Search Results (842)

Against the background of the unprecedented advancements related to Industry 4.0 and beyond, transitioning from classical mathematical models to fully embodied digital twins represents a critical change in the planning, monitoring, and optimization of complex industrial systems. This work outlines the subject within the broader field of applied mathematics and computational simulation while highlighting the critical role of sound mathematical foundations, numerical methodologies, and advanced computational tools in creating data-informed virtual models of physical infrastructures and processes in real time. The discussion includes examples related to smart manufacturing, additive manufacturing technologies, and cyber–physical systems with a focus on the potential for collaboration between physics-informed simulations, data unification, and hybrid machine learning approaches. Central issues including a lack of scalability, measuring uncertainties, interoperability challenges, and ethical concerns are discussed along with rising opportunities for multi/macrodisciplinary research and innovation. This work argues in favor of the continued integration of advanced mathematical approaches with state-of-the-art technologies including artificial intelligence, edge computing, and fifth-generation communication networks with a focus on deploying self-regulating autonomous digital twins. Finally, defeating these challenges via effective collaboration between academia and industry will provide unprecedented society- and economy-wide benefits leading to resilient, optimized, and intelligent systems that mark the future of critical industries and services. Full article

While technology has influenced today’s society in many aspects, how does it advance research and practice in the field of autism spectrum disorder (ASD)? In this article, we provide a narrative review of how technology enhances early detection, subtype stratification, and intervention of ASD through advancements in both hardware and software, including neuroimaging, telehealth, and artificial intelligence. Furthermore, given that technology has become an intrinsic part of humans’ daily lives, we discuss how technology can be considered more broadly as a sociocultural context for individuals with ASD in future assessments, diagnoses, and research. Full article

Taohuawu woodblock New Year prints are one of the most representative traditional multicolor woodblock print forms from the Jiangnan region of China and are recognized as an Intangible Cultural Heritage at the provincial level in Jiangsu. However, the development of mechanized and high-tech production methods, combined with the declining role of traditional festive customs in modern society, has posed significant challenges to the preservation and transmission of this art form. Existing digital preservation efforts mainly focus on two-dimensional scanning and archival storage, largely neglecting the essential processes of color separation and multicolor overprinting. In this study, a digital restoration method is proposed that integrates image processing, color clustering, and edge detection techniques for the efficient reconstruction of the traditional multicolor woodblock overprinting process. The approach applies the K-means++ clustering algorithm to extract the dominant colors and reconstruct individual color layers, in combination with CIELAB color space transformation to enhance color difference perception and improve segmentation accuracy. To address the uncertainty in determining the number of color layers, the elbow method, silhouette coefficient, and Calinski-Harabasz index are employed as clustering evaluation methods to identify the optimal number of clusters. The proposed approach enables the generation of complete, standardized digital color separations, providing a practical pathway for efficient reproduction and intelligent application of TWNY Prints, contributing to the digital preservation and innovative revitalization of intangible cultural heritage. Full article

In recent years, artificial intelligence (AI) has emerged as a transformative force across various sectors of modern society, reshaping economic landscapes, social interactions, and ethical considerations. This paper explores the multifaceted impact of AI, analyzing its implications for employment, privacy, and decision-making processes. By synthesizing recent research and case studies, we investigate the dual nature of AI as both a catalyst for innovation and a source of potential disruption. The findings highlight the necessity for proactive governance and ethical frameworks to mitigate risks associated with AI deployment while maximizing its benefits. Ultimately, this paper aims to provide a comprehensive understanding of how AI is redefining human experiences and societal norms, encouraging further discourse on the sustainable integration of these technologies in everyday life. Full article

Emerging technologies present significant challenges for society as a whole. Among these, Artificial Intelligence (AI) stands out for its transformative potential, with the capacity to fundamentally reshape human thought, behavior, and lifestyle. This article seeks to explore the business-oriented perspective on how AI should be approached in Higher Education (HE) in order to serve the commercial objectives of companies. The motivation for this inquiry stems from recurrent criticisms directed at HE institutions, particularly their perceived inertia in adopting innovations, resistance to change, and delayed responsiveness to evolving labor market demands. In this context, the study examines what businesses deem essential for universities to provide in the context of AI familiarity and examines how companies envision future collaboration between the business sector and Higher Education institutions in using AI for business applications. Adopting a qualitative research methodology, this study conducted interviews with 16 middle-management representatives from international corporations operating across diverse industries. The data were analyzed using Gioia’s methodology, which facilitated a structured identification of first-order concepts, second-order themes, and aggregate dimensions. This analytical framework enabled a nuanced understanding of business expectations regarding the role of HE institutions in preparing graduates capable of meeting economic and commercial imperatives under the pressure of AI diffusion. Full article

