Search Results (128)

This paper examines how 360-degree panoramic tours can enhance heritage promotion, accessibility, and engagement, illustrated through two case studies: the Understory Art and Nature Trail in Northcliffe and the Subiaco Hotel in Perth, Western Australia. The Understory Art Trail was deployed in Google Street View to deliver an interactive, virtual walkthrough of outdoor art installations. This made the site accessible to a geographically diverse global audience, including those unable to visit in person. In contrast, the Subiaco Hotel tour was created with 3DVista. It integrated multimedia features such as historical photographs, architectural drawings, and narrative audio, offering users a layered exploration of built heritage. The two studies were designed so that frameworks like Technology Acceptance Model (TAM) could be applied to them to evaluate visitor experience. However, this paper focuses on the workflow for providing 360-degree panoramic tours, the integration of AR, low-cost digital twins, and the testing of interactive web platforms. Google Street View demonstrates ease of use through familiar navigation, while 3DVista reflects usefulness through its richer interpretive features. By analyzing workflows and digital strategies on both platforms, the study evaluates their effectiveness in increasing online visitor engagement, supporting heritage tourism, and communicating cultural significance. Challenges related to technical limitations, geolocation accuracy, audience targeting, and resource constraints are critically discussed. The findings demonstrate that context-sensitive applications of 360-degree tours are valuable for visibility, education, and long-term preservation. The paper concludes with targeted recommendations to guide future heritage projects in leveraging immersive digital technologies to expand audience engagement and support sustainable heritage management. Full article

Traditional methods for documenting cultural heritage often remain inadequate for preserving structural data, making it virtually impossible to archive architectural works that no longer survive. This study investigates the use of augmented reality (AR) technology to improve the sustainability of architectural heritage in the digital environment. The former People’s House (Halkevi) building, once located in Konya, Türkiye but no longer standing, was selected as the case study. Drawing on available photographs and historical documents, a 3D model of the building was generated using Autodesk Revit, further refined in 3ds Max, and transferred to an interactive digital platform via AR applications (ARki, Augmentecture, and a custom AR solution developed with Unity 3D + Vuforia). These applications offer an accessible solution for art and architectural historians thanks to their user-friendly interfaces and the fact that they do not require coding knowledge. Among the tested AR platforms, the Unity + Vuforia-based application yielded the most consistent performance, especially in terms of interactivity, visual stability, and environ-mental integration. The findings indicate that augmented reality can serve as a practical tool for the digital documentation of cultural heritage, demonstrating that researchers without advanced technical expertise can effectively utilize these technologies. This study contributes to digital heritage preservation by proposing a simplified AR-based methodology that reduces the need for cross-disciplinary expertise, enabling wider participation of local stakeholders in the documentation and visualization of lost architectural heritage. Full article

As a national treasure, architectural heritage carries multiple value dimensions such as history, technology, art, and culture. With the increasing demand for architectural heritage protection and utilization, the traditional static digital model of architectural heritage based on geometric expression can no longer meet the practical application of multi-stage and multi-level scenarios. To this end, this paper proposes a value-chain-driven multi-level digital twin model of architectural heritage. Based on the three-stage logic of protection, management, and dissemination of value-chain classification, it integrates four types of models: geometry, physics, rules, and behavior. Combined with different hierarchical application levels, the digital model of architectural heritage is refined into a VCLOD (Value-Chain-Driven Level of Detail) detail hierarchy system to achieve a unified expression from spatial form restoration to intelligent response. Through the empirical application of three typical scenarios: the full-area guided tour of the Forbidden City, the exhibition curation of the central axis and the preventive protection of the Meridian Gate, the model shows the following specific results: (1) the efficiency of tourist guidance is improved through real-time personalized path planning; (2) the exhibition planning and visitor experience are improved through dynamic monitoring and interactive management of the exhibition environment; (3) the predictive analysis and preventive protection measures of structural safety are realized, effectively ensuring the structural safety of the Meridian Gate. The research results provide a theoretical basis and practical support for the systematic expression and intelligent evolution of digital twins of architectural heritage. Full article

The development of scientific research related to the impact of the built environment on people in recent decades has changed the way healthcare facilities are designed in the 21st century. The ideal approach is characterized by research-based design and the creation of a therapeutic environment. An integral component of the hospital environment is art, the positive impact of which on hospital users has been proven by many scientific studies. The purpose of this research was to determine the specifics and forms of art in contemporary hospitals. The qualitative study included 91 hospitals in 18 European countries. Based on scientific literature, acquired data on the surveyed facilities, and study visits to 20 hospitals, 12 forms of art were identified. In addition to traditional art forms, art reflecting the achievements of 21st-century science and technology can often be seen in contemporary hospitals. The best example is interactive graphics using digital techniques and tools. This is art whose base is created by the artist, but at the same time, it is a form that responds to the environment and movement, engaging the viewer to interact. Full article

