Julkaisuarkisto

Extended Reality (XR) technologies are rapidly reshaping human–computer interaction; however, persistent ambiguity in the use of core terms (VR, AR, MR) hampers cumulative knowledge building, cross-study comparability, and technical standardisation. This review evaluates the XR conceptual landscape across four primary dimensions: the historical evolution of core definitions, the synthesis of contemporary theoretical frameworks, the critical extensions of the Reality-Virtuality (RV) Continuum, and the alignment between academic taxonomies and industry practices. This review evaluates the XR conceptual landscape across four primary dimensions: the historical evolution of core definitions, the synthesis of contemporary theoretical frameworks, the critical extensions of the Reality-Virtuality (RV) Continuum, and the alignment between academic taxonomies and industry practices. To address this issue, we conducted a PRISMA-guided systematic literature review across four major databases (IEEE Xplore, ACM Digital Library, Scopus, and Web of Science), complemented by seminal and industry sources. Of the 173,677 retrieved records, 59 studies were included in the synthesis. Using thematic synthesis, we mapped the historical evolution of definitions and conceptual models and identified recurring analytical dimensions. The results indicate a clear paradigm shift from Milgram’s one-dimensional Reality–Virtuality continuum—originally grounded in visual display technology—towards a multidimensional conceptual space that integrates subjective user-experience constructs (e.g., coherence and plausibility) with objective system characteristics. The included studies cover 1968–2025, with marked acceleration in the 2020s: 2022 alone accounts for the highest annual count (9 studies), and nearly half of the corpus (47.5%) was published in 2021–2025. We further show that industry actors pragmatically re-bound these academic concepts for product and market positioning, leading to systematic divergences between academic and industrial definitions. By distilling key turning points and synthesising core analytical dimensions into a structured lens, this review provides a historically grounded, actionable understanding of the XR conceptual landscape to support terminological alignment across research and practice.

Generative AI (GenAI) is increasingly applied in urban planning for text production, visualization, analytics, stakeholder communication, and participatory engagement. Large language models (LLMs) enable the creation of synthetic participants to support the early-stage design, analysis, and testing of participatory tools. This article demonstrates an innovative use of GenAI through synthetic inhabitants and experts in an immersive digital urban planning environment. DigitalTurku serves as a proof-of-concept for an immersive planning tool within an urban digital twin. The case relies on synthetic personas—residents and expert stakeholders—to evaluate how a GenAI-assisted urban platform may shape participation experiences and trust in local urban planning. The findings indicate that synthetic experts expressed a reduced bureaucratic distance, enhanced transparency, and more meaningful participation. However, assessments of tools and digital environment usability varied according to digital skills and demographic characteristics embedded in the personas. The use of synthetic personas helps identify opportunities and challenges in immersive urban planning environments and supports the design of digital tools in smart cities to strengthen human residents’ spatial understanding and experiential engagement in planning processes. The creation of synthetic data and participants is convenient with LLMs. Despite these tools’ limitations, they can play a valuable role in piloting participatory planning processes to support and complement human-based participation.

This article explores how public health information was contested on social media during the COVID-19 pandemic. Through digital ethnography of four Finnish alternative health influencers, we examine their role in politicising and polarising health attitudes and pandemic governance, and how these dynamics evolved from the early to later stages of the crisis.

Drawing on social media content from three key periods – early pandemic (January–June 2020), late pandemic (October–December 2021), and post-pandemic (March–April 2025) – the study reveals that influencers amplified politicisation and polarisation of health and distrust in authorities by leveraging: 1) lay expertise rooted in personal pre-pandemic healing narratives, 2) alternative immunological framings, 3) explicit political opposition to public health measures, and 4) engagement with conspiracy narratives.

Influencer communication was characterised by opportunism, blending political activism, commercial interests, and personal wellness branding. By aligning with values of authenticity and trust, influencers cultivated belonging within alternative or conspiritual health communities. Yet, their trajectories varied in terms of politicisation and conspirituality, illustrating how pandemic-driven polarisation unfolded within the alternative health scene. This study offers critical insights into the evolving dynamics of the health politicisation and the role of social media in shaping public trust in medical expertise.

Observations of core-collapse supernovae (CCSNe) suggest that some massive stars undergo intense mass loss shortly before explosion, but the underlying mechanisms remain unknown. Here, we report evidence of giant outbursts of clumpy material from a massive star in the final decades before explosion. Photometric, spectroscopic, and polarimetric data of SN 2024qiw reveal a bumpy light curve, a broad Hα profile, and variable polarization, all consistent with interactions taking place between SN ejecta and clumpy circumstellar material, implying a mass-loss rate of ≳10⁻² M_⊙ yr⁻¹. Taken together, the most likely explanation is multiple major eruptions, similar to those of luminous blue variables (LBVs), but occurring shortly before explosion. This challenges standard stellar evolution theory by requiring either that LBVs explode terminally or that other evolutionary phases produce eruptive episodes. In spite of very high pre-SN mass loss, the resulting SN is classed as Type II, rather than Type IIn, highlighting diverse and previously unrecognized late-stage mass-loss processes.