Digital in 2024

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

Deadline for manuscript submissions: 31 March 2024 | Viewed by 1740

Special Issue Editors

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
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
Special Issues, Collections and Topics in MDPI journals
School of Pure and Applied Sciences, Open University of Cyprus, 2220 Nicosia, Cyprus
Interests: data management; data mining; data science; scientometrics
Special Issues, Collections and Topics in MDPI journals

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 (digital@mdpi.com) 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 (1 paper)

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Research

17 pages, 28788 KiB  
Article
An Improved Approach for Generating Digital Twins of Cultural Spaces through the Integration of Photogrammetry and Laser Scanning Technologies
Digital 2024, 4(1), 215-231; https://doi.org/10.3390/digital4010011 - 16 Feb 2024
Viewed by 504
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Artificial intelligence for the prevention of gender violence
Authors: Donatella Curtotti; Isabella Loiodice; Maria Novella Masullo; Mastroserio Annalisa; Guido Colaiacovo; Agostino Marengo; Wanda Nocerino
Affiliation: University of Foggia (IT)
Abstract: Gender-based violence represents a 'perennial emergency' that plagues contemporary society. The dangers lurking between the folds of the phenomenon are manifold (physical and psychological safety, impairment of relational and working capacities) and risk escalating in a degenerative escalation culminating in its most extreme form, i.e. the murder of the victim (so-called 'feminicide'). On the other hand, as recent studies on the subject show, the phenomenon is constantly evolving and ends up taking on new traits that make it even more pervasive: in addition to the 'traditional' cases of aggression in the 'physical' dimension, in fact, new forms of gender-based violence perpetrated online (hate speech, cyberstalking) are emerging. At the legislative level, several attempts have been made to curb the phenomenon. A lot has been done on the repressive front (e.g. the introduction of the crime of persecutory acts and revenge porn) and something is moving on the preventive side (e.g. the strengthening of the prevention measure of the police commissioner's warning and the application of the prevention measures of special surveillance and residence obligation to suspects of crimes related to gender violence) in the awareness of the centrality of proactive intervention to prevent 'spy crimes' from degenerating into more serious offences. In the educational sphere, too, preventive action has been taken in recent years to 'train' the younger generations, from an early age, in different models of intergenerational relations. However, the state of the art is still far from achieving the expected effects. In this context, the research - in an integrated and interdisciplinary vision - intends to exploit the potential offered by Artificial Intelligence (AI) for general-preventive purposes, in order to identify, detect and monitor the alarm signals that, very often, are prodromal to the realisation of the cases falling within the genus of gender-based violence in its twofold dimension, physical and digital. Hence, once the traits characterising 'spy behaviours' have been identified - with a view to differentiating them from 'spy crimes' - and the impact of such conducts on the commission of crimes of gender-based violence and so-called feminicides has been assessed, the creation of special algorithms for preventive detection will be carried out, assessing the limits and legal implications arising therefrom, also in the light of the obligations imposed by the European Parliament Resolution of 2021 and the annexed Proposal for a regulation on AI. It is necessary: 1) to verify the tightness of the new systems with the internal principles and supranational sources to which the system is inspired; 2) to understand whether and to what extent it is possible to use the 'information' results acquired in the preventive phase as evidence in criminal proceedings. The ultimate aim of the research is to develop - through the integrated action of the various competences - an operational model that makes the project concretely applicable in practice

Title: Developing scenarios for use in algo-literacy: Design considerations for Media and Information Literacy
Authors: Divina Frau-Meigs
Affiliation: Digital Humanities, University Sorbonne Nouvelle, Paris, France
Abstract: Algorithm literacy and AI literacy have both been called to the rescue in the face of the fast-evolving large language models (LLMs) systems that have been released in the public sphere and embedded in many information services. Such emerging literacies are crucial to combat disinformation in the era of disinformation and synthetic media as research shows that AI tools have generated “fake news” and presented them convincingly to users (Ngo et al 2023). Yet, in the education field, several challenges are observed: “(1) lack of teachers’ AI knowledge, skills, and confidence; (2) lack of curriculum design; and (3) lack of teaching guidelines.” (Su et al 2023). In this study, we investigated whether a professional fact-checking tool called “the Crossover Dashboard” could be utilized in training educators in developing scenarios for use to detect the role of algorithms in users’ online news consumption. We further investigated a) what competences users needed to be algo-literate, b) how to design a media literacy AI-centred matrix that fosters trust in using algorithms in everyday life. This paper relies on an empirical research design, based on the Crossover project (2023). We first discuss prior attempts to define and measure algo- and AI-literacy, insisting on the needs for a user-centred focus. Then, we describe the four scenarios for use that emerge from the Crossover dashboard monitoring of real time online news and the role of ranking, recommendation, and prediction algorithms. Finally, we present a comprehensive approach to the design of algo-literacy within Media and Information Literacy education, to scale up citizen’s agency to ensure familiarity and confidence for educators and learners. The paper’s contributions to competences and scenarios for use derived from the experimentation can contribute to guide future adoption of Algo- and AI-literacy within the extended community of MIL course developers and educators. Keywords: fake news, disinformation, algorithms, AI-literacy, algorithm-literacy, Media and Information Literacy, teaching and learning, fact-checking

Title: Empowering Community Clinical Triage Through Innovative Data-Driven Machine Learning
Author: Herzog
Highlights: Proposed a new AI-based decision-making approach for Community Health Care Services. An ML-powered patient triaging system gives a 99% accuracy in prioritising cardiological patients for treatment and doctor/nurse visits. The model helps healthcare professionals reduce the triage time (ML decision is made in 0.059 seconds) and focus on the immediate treatment of the first-line patients.

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