Digital in 2024

A special issue of Digital (ISSN 2673-6470).

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 8748

Special Issue Editors


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Guest Editor
Department of Health Promotion and e-Health, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, Skawińska Str. 8, 31-066 Krakow, Poland
Interests: e-health; telemedicine; digital health; public health; health promotion; health literacay; digital health literecy; respiratory medicine
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Urban and Regional Innovation Research (URENIO) Faculty of Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: smart cities; intelligent cities; innovation systems; innovation strategy; urban and regional planning
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Guest Editor
School of Pure and Applied Sciences, Open University of Cyprus, 2220 Nicosia, Cyprus
Interests: data management; data mining; data science; scientometrics
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Special Issue Information

Dear Colleagues,

In 2023, our journal Digital was accepted into Scopus and dblp Computer Science Bibliography. Thanks go to our readers, innumerable authors, anonymous peer reviewers, editors, and all the people working in some way for the journal who have joined their efforts for years.

To highlight another consecutive year of excellence, and to create a good start to the year, a Special Issue entitled "Digital in 2024" is being launched, which is part of the MDPI journal New Year Special Issue Series. This Special Issue will be a collection of high-quality reviews from our editors-in-chief, editorial board members, guest editors, topical advisory panel members, reviewer board members, authors, and reviewers. The submission deadline will be 31 March 2024. We kindly encourage all research groups to contribute up-to-date results from the latest development in their respective laboratories.

You are welcome to send short proposals of feature papers to our editorial office ([email protected]) before submission.

These will first be evaluated by our editors. Please note that the selected full papers will still be subject to a thorough and rigorous peer review.

Prof. Dr. Mariusz Duplaga
Prof. Dr. Nicos Komninos
Prof. Dr. Yannis Manolopoulos
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. Digital 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 1000 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

  • artificial intelligence
  • machine learning
  • big data
  • recommendation system
  • natural language processing
  • IoT
  • blockchain
  • cybersecurity
  • virtual and augmented reality
  • digital twins
  • smart cities
  • digital health
  • digital education
  • digital society
  • digital economy
  • digital agriculture

Published Papers (6 papers)

