. These are reinforced by intricate innovations, such as Internet of Things (IoT), big data, shallow and deep machine learning, optimization, cloud/fog/edge computing, socially aware computing, and blockchain.

Generally, people are adopting a human-centric approach which puts citizens at the center of the smart sustainable transformation revolution. This paves the way toward smart sustainable cities, and communities that increase life quality, social engagement, efficiency, security, privacy, robustness, and sustainability. We could consider previous decades as the development phase of theories, platforms, tools, hardware, and software, whereas we are now ready to enjoy the fruitful results of various applications everywhere.

This Special Issue is intended to report high-quality research on new directions and innovations towards applications in smart sustainable cities and communities, more specifically state-of-the-art approaches, methodologies, and systems for the design, development, deployment, and innovative use of those smart and sustainable technologies for providing insights. To exchange knowledge, all submissions are very welcome that share personal opinions on future directions (as sustainability is a continuous process and development) in smart sustainable cities and communities, as concluding paragraphs. Topics of interest include but are not limited to:

  • Multidisciplinary, transdisciplinary, and cross-disciplinary approaches
  • Social science and political science
  • Governance and civic engagement
  • Transportation, mobility, and logistics management
  • Internet of Things applications
  • Machine learning algorithms including shallow and deep learning
  • Education and learning analytics
  • Energy and wastage reduction
  • Semantic and sentiment analysis
  • Complex network
  • Pilot studies and case studies
  • Literature review in smart cities and communities
  • Meta-analysis in smart cities and communities
  • Smart cities, smart communities, smart applications and the aftermath of the Covid-19 pandemic
Prof. Miltiadis D. Lytras
Prof. Anna Visvizi
Dr. Kwok Tai Chui
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 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.

Published Papers (6 papers)

