Search Results (40)

Currently, exploring digital immersive experiences is a new trend in the innovation and development of cultural tourism. This study addresses the growing demand for digital immersion in cultural tourism by examining the integration of spatial narrative and digitally gamified architectural scenarios. This study synthesizes an optimized framework for narrative design in digitally gamified architectural scenarios, integrating spatial narrative theory and feedback-informed design. The proposed model comprises four key components: (1) developing spatial narrative design methods for such scenarios; (2) constructing a spatial language system for spatial narratives using linguistic principles to organize narrative expression; (3) building a preliminary digitally gamified scenario based on the “Wuhu Jiaoji Temple Renovation Project” after architectural and environmental enhancements; and (4) optimization through thermal feedback experiments—collecting visitor trajectory heatmaps, eye-tracking heatmaps, and oculometric data. The results show that the optimized design, validated in the original game Dreams of Jiaoji, effectively enhanced spatial narrative execution by refining both on-site and in-game architectural scenarios. Post-optimization visitor feedback confirmed the validity of the proposed optimization strategies and principles, providing theoretical and practical references for innovative digital cultural tourism models and architectural design advancements. In the context of site-specific architectural conservation, this approach achieves two key objectives: the generalized interpretation of architectural cultural resources and their visual representation through gamified interactions. This paradigm not only enhances public engagement through enabling a multidimensional understanding of historical building cultures but also accelerates the protective reuse of heritage sites, allowing heritage value to be maximized through contemporary reinterpretation. The interdisciplinary methodology promotes sustainable development in the digital transformation of cultural tourism, fostering user-centered experiences and contributing to rural revitalization. Ultimately, this study highlights the potential use of digitally gamified architectural scenarios as transformative tools for heritage preservation, cultural dissemination, and rural community revitalization. Full article

The COVID-19 pandemic severely impacted Japan’s inbound tourism, but recent recovery trends highlight the growing importance of Chinese tourists. Understanding their motivations is crucial for revitalizing the industry. Building on our previous framework, this study applies Transformer-based natural language processing (NLP) models and principal component analysis (PCA) to analyze large-scale user-generated content (UGC) and identify key motivational factors influencing Chinese tourists’ visits to Hokkaido. Traditional survey-based approaches to tourism motivation research often suffer from response biases and small sample sizes. In contrast, we leverage a pre-trained Transformer model, RoBERTa, to score motivational factors like self-expansion, excitement, and cultural observation. PCA is subsequently used to extract the most significant factors across different destinations. Findings indicate that Chinese tourists are primarily drawn to Hokkaido’s natural scenery and cultural experiences, and the differences in these factors by season. While the model effectively aligns with manual scoring, it shows limitations in capturing more abstract motivations such as excitement and self-expansion. This research advances tourism analytics by applying AI-driven methodologies, offering practical insights for destination marketing and management. Future work can extend this approach to other regions and cross-cultural contexts, further enhancing AI’s role in understanding evolving traveler preferences. Full article

This article introduces a freely available Spanish–Mixtec parallel corpus designed to foster natural language processing (NLP) development for an indigenous language that remains digitally low-resourced. The dataset, comprising 14,587 sentence pairs, covers Mixtec variants from Guerrero (Tlacoachistlahuaca, Northern Guerrero, and Xochapa) and Oaxaca (Western Coast, Southern Lowland, Santa María Yosoyúa, Central, Lower Cañada, Western Central, San Antonio Huitepec, Upper Western, and Southwestern Central). Texts are classified into four main domains as follows: education, law, health, and religion. To compile these data, we conducted a two-phase collection process as follows: first, an online search of government portals, religious organizations, and Mixtec language blogs; and second, an on-site retrieval of physical texts from the library of the Autonomous University of Querétaro. Scanning and optical character recognition were then performed to digitize physical materials, followed by manual correction to fix character misreadings and remove duplicates or irrelevant segments. We conducted a preliminary evaluation of the collected data to validate its usability in automatic translation systems. From Spanish to Mixtec, a fine-tuned GPT-4o-mini model yielded a BLEU score of 0.22 and a TER score of 122.86, while two fine-tuned open source models mBART-50 and M2M-100 yielded BLEU scores of 4.2 and 2.63 and TER scores of 98.99 and 104.87, respectively. All code demonstrating data usage, along with the final corpus itself, is publicly accessible via GitHub and Figshare. We anticipate that this resource will enable further research into machine translation, speech recognition, and other NLP applications while contributing to the broader goal of preserving and revitalizing the Mixtec language. Full article

This research addresses the time-consuming and error-prone nature of manual code compliance checking in Building Information Modeling (BIM) by introducing a Large Language Model (LLM)-driven approach to semi-automate this critical process. The developed system integrates LLMs such as ChatGPT, Claude, Gemini, and Llama, with Revit software to interpret building codes, generate Python scripts, and perform semi-automated compliance checks within the BIM environment. Case studies on a single-family residential project and an office building project demonstrated the system’s ability to reduce the time and effort required for compliance checks while improving accuracy. It streamlined the identification of violations, such as non-compliant room dimensions, material usage, and object placements, by automatically assessing relationships and generating actionable reports. Compared to manual methods, the system eliminated repetitive tasks, simplified complex regulations, and ensured reliable adherence to standards. By offering a comprehensive, adaptable, and cost-effective solution, this proposed approach offers a promising advancement in BIM-based compliance checking, with potential applications across diverse regulatory documents in construction projects. Full article