In the ongoing ‘data revolution’, the ubiquity of digital data in society underlines a transformative era. This is mirrored in the sciences, where ‘big data’ has emerged as a major research field. This article significantly extends previous scientometric analyses by tracing the field’s conceptual emergence and evolution across a 30-year period (1993–2022). Bibliometric analysis is based on 17 data categories that co-constitute the conceptual network of ‘big data’ research. Using Scopus, the search query resulted in 70,163 articles and 315,235 author keywords. These are analysed aggregately regarding co-occurrences of the 17 data categories and co-occurrences of data categories with author keywords, and regarding their disciplinary distributions and interdisciplinary reach. Temporal analysis reveals two major development phases: 1993–2012 and 2013–2022. The study demonstrates: (1) the rapid expansion of the research field concentrated on seven main data categories; (2) the consolidation of keyword (co-)occurrences on ‘machine learning’, ‘deep learning’, ‘artificial intelligence’ and ‘cloud computing’; and (3) significant interdisciplinarity across four main subject areas. Scholars can use the findings to combine data categories and author keywords in ways that align scholarly work with specific thematic and disciplinary interests. The findings could also inform research funding, especially concerning opportunities for cross-disciplinary research. Full article

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

Artificial intelligence (AI) agents are increasingly shaping vital sectors of society, including healthcare, education, supply chains, and finance. As their influence grows, AI alignment research plays a pivotal role in ensuring these systems are trustworthy, transparent, and aligned with human values. Leveraging blockchain technology, proven over the past decade in enabling transparent, tamper-resistant distributed systems, offers significant potential to strengthen AI alignment. However, despite its potential, the current AI alignment literature has yet to systematically explore the effectiveness of blockchain in facilitating secure and ethical behavior in AI agents. While existing systematic literature reviews (SLRs) in AI alignment address various aspects of AI safety and AI alignment, this SLR specifically examines the gap at the intersection of AI alignment, blockchain, and ethics. To address this gap, this SLR explores how blockchain technology can overcome the limitations of existing AI alignment approaches. We searched for studies containing keywords from AI, blockchain, and ethics domains in the Scopus database, identifying 7110 initial records on 28 May 2024. We excluded studies which did not answer our research questions and did not discuss the thematic intersection between AI, blockchain, and ethics to a sufficient extent. The quality of the selected studies was assessed on the basis of their methodology, clarity, completeness, and transparency, resulting in a final number of 46 included studies, the majority of which were journal articles. Results were synthesized through quantitative topic analysis and qualitative analysis to identify key themes and patterns. The contributions of this paper include the following: (i) presentation of the results of an SLR conducted to identify, extract, evaluate, and synthesize studies on the symbiosis of AI alignment, blockchain, and ethics; (ii) summary and categorization of the existing benefits and challenges in incorporating blockchain for AI alignment within the context of ethics; (iii) development of a framework that will facilitate new research activities; and (iv) establishment of the state of evidence with in-depth assessment. The proposed blockchain-based AI alignment framework in this study demonstrates that integrating blockchain with AI alignment can substantially enhance robustness, promote public trust, and facilitate ethical compliance in AI systems. Full article

Intelligent transport systems are revolutionising all aspects of modern life, increasing the efficiency of commerce, modern living, and international travel. Intelligent transport systems are systems of systems comprised of cyber, physical, and social nodes. They represent unique opportunities but also have potential threats to system operation and correctness. The emergent behaviour in Complex Cyber–Physical–Social Systems (C-CPSSs), caused by events such as cyber-attacks and network outages, have the potential to have devastating effects to critical services across society. It is therefore imperative that the risk of cascading failure is minimised through the fortifying of these systems of systems to achieve resilient mission assurance. This work designs and implements a programmatic model to validate the value of cascading failure simulation and analysis, which is then tested against a C-CPSS intelligent transport system scenario. Results from the model and its implementations highlight the value in identifying both critical nodes and percolation of consequences during a cyber failure, in addition to the importance of including social nodes in models for accurate simulation results. Understanding the relationships between cyber, physical, and social nodes is key to understanding systems’ failures that occur because of or that involve cyber systems, in order to achieve cyber and system resilience. Full article

This paper examines the epistemological transformation prompted by the integration of generative artificial intelligence technologies into social science research, proposing the “adaptive epistemology” paradigm. In today’s post-digital era—characterized by pervasive infrastructures and non-human agents endowed with generative capabilities—traditional research approaches have become inadequate. Through a critical review of historical and discursive paradigms (positivism, interpretivism, critical realism, pragmatism, transformative paradigms, mixed and digital methods), here I show how the advent of digital platforms and large language models reconfigures the boundaries between data collection, analysis, and interpretation. Employing a theoretical–conceptual framework that draws on sociotechnical systems theory, platform studies, and the philosophy of action, the core features of adaptive epistemology are identified: dynamism, co-construction of meaning between researcher and system, and the capacity to generate methodological solutions in response to rapidly evolving contexts. The findings demonstrate the need for reasoning in terms of an adaptive epistemology that could offer a robust theoretical and methodological framework for guiding social science research in the post-digital society, emphasizing flexibility, reflexivity, and ethical sensitivity in the deployment of generative tools. Full article