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Research

Article
Goal Programming and Mathematical Modelling for Developing a Capacity Planning Decision Support System-Based Framework in Higher Education Institutions
Appl. Sci. 2022, 12(3), 1702; https://doi.org/10.3390/app12031702 - 07 Feb 2022
Viewed by 640
Abstract
Achieving the Saudi Kingdom’s vision 2030 in the higher education sector requires higher education institutions to make a significant simultaneous change in their current practices. This encompasses the transitioning of government-funded educational institutions to be financially independent. Therefore, a prompt, agile transition is [...] Read more.
Achieving the Saudi Kingdom’s vision 2030 in the higher education sector requires higher education institutions to make a significant simultaneous change in their current practices. This encompasses the transitioning of government-funded educational institutions to be financially independent. Therefore, a prompt, agile transition is required while maintaining a positive socioeconomic impact, entrepreneurship and innovation, and high-quality education. This necessitates the transition to lean processes and the review of current practices. One of the most vital processes in educational institutions is student admission/enrollment capacity planning. This study puts forward a capacity planning decision support system (DSS)-based framework for university student enrollment. The framework was applied to the case of KAU, where current practice and challenges are presented, and from which data were collected. A top-down/bottom-up approach was followed and applied using the goal programming technique and a developed mathematical model, respectively. Results show that the proposed framework effectively affects student admission/enrollment capacity planning on strategic and operational levels. Moreover, it can be used in other planning aspects of higher education in universities, such as human resources planning, teaching load planning, faculty-to-student ratios, accreditation, quality requirements, lab capacity planning, equipment/teaching aids procurement, and financial planning, to mention a few. The implications of this study include assisting decision-makers in higher education institutions in matching their admission/enrollment capacity of student numbers between the macro-strategic and the micro-operational level. Full article
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Article
Recognition of Vehicle License Plates Based on Image Processing
Appl. Sci. 2021, 11(14), 6292; https://doi.org/10.3390/app11146292 - 07 Jul 2021
Cited by 2 | Viewed by 1195
Abstract
In this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is [...] Read more.
In this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is commonly used suffers with a disadvantage of low recognition rate in the tilted and low-resolution images, as it is trained with images acquired from the front of the license plate. Furthermore, the vehicle images acquired by using CCTV have issues such as limitation of resolution and perspective distortion. Such factors make it difficult to apply the commonly used deep learning model. To improve the recognition rate, an algorithm which is a combination of the super-resolution generative adversarial network (SRGAN) model, and the perspective distortion correction algorithm is proposed in this paper. The accuracy of the proposed algorithm was verified with a character recognition algorithm YOLO v2, and the recognition rate of the vehicle license plate image was improved 8.8% from the original images. Full article
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Article
Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method
Appl. Sci. 2021, 11(9), 4087; https://doi.org/10.3390/app11094087 - 29 Apr 2021
Cited by 1 | Viewed by 723
Abstract
With the popularization of social networks, the abundance of unstructured data regarding environmental complaints is rapidly increasing. This study established a text mining framework for Chinese civil environmental complaints and analyzed the characteristics of environmental complaints, including keywords, sentiment, and semantic networks, with [...] Read more.
With the popularization of social networks, the abundance of unstructured data regarding environmental complaints is rapidly increasing. This study established a text mining framework for Chinese civil environmental complaints and analyzed the characteristics of environmental complaints, including keywords, sentiment, and semantic networks, with two–year environmental complaints records in Guangzhou city, China. The results show that the keywords of environmental complaints can be effectively extracted, providing an accurate entry point for solving environmental problems; light pollution complaints are the most negative, and electromagnetic radiation complaints have the most fluctuating emotions, which may be due to the diversity of citizens’ perceptions of pollution; the nodes of the semantic network reveal that citizens pay the most attention to pollution sources but the least attention to stakeholders; the edges of the semantic network shows that pollution sources and pollution receptors show the most concerning relationship, and the pollution receptors’ relationships with pollution behaviors, sensory features, stakeholders, and individual health are also highlighted by citizens. Thus, environmental pollution management should not only strengthen the control of pollution sources but also pay attention to these characteristics. This study provides an efficient technical method for unstructured data analysis, which may be helpful for precise and smart environmental management. Full article
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Article
An Optimization Route Selection Method of Urban Oversize Cargo Transportation
by and
Appl. Sci. 2021, 11(5), 2213; https://doi.org/10.3390/app11052213 - 03 Mar 2021
Cited by 3 | Viewed by 709
Abstract
In order to select the optimal transportation route among alternative transportation routes more accurately and objectively, the choice of urban oversize cargo transportation routes was studied by taking the optimization weight–TOPSIS combination method for specific calculations. This model, based on an entropy weight [...] Read more.
In order to select the optimal transportation route among alternative transportation routes more accurately and objectively, the choice of urban oversize cargo transportation routes was studied by taking the optimization weight–TOPSIS combination method for specific calculations. This model, based on an entropy weight method, cloud model, and TOPSIS method, combines the superiority of the cloud model for reflecting the randomness and discreteness of subjective evaluation with the advantages of the TOPSIS method in dealing with the problem of multi-objective programming. Through selecting and classifying several the main road influencing factors of urban oversize cargo transportation, based on the data of four urban roads, the entropy weight method is used to initially determine the weights of each influencing factor, the cloud model is used to optimize weights, the TOPSIS method is used to compare and evaluate the paths, and the optimal transportation route is selected on this basis. The results showed that the optimization weight–TOPSIS method is scientific and accurate for the multi-objective planning of oversize cargo transportation route selection, and solves the problem of the impact of subjective factors in existing methods and the difficulty of processing multiple influencing factors. The Pearson consistency test results show that the Pearson correlation coefficient between the proposed method and the actual oversize cargo transportation route selection is 0.995, which is higher than the calculation results without using the combination weight. Full article
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Article
Ensuring Inclusion and Diversity in Research and Research Output: A Case for a Language-Sensitive NLP Crowdsourcing Platform
Appl. Sci. 2020, 10(18), 6216; https://doi.org/10.3390/app10186216 - 08 Sep 2020
Cited by 4 | Viewed by 1014
Abstract
In the context of the debate on the need to place citizens at the center of the technological revolution, this paper makes a case for a natural language processing (NLP) crowdsourcing platform that ensures inclusion and diversity, thus making the research outcome relevant [...] Read more.
In the context of the debate on the need to place citizens at the center of the technological revolution, this paper makes a case for a natural language processing (NLP) crowdsourcing platform that ensures inclusion and diversity, thus making the research outcome relevant and applicable across issues and domains. This paper also makes the case that by enabling participation for a wide variety of stakeholders, this NLP crowdsourcing platform might ultimately prove useful in the decision- and policy-making processes at city, community, and country levels. Against the backdrop of the debates on artificial intelligence (AI) and NLP research, and considering substantial differentiation specific to the Arab language, this paper introduces and evaluates an Arab language-sensitive NLP crowdsourcing platform. The value of the platform and its accuracy are measured via the System Usability Scale (SUS), where it scores 72.5, i.e., above the accepted usability average. These findings are crucial for NLP research and the research community in general. They are equally promising in view of the practical application of the research findings. Full article
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Article
Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
Appl. Sci. 2020, 10(17), 5892; https://doi.org/10.3390/app10175892 - 26 Aug 2020
Cited by 9 | Viewed by 1057
Abstract
Non-orthogonal multiple access (NOMA) plays an important role in achieving high capacity for fifth-generation (5G) networks. Efficient resource allocation is vital for NOMA system performance to maximize the sum rate and energy efficiency. In this context, this paper proposes optimal solutions for user [...] Read more.
Non-orthogonal multiple access (NOMA) plays an important role in achieving high capacity for fifth-generation (5G) networks. Efficient resource allocation is vital for NOMA system performance to maximize the sum rate and energy efficiency. In this context, this paper proposes optimal solutions for user pairing and power allocation to maximize the system sum rate and energy efficiency performance. We identify the power allocation problem as a nonconvex constrained problem for energy efficiency maximization. The closed-form solutions are derived using Karush–Kuhn–Tucker (KKT) conditions for maximizing the system sum rate and the Dinkelbach (DKL) algorithm for maximizing system energy efficiency. Moreover, the Hungarian (HNG) algorithm is utilized for pairing two users with different channel condition circumstances. The results show that with 20 users, the sum rate of the proposed NOMA with optimal power allocation using KKT conditions and HNG (NOMA-PKKT-HNG) is 6.7% higher than that of NOMA with difference of convex programming (NOMA-DC). The energy efficiency with optimal power allocation using DKL and HNG (NOMA-PDKL-HNG) is 66% higher than when using NOMA-DC. Full article
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