Special Issue "Artificial Intelligence, Machine Learning and Blockchain Technology for Sustainable Organisation"

A special issue of J (ISSN 2571-8800). This special issue belongs to the section "Computer Science & Mathematics".

Deadline for manuscript submissions: 15 December 2021.

Special Issue Editors

Prof. Dr. Florin Dragan
E-Mail Website
Guest Editor
Department of Automation and Applied Informatics, Faculty of Automation and Computers, Politehnica University of Timisoara, 14 Remus Str., 300191 Timisoara, Romania
Interests: chaotic systems; robotics; artificial intelligence; control systems; PLC's programming
Dr. Larisa Ivascu
E-Mail Website
Guest Editor
1. Faculty of Management in Production and Transportation, Politehnica University of Timisoara, 14 Remus Str., 300191 Timisoara, Romania
2. Research Center in Engineering and Management, Politehnica University of Timisoara, 14 Remus Str., 300191 Timisoara, Romania
Interests: risk management; sustainability; strategic management; organizational management; environmental management; digital economy; energy
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Special Issue Information

Dear Colleagues,

The organizational approach to sustainability has become a priority for more and more organizations. Environmental issues that include climate change, depletion of natural resources, waste management and many others are in the form of priority trends that have a considerable effect on organizational management, competitiveness, growth strategy and stakeholders. The combination of these challenges is the most important opportunity for territorial, organizational and societal units. The major challenge is found in organizations that have very large amounts of data. These amounts of data are complemented by investments in new technologies that add new data. Optimal use of this data for sustainable development is a challenge for shareholders and management. Often these data are used in a qualitative approach, predominantly. To optimize organizational decisions, Artificial Intelligence (AI), Machine Learning (ML), Blockchain and other related technologies offer the possibility to capture the value and improve the decision system. AI offers new opportunities to improve packaging performance as volume and complexity increase, and the distribution chain can be improved. ML contributes to the improvement of the decision-making process, offering directions to new innovations.

Guest editors are looking for publications that address these topics, but their work is not limited to these, including other aspects of artificial intelligence, machine learning and blockchain for sustainability research, as follows:

  • Technology for sustainability;
  • Artificial intelligence, machine learning and blockchain for environmental dimension;
  • Artificial intelligence, machine learning and blockchain for circular economy;
  • Artificial inteligence, machine learning and blockchain for sustainable decisions;
  • Blockchain for supply chain efficiency;
  • Life cycle assessment;
  • Efficiency of the decision-making system based on related technologies;
  • Technology-Organization-Environment;
  • Academic perspectives and propositions for future research on sustainability;
  • Emerging technologies for organizational sustainability;
  • Organizational models;
  • Applications used to improve organizational competitiveness;

Prof. Dr. Florin Dragan
Dr. Larisa Ivascu
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 papers will be 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. J is an international peer-reviewed open access quarterly 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 1200 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

  • life cycle assessment
  • sustainability opportunity
  • blockchain technology
  • supply chain sustainability
  • barrier analysis
  • artificial intelligence
  • machine learning
  • optimization
  • decision
  • global sustainability
  • organizational sustainability

Published Papers (1 paper)

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Research

Article
A Cost-Effective IoT Model for a Smart Sewerage Management System Using Sensors
J 2021, 4(3), 356-366; https://0-doi-org.brum.beds.ac.uk/10.3390/j4030027 - 15 Jul 2021
Viewed by 751
Abstract
The sewerage system is a primary element of a city and is responsible for the congestion of both rain and gray water from homes and industries. It is essential to have a monitoring system and a plan to perform prior expansion in the [...] Read more.
The sewerage system is a primary element of a city and is responsible for the congestion of both rain and gray water from homes and industries. It is essential to have a monitoring system and a plan to perform prior expansion in the sewerage management system, to avoid massive disruption. However, there is no monitoring system in several overpopulated cities in the world, and the expansion process faces myriad difficulties and takes much time. This paper presents a model for an intelligent sewerage management system that provides real-time monitoring without any major changes to the previous system, using water sensors, a Global System for Mobile Communications (GSM) module, and a micro-controller. The condition of the sewerage acts as an input through the sensors; then, the microcontroller stores the value in the cloud and performs waste collection depending on the current situation. Meanwhile, after processing, the information reaches the monitoring system. Various trial installations of the proposed system have shown that it enables real-time monitoring to observe live conditions and helps to prevent sewerage blockage caused by solid waste. Considering a deficient cost model, this system can intensify the performance of poorly managed sewerage systems. Full article
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