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Research

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17 pages, 1897 KiB  
Article
Algorithm Literacy as a Subset of Media and Information Literacy: Competences and Design Considerations
by Divina Frau-Meigs
Digital 2024, 4(2), 512-528; https://doi.org/10.3390/digital4020026 - 6 Jun 2024
Viewed by 332
Abstract
Algorithms, indispensable to understand Artificial Intelligence (AI), are omnipresent in social media, but users’ understanding of these computational processes and the way they impact their consumption of information is often limited. There is a need for Media and Information Literacy (MIL) research investigating [...] Read more.
Algorithms, indispensable to understand Artificial Intelligence (AI), are omnipresent in social media, but users’ understanding of these computational processes and the way they impact their consumption of information is often limited. There is a need for Media and Information Literacy (MIL) research investigating (a) how MIL can support algorithm literacy (AL) as a subset of competences and with what working definition, (b) what competences users need in order to evaluate algorithms critically and interact with them effectively, and (c) how to design learner-centred interventions that foster increased user understanding of algorithms and better response to disinformation spread by such processes. Based on Crossover project research, this paper looks at four scenarios used by journalists, developers and MIL experts that mirror users’ daily interactions with social media. The results suggest several steps towards integrating AL within MIL goals, while providing a concrete definition of algorithm literacy that is experience-based. The competences and design considerations are organised in a conceptual framework thematically derived from the experimentation. This contribution can support AI developers and MIL educators in their co-design of algorithm-literacy interventions and guide future research on AL as part of a set of nested AI literacies within MIL. Full article
(This article belongs to the Special Issue Digital in 2024)
11 pages, 220 KiB  
Article
Assistive Technology for Higher Education Students with Disabilities: A Qualitative Research
by Konstantinos Papadopoulos, Eleni Koustriava, Lisander Isaraj, Elena Chronopoulou, Flavio Manganello and Rafael Molina-Carmona
Digital 2024, 4(2), 501-511; https://doi.org/10.3390/digital4020025 - 23 May 2024
Viewed by 621
Abstract
The objective of this qualitative investigation is to identify the assistive technology recognized by students with disabilities and to determine the assistive technology (software apps and devices) they require both at university and at home. A total of forty-two students, comprising 20 males [...] Read more.
The objective of this qualitative investigation is to identify the assistive technology recognized by students with disabilities and to determine the assistive technology (software apps and devices) they require both at university and at home. A total of forty-two students, comprising 20 males and 22 females, were recruited from four different countries (Germany, Greece, Italy, and Spain) for participation in this study. The sample encompassed 10 students with visual impairments, 11 with hearing impairments, 11 with mobility impairments, and 10 with specific learning disabilities. Semi-structured interviews were conducted with the students either online or in person. Content analysis was employed to scrutinize the data obtained from these interviews. The outcomes of this analysis shed light on the assistive technology acknowledged, utilized, or desired by students with disabilities in both academic and domestic settings. The findings from this study carry practical implications for fostering inclusive and accessible education within higher education institutions, benefiting accessibility units/offices staff as well as teaching personnel. Full article
(This article belongs to the Special Issue Digital in 2024)
15 pages, 3776 KiB  
Article
Empowering Community Clinical Triage through Innovative Data-Driven Machine Learning
by Binu M. Suresh and Nitsa J. Herzog
Digital 2024, 4(2), 410-424; https://doi.org/10.3390/digital4020020 - 26 Apr 2024
Viewed by 537
Abstract
Efficient triaging and referral assessments are critical in ensuring prompt medical intervention in the community healthcare (CHC) system. However, the existing triaging systems in many community health services are an intensive, time-consuming process and often lack accuracy, particularly for various symptoms which might [...] Read more.
Efficient triaging and referral assessments are critical in ensuring prompt medical intervention in the community healthcare (CHC) system. However, the existing triaging systems in many community health services are an intensive, time-consuming process and often lack accuracy, particularly for various symptoms which might represent heart failure or other health-threatening conditions. There is a noticeable limit of research papers describing AI technologies for triaging patients. This paper proposes a novel quantitative data-driven approach using machine learning (ML) modelling to improve the community clinical triaging process. Furthermore, this study aims to employ the feature selection process and machine learning power to reduce the triaging process’s waiting time and increase accuracy in clinical decision making. The model was trained on medical records from a dataset of patients with “Heart Failure”, which included demographics, past medical history, vital signs, medications, and clinical symptoms. A comparative study was conducted using a variety of machine learning algorithms, where XGBoost demonstrated the best performance among the other ML models. The triage levels of 2,35,982 patients achieved an accuracy of 99.94%, a precision of 0.9986, a recall of 0.9958, and an F1-score of 0.9972. The proposed diagnostic model can be implemented for the CHC decision system and be developed further for other medical conditions. Full article
(This article belongs to the Special Issue Digital in 2024)