The integration of artificial intelligence (AI) in education offers novel opportunities to enhance critical thinking while also posing challenges to independent cognitive development. In particular, Human-Centered Artificial Intelligence (HCAI) in education aims to enhance human experience by providing a supportive and collaborative learning environment. Rather than replacing the educator, HCAI serves as a tool that empowers both students and teachers, fostering critical thinking and autonomy in learning. This study investigates the potential for AI to become a collaborative partner that assists learning and enriches academic engagement. The research was conducted during the 2024–2025 winter semester within the Pedagogical and Teaching Sufficiency Program offered by the Audio and Visual Arts Department, Ionian University, Corfu, Greece. The research employs a hybrid ethnographic methodology that blends digital interactions—where students use AI tools to create artistic representations—with physical classroom engagement. Data was collected through student projects, reflective journals, and questionnaires, revealing that structured dialog with AI not only facilitates deeper critical inquiry and analytical reasoning but also induces a state of flow, characterized by intense focus and heightened creativity. The findings highlight a dialectic between individual agency and collaborative co-agency, demonstrating that while automated AI responses may diminish active cognitive engagement, meaningful interactions can transform AI into an intellectual partner that enriches the learning experience. These insights suggest promising directions for future pedagogical strategies that balance digital innovation with traditional teaching methods, ultimately enhancing the overall quality of education. Furthermore, the study underscores the importance of integrating reflective practices and adaptive frameworks to support evolving student needs, ensuring a sustainable model. Full article

Chinese Optical Character Recognition (OCR) technology is essential for digital transformation in Chinese regions, enabling automated document processing across various applications. However, Chinese OCR systems struggle with visually similar characters, where subtle stroke differences lead to systematic recognition errors that limit practical deployment accuracy. This study develops FLIP (Feedback Learning-based Intelligent Plugin), a lightweight post-processing plugin designed to improve Chinese OCR accuracy across different systems without external dependencies. The plugin operates through three core components as follows: UTF-8 encoding-based output parsing that converts OCR results into mathematical representations, error correction using information entropy and weighted similarity measures to identify and fix character-level errors, and adaptive feedback learning that optimizes parameters through user interactions. The approach functions entirely through mathematical calculations at the character encoding level, ensuring universal compatibility with existing OCR systems while effectively handling complex Chinese character similarities. The plugin’s modular design enables seamless integration without requiring modifications to existing OCR algorithms, while its feedback mechanism adapts to domain-specific terminology and user preferences. Experimental evaluation on 10,000 Chinese document images using four state-of-the-art OCR models demonstrates consistent improvements across all tested systems, with precision gains ranging from 1.17% to 10.37% and overall Chinese character recognition accuracy exceeding 98%. The best performing model achieved 99.42% precision, with ablation studies confirming that feedback learning contributes additional improvements from 0.45% to 4.66% across different OCR architectures. Full article

Style transfer technology has seen substantial attention in image synthesis, notably in applications like oil painting, digital printing, and Chinese landscape painting. However, it is often difficult to generate migrated images that retain the essence of paper-cutting art and have strong visual appeal when trying to apply the unique style of Chinese paper-cutting art to style transfer. Therefore, this paper proposes a new method for Chinese paper-cutting style transformation based on the Transformer, aiming at realizing the efficient transformation of Chinese paper-cutting art styles. Specifically, the network consists of a frequency-domain mixture block and a multi-level feature contrastive learning module. The frequency-domain mixture block explores spatial and frequency-domain interaction information, integrates multiple attention windows along with frequency-domain features, preserves critical details, and enhances the effectiveness of style conversion. To further embody the symmetrical structures and hollowed hierarchical patterns intrinsic to Chinese paper-cutting, the multi-level feature contrastive learning module is designed based on a contrastive learning strategy. This module maximizes mutual information between multi-level transferred features and content features, improves the consistency of representations across different layers, and thus accentuates the unique symmetrical aesthetics and artistic expression of paper-cutting. Extensive experimental results demonstrate that the proposed method outperforms existing state-of-the-art approaches in both qualitative and quantitative evaluations. Additionally, we created a Chinese paper-cutting dataset that, although modest in size, represents an important initial step towards enriching existing resources. This dataset provides valuable training data and a reference benchmark for future research in this field. Full article