This article presents a scoping review of the academic literature published between 2021 and 2024 on the intersection of generative artificial intelligence (AI) and disinformation. Drawing from 64 peer-reviewed studies, the review examines the current research landscape and identifies six key thematic areas: political disinformation and propaganda; scientific disinformation; fact-checking; journalism and the media; media literacy and education; and deepfakes. The findings reveal that generative AI plays a dual role: it enables the rapid creation and targeted dissemination of synthetic content but also offers new opportunities for detection, verification, and public education. Beyond summarizing research trends, this review highlights the broader societal and practical implications of generative AI in the context of information disorder. It outlines how AI tools are already reshaping journalism, challenging scientific communication, and transforming strategies for media literacy and fact-checking. The analysis also identifies key policy and governance challenges, particularly the need for coordinated responses from governments, platforms, educators, and civil society actors. By offering a structured overview of the field, the article enhances our understanding of how generative AI can both exacerbate and help mitigate disinformation, and proposes directions for research, regulation, and public engagement. Full article

The accelerated development of Artificial Intelligence (AI) capabilities and systems is driving a paradigm shift in productivity, innovation and growth. Despite this generational opportunity, AI is fraught with significant challenges and risks. To address these challenges, responsible AI has emerged as a modus operandi that ensures protections while not stifling innovations. Responsible AI minimizes risks to people, society, and the environment. However, responsible AI principles and practice are impacted by ‘principle proliferation’ as they are diverse and distributed across the applications, stakeholders, risks, and downstream impact of AI systems. This article presents a systematic review of responsible AI principles and practice with the objectives of discovering the current state, the foundations and the need for responsible AI, followed by the principles of responsible AI, and translation of these principles into the responsible practice of AI. Starting with 22,711 relevant peer-reviewed articles from comprehensive bibliographic databases, the review filters through to 9700 at de-duplication, 5205 at abstract screening, 1230 at semantic screening and 553 at final full-text screening. The analysis of this final corpus is presented as six findings that contribute towards the increased understanding and informed implementation of responsible AI. Full article

This study aims to provide a comprehensive overview of the application of artificial intelligence (AI) methods to solve real-world problems in the oil and gas sector. The methodology involved a two-step process for analyzing AI applications. In the first step, an initial exploration of scientific articles in the Scopus database was conducted using keywords related to AI and computational intelligence, resulting in a total of 11,296 articles. The bibliometric analysis conducted using VOS Viewer version 1.6.15 software revealed an average annual growth of approximately 15% in the number of publications related to AI in the sector between 2015 and 2024, indicating the growing importance of this technology. In the second step, the research focused on the OnePetro database, widely used by the oil industry, selecting articles with terms associated with production and drilling, such as “production system”, “hydrate formation”, “machine learning”, “real-time”, and “neural network”. The results highlight the transformative impact of AI on production operations, with key applications including optimizing operations through real-time data analysis, predictive maintenance to anticipate failures, advanced reservoir management through improved modeling, image and video analysis for continuous equipment monitoring, and enhanced safety through immediate risk detection. The bibliometric analysis identified a significant concentration of publications at Society of Petroleum Engineers (SPE) events, which accounted for approximately 40% of the selected articles. Overall, the integration of AI into production operations has driven significant improvements in efficiency and safety, and its continued evolution is expected to advance industry practices further and address emerging challenges. Full article

This conceptual paper explores how artificial intelligence—particularly machine learning-based algorithmic systems—is reshaping cultural transmission and symbolic power in the digital age. It argues that algorithms operate as cultural agents, acquiring a form of operative agency that enables them to intervene in the production, circulation, and legitimation of meaning. Drawing on critical pedagogy, sociotechnical theory, and epistemological perspectives, the paper introduces an original framework: Critical Algorithmic Mediation (CAM). CAM conceptualizes algorithmic agency through three interrelated dimensions—structural, operational, and symbolic—providing a lens to analyze how algorithmic systems structure knowledge hierarchies and cultural experience. The article examines the historical role of media in cultural transmission, the epistemic effects of algorithmic infrastructures, and the emergence of algorithmic hegemony as a regime of symbolic power. In response, it advocates for a model of critical digital literacy that promotes algorithmic awareness, epistemic justice, and democratic engagement. By reframing education as a space for symbolic resistance and cultural reappropriation, this work contributes to rethinking digital literacy in societies increasingly governed by algorithmic infrastructures. Full article

Background: Drones are rapidly establishing themselves as one of the most critical technologies. Robotics, automated machinery, intelligent manufacturing, and other high-impact technological research and applications bring up pressing ethical, social, legal, and political issues. Methods: The present research aims to present the results of a qualitative investigation that looked at perceptions of the growing socio-ethical conundrums surrounding the development of drone applications. Results: According to the obtained results, participants often share similar opinions about whether different drone applications are approved by the public, regardless of their level of experience. Perceptions of drone applications appear consistent across various levels of expertise. The most notable associations are with military objectives (73%), civil protection (61%), and passenger transit and medical purposes (56%). Applications that have received high approval include science (8.70), agriculture (8.78), and disaster management (8.87), most likely due to their obvious social benefits and reduced likelihood of ethical challenges. Conclusions: The study’s findings can help shape the debate on drone acceptability in particular contexts, inform future research on promoting value-sensitive development in society more broadly, and guide researchers and decision-makers on the use of drones, as people’s attitudes, understanding, and usage will undoubtedly impact future advancements in this technology. Full article