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17 pages, 26443 KiB  
Article
Generative Artificial Intelligence Image Tools among Future Designers: A Usability, User Experience, and Emotional Analysis
by Joana Casteleiro-Pitrez
Digital 2024, 4(2), 316-332; https://doi.org/10.3390/digital4020016 - 17 Apr 2024
Viewed by 938
Abstract
Generative Artificial Intelligence (GenAI) image tools hold the promise of revolutionizing a designer’s creative process. The increasing supply of this type of tool leads us to consider whether they suit future design professionals. This study aims to unveil if three GenAI image tools—Midjourney [...] Read more.
Generative Artificial Intelligence (GenAI) image tools hold the promise of revolutionizing a designer’s creative process. The increasing supply of this type of tool leads us to consider whether they suit future design professionals. This study aims to unveil if three GenAI image tools—Midjourney 5.2, DreamStudio beta, and Adobe Firefly 2—meet future designers’ expectations. Do these tools have good Usability, show sufficient User Experience (UX), induce positive emotions, and provide satisfactory results? A literature review was performed, and a quantitative empirical study based on a multidimensional analysis was executed to answer the research questions. Sixty users used the GenAI image tools and then responded to a holistic evaluation framework. The results showed that while the GenAI image tools received favorable ratings for Usability, they fell short in achieving high scores, indicating room for improvement. None of the platforms received a positive evaluation in all UX scales, highlighting areas for enhancement. The benchmark comparison revealed that all platforms, except for Adobe Firefly’s Efficiency scale, require enhancements in pragmatic and hedonic qualities. Despite inducing neutral to above-average positive emotions and minimal negative emotions, the overall satisfaction was moderate, with Midjourney aligning more closely with user expectations. This study emphasizes the need for significant improvements in Usability, positive emotional resonance, and result satisfaction, even more so in UX, so that GenAI image tools can meet future designers’ expectations. Full article
(This article belongs to the Special Issue Digital in 2024)
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17 pages, 28788 KiB  
Article
An Improved Approach for Generating Digital Twins of Cultural Spaces through the Integration of Photogrammetry and Laser Scanning Technologies
by Markos Konstantakis, Georgios Trichopoulos, John Aliprantis, Nikitas Gavogiannis, Anna Karagianni, Panos Parthenios, Konstantinos Serraos and George Caridakis
Digital 2024, 4(1), 215-231; https://doi.org/10.3390/digital4010011 - 16 Feb 2024
Viewed by 1323
Abstract
The paper introduces an innovative methodology that combines photogrammetry and laser scanning techniques to create detailed 3D models of historic mansions within the Kifissia region of Attica, Greece. While photogrammetry excels in capturing intricate textures, it faces challenges such as lighting variations and [...] Read more.
The paper introduces an innovative methodology that combines photogrammetry and laser scanning techniques to create detailed 3D models of historic mansions within the Kifissia region of Attica, Greece. While photogrammetry excels in capturing intricate textures, it faces challenges such as lighting variations and precise image alignment. On the other hand, laser scanning offers precision in capturing geometric details but struggles with reflective surfaces and large datasets. Our study integrates these methods to leverage their strengths and address limitations, resulting in comprehensive and accurate digital twins of cultural spaces. The methodology section outlines the step-by-step process of integration, emphasizing solutions to specific challenges encountered in the study area. Preliminary results showcase the enhanced fidelity and completeness of the digital twins, demonstrating the effectiveness of the combined approach. The subsequent sections of the paper delve into a detailed presentation of the methodology, provide a comprehensive analysis of obtained results, and discuss the implications of this innovative approach in cultural preservation and broader applications. Full article
(This article belongs to the Special Issue Digital in 2024)
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Review

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27 pages, 7874 KiB  
Review
Decoding the Relationship of Artificial Intelligence, Advertising, and Generative Models
by Camille Velasco Lim, Yu-Peng Zhu, Muhammad Omar and Han-Woo Park
Digital 2024, 4(1), 244-270; https://doi.org/10.3390/digital4010013 - 1 Mar 2024
Viewed by 2618
Abstract
Although artificial intelligence technologies have provided valuable insights into the advertising industry, more comprehensive studies that properly examine the applications of AI in advertising using scientometric network analysis are needed. Using publications from journals indexed in the Web of Science, we seek to [...] Read more.
Although artificial intelligence technologies have provided valuable insights into the advertising industry, more comprehensive studies that properly examine the applications of AI in advertising using scientometric network analysis are needed. Using publications from journals indexed in the Web of Science, we seek to analyze the emergence of AI through the examination of keyword co-occurrences and co-authorship. Our goal is to identify essential concepts and influential research that have significantly impacted the advertising business. The findings highlight noteworthy patterns, indicating the growing importance of machine learning tools and techniques such as deep learning, and advanced natural language processing methods like word2vec, GANs, and others, as well as their societal impacts as they continue to define the future of advertising practices. Full article
(This article belongs to the Special Issue Digital in 2024)
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