The integration of digital serious games into speech learning (rehabilitation) has demonstrated significant potential in enhancing accessibility and inclusivity for children with speech disabilities. This review of the state of the art examines the role of serious games, Artificial Intelligence (AI), and Natural Language Processing (NLP) in speech rehabilitation, with a particular focus on interaction modalities, engagement autonomy, and motivation. We have reviewed 45 selected studies. Our key findings show how intelligent tutoring systems, adaptive voice-based interfaces, and gamified speech interventions can empower children to engage in self-directed speech learning, reducing dependence on therapists and caregivers. The diversity of interaction modalities, including speech recognition, phoneme-based exercises, and multimodal feedback, demonstrates how AI and Assistive Technology (AT) can personalise learning experiences to accommodate diverse needs. Furthermore, the incorporation of gamification strategies, such as reward systems and adaptive difficulty levels, has been shown to enhance children’s motivation and long-term participation in speech rehabilitation. The gaps identified show that despite advancements, challenges remain in achieving universal accessibility, particularly regarding speech recognition accuracy, multilingual support, and accessibility for users with multiple disabilities. This review advocates for interdisciplinary collaboration across educational technology, special education, cognitive science, and human–computer interaction (HCI). Our work contributes to the ongoing discourse on lifelong inclusive education, reinforcing the potential of AI-driven serious games as transformative tools for bridging learning gaps and promoting speech rehabilitation beyond clinical environments. Full article

The digitisation of cultural heritage has transformed how GLAM (Galleries, Libraries, Archives and Museums) institutions manage and share collections. Digital catalogues are indispensable for documenting and granting public access to cultural assets. However, integrating spatial data remains challenging due to the ambiguity, uncertainty, granularity, and heterogeneity of historical data. This study addresses these issues through a case study on the Museo de América’s “Place of Provenance” data, proposing a methodology for data cleaning and evaluating geocoding accuracy using Nominatim, ArcGIS, and GeoNames APIs. We assess these APIs by quantifying geocoding errors through a “balance sheet” method, identifying instances of over-representation, under-representation, or neutral results for geographical regions. The effectiveness of each API is analysed using confusion matrices and interactive cartograms, offering insights into misallocations. Our findings reveal varying accuracy among the APIs in processing heterogeneous historical spatial data. Nominatim achieved a 40.91% neutral result in correctly geocoding countries, underscoring challenges in spatial data representation. This research provides valuable methodological experiences and insights for researchers and GLAM institutions working with cultural heritage datasets. By enhancing spatial dispersion visualisation, this work contributes to understanding cultural circulations and historical patterns. This interdisciplinary work was developed as part of the ClioViz project, integrating Data Science, data Visualisation, and art history. Full article

Digitalization has shaped every part of our world, from education to healthcare, from finance to entertainment, and from manufacturing to social interactions. This digital transformation has put tremendous power in the hands of individuals and delivered even better results than sticking to old, traditional ways. Such effective digitalization cannot be achieved easily; a good digitized system is the product of a lot of effort. In this paper, we explore the state-of-the-art advancements in smart education technologies, analyze existing digital solutions, and outline considerations for developing a tailored system to digitalize the Teaching at the Right Level (TaRL) assessment for Moroccan primary education. Our approach aims to bridge skill gaps by integrating serious games, adaptive learning techniques, and real-time analytics to enhance assessment effectiveness and ease educators’ workloads. Full article

In an era marked by climate change, socio-economic disparity, and digital acceleration, the challenge of effectively communicating complex environmental data to diverse audiences has become increasingly urgent. This study examines how data visualization and interactive art can be employed to translate scientific knowledge into engaging, accessible formats that raise public awareness and encourage climate action. We reviewed 495 environmental art and design projects and selected a corpus of 36 that focus on dynamic data visualisation and interactive experience design in response to climate and urban challenges. A multi-scalar, qualitative analysis was conducted to assess the thematic structure, technological strategies, and aesthetic dimensions of these projects. Our findings show that interdisciplinary methods—blending real-time data, machine learning, sonification, and immersive installations—are effective in rendering abstract climate data into emotionally resonant, actionable narratives. Through detailed case studies, we demonstrate how artistic interventions can facilitate public understanding, stimulate behavioural change, and support participatory urban adaptation. We conclude by highlighting the significance of collaborative, cross-sectoral approaches in designing future-oriented communication strategies for climate resilience, and by identifying future research pathways in the integration of environmental science, technology, and the arts. Full article

In the central Mesa Verde region, rock art occurs on canyon walls and on boulders that are frequently associated with other archaeological remains. Moreover, rock art, together with architecture and pottery, is actually a primary source of archaeological information about the presence of various cultures in the area. It includes paintings and petroglyphs of Ancestral Pueblo farming communities, images and inscriptions made by post-contact Ute and possibly Diné (Navajo) people as well as historical inscriptions of the early Euro-Americans in this area. This paper presents the results of archaeological investigations at four large rock art sites from Sandstone Canyon, southwestern Colorado, within the Canyons of the Ancients National Monument (CANM). Methods of rock art recording included advanced digital photography, photogrammetry, terrestrial laser scanning (TLS), hand tracing, and consultations with members of indigenous societies and rock art scholars. Geophysics and sondage excavations were conducted at one site revealed important information about archaeology, environment, and geology of the area. Analysis of rock art and other material evidence aims to help reconstruct and understand the mechanisms and nature of cultural changes, migrations, and human–environmental interactions and later cross-cultural contacts between indigenous peoples and Anglo-American ranchers and settlers in southwestern Colorado and the US Southwest. Full article

In contemporary research, the digital transformation of industries and societies has increased the importance of interdisciplinary exploration, particularly when addressing the complex challenges faced by modern organizations and social systems. From the perspective of digitalization, this literature review examines the intricate interactions between three key research domains: behavior dynamics, environmental impact, and fairness. By reviewing a wide range of studies and methodologies, it reveals new insights, challenges, and opportunities that arise at the intersection and through the interdependencies of these areas within digital ecosystems. Through a structured approach covering preliminary background, state-of-the-art methods, and comprehensive analysis, this document seeks to reveal the synergies and divergences among these domains. Special emphasis is placed on their implications in the digitalization of modern circular economy, business models, and supply chain management contexts where these domains converge in meaningful ways. Additionally, through an extensive review of the existing literature, this document highlights the current state of research and identifies notable gaps. These include issues such as ensuring fairness in digitalized sustainable strategies, understanding the role of digital behavior dynamics in promoting environmental management, and managing environmental impacts in new digitally driven business models. By weaving together these diverse elements, this work offers a novel perspective, emphasizing the importance of collaborative and integrative research in shaping a sustainable and equitable digital future. Full article

The paper explores the significance of digitalization and spatial modeling for the preservation and management of cultural heritage, addressing challenges posed by architectural complexity and extensive data volumes and developing a tailored data model to organize and integrate geometric, material, and historical information. The case study of Pitti Palace in Florence, Italy, is proposed, considering that its architectural complexity and cultural significance require innovative approaches to documentation and management. The “Pitti Data Model” is proposed as a tailored information system to organize and manage the data. It classifies spaces by adopting a hierarchical approach that supports detailed spatial analysis and reflects the historical and functional diversity of the site. The model links geometric data with thematic data such as material types, state of conservation, and historical names of spaces, providing a multi-dimensional understanding of the building. Based on Getty’s Art & Architecture Thesaurus (AAT), a controlled vocabulary was employed to ensure semantic consistency and interoperability. This semantic enrichment facilitates the integration of geometric data with broader heritage information systems. The paper presents, therefore, the integration in existing standards like INSPIRE, CityGML, and IndoorGML, thus providing a data model supporting efficient querying and visualization in a GeoDB that integrates spatial and non-spatial data, supporting collaborative and sustainable heritage management by enabling advanced analyses such as visitor flow optimization and conservation planning. This aligns with the concept of Heritage Digital Twins (HDT), which are interactive, evolving representations of cultural assets. HDTs support collaborative and sustainable heritage management by enabling stakeholders to access, analyze, and update information in real time. Full article

The Internet of Things (IoT) is a revolutionary advancement that automates daily tasks by interacting between digital and physical realms through a network of mostly Low-Power IoT (LP-IoT) devices. For an IoT ecosystem, reliable wireless connectivity is essential to ensure the optimal operation of LP-IoT devices, especially considering their limited resource capacity. This reliability is often achieved through channel estimation, an essential aspect for optimising signal transmission. Considering the importance of reliable channel estimation for constrained IoT devices, we developed two lightweight yet effective channel estimation models based on Random Forest Regressor (RFR). These two models are namely classified as Feature-based RFR(F) and Sequence-based RFR(S) methods and utilise Received Signal Strength Indicator (RSSI) as a fundamental channel metric to enhance efficiency for the reliability of channel estimation in constrained LP-IoT devices. The models’ performance was assessed by comparing them with the state-of-the-art and our previously developed Artificial Neural Network (ANN)-based method. The experimental results show that the RFR(F) method shows approximately 39.62% improvement in Mean Squared Error (MSE) over the Feature-based ANN(F) model and 37.86% advancement over the state-of-the-art. Similarly, the RFR(S) model shows an improvement in MSE of 24.9% compared to the Sequence-based ANN(S) model and an 80.59% improvement compared to the leading existing methods. We also evaluated the lightweight characteristics of our RFR(F) and RFR(S) methods by deploying them on Raspberry Pi 4 Model B to demonstrate their practicality for LP-IoT devices. Full article