Aiming at problems such as difficulty and low efficiency in modeling complex building structures in Revit and Abaqus, Revit and Dynamo visual programming modeling were used to realize rapid parametric modeling of structures, and a Revit–Abaqus model conversion interface was developed. Based on Revit’s API modeling function and Dynamo’s visual programming function, a proton hospital structure model was quickly created, secondary development of Revit conducted on Visual Studio 2022 using C# language, the Revit model exported to a single unified management .sat file, and a corresponding Python script file conforming to Abaqus generated. The script file contained all the .sat file path and parameter information to realize the conversion of the BIM model to Abaqus 2020 finite element software. The proposed modeling method can effectively expand the application range of BIM in a proton hospital project and effectively improve the efficiency of BIM modeling technology. The Revit–Abaqus model conversion interface developed can realize the automatic conversion of a BIM model to a structural analysis model. Compared with the traditional finite element modeling method, this effectively improves the modeling efficiency of structural analysis of proton hospital engineering and makes up for the shortcomings of BIM technology in structural analysis of proton hospital construction. Full article

Revitalizing Japan’s remote areas has become an urgent challenge, particularly in regions with aging populations. Despite their rich cultural and natural resources, these areas struggle to attract younger demographics, including young families and children. To address this, local governments have introduced temporary events to enhance urban vibrancy and create inclusive spaces. However, research on optimizing event design faces significant challenges due to the vast amount of data required for comprehensive analysis, making it difficult to gain deeper insights into user experience. Recent advancements in natural language processing (NLP) and AI have opened new possibilities for analyzing large-scale, multi-person interview data. While models like ChatGPT-4 have enhanced data-driven decision-making, structuring user metadata and identifying shared themes across events remain key challenges. This research integrates visual segmentation, spatial perception analysis, and NLP-driven keyword extraction into a novel, scalable approach. Using Matsue City as a case study, the method enhances the visual attractiveness of temporary event spaces by optimizing spatial layout, product visibility, and user engagement, ensuring they remain appealing and inclusive despite demographic challenges. From a data perspective, the proposed model improves the analysis of complex qualitative datasets and supports a more accurate interpretation of public event experiences. This integrated approach not only bridges spatial design and participant engagement but also establishes a replicable AI-assisted framework for systematically enhancing temporary event spaces, overcoming current limitations in large-scale data processing. Full article

Religious places represent one of the most significant categories of protected heritage. In Italy, however, places of worship belonging to minority communities often remain inconspicuous and are not legally recognized as part of the nation’s cultural heritage. Consequently, the histories of these communities face challenges in securing a space within the collective memory. This contribution, through a spatial approach and an interdisciplinary methodology, highlights the richness of the hidden heritage—both tangible and intangible—of the Greek Orthodox Church of the Nativity of St. John the Baptist in Turin. In particular, this research explores the role of the Greek language, which constitutes a significant element of intangible heritage for the community. Since the 1960s, regular celebrations in the Byzantine rite and the Greek language have been held in the Piedmontese capital. These biritual practices emerged in response to the demands of numerous Greek university students and families who revitalized the Orthodox presence in the territory during those years. In 2000, the Catholic Archdiocese granted the Greek Orthodox community the use of a church in the city’s historic center. This church is interpreted as a shared religious space, having undergone a transformation of identity over time: its Orthodox identity remains architecturally invisible, as the community continues to worship in a former Catholic church. Full article

This paper explores the crucial role of Cree elders’ knowledge in revitalizing Indigenous food sovereignty, focusing on food as a cornerstone of Cree identity, spirituality, and wellbeing. Based on a study of Cree elders in Manitoba in 2020, this paper highlights the depth of Cree food knowledge, intertwined with spiritual practices, language, and land ethics. Using an Indigenous research paradigm, ten Cree elders were interviewed and shared their experiences of traditional communal practices, the detrimental impacts of colonialism on food systems, and the spiritual connections between food, land, and community. The elders emphasized the need for education and the preservation of Cree languages, which encode critical knowledge for sustaining food practices. Through their stories, elders illustrated how food sovereignty is not merely about physical sustenance but involves maintaining sacred relationships and responsibilities to the land and all its inhabitants. This research underscores the importance of Cree knowledge in reclaiming and sustaining Indigenous food systems, essential for the health and resilience of Cree communities. Full article

This article addresses the relationship between worldview, territory, and language among the Mapuche people. Our research aims to explore, through the voices of Mapuche elders, the sociocultural, political, and territorial knowledge that guides the teaching and transmission of Mapuzugun to new generations. In alignment with the proposed objective, we have adopted a socio-critical approach to explore Mapuche identity through the territorial and communal dimensions that underpin linguistic revitalization. The findings reveal that the elders possess a vast source of knowledge, serving as primary cultural bearers through epistemology centered on the unity of language and territory. Full article

Through fieldwork conducted between 2014 and 2016 in Arkhangelsk, Naryan-Mar, Krasnoye, and Saint Petersburg, this paper investigates the endangered status of Tundra Nenets, an underrepresented and understudied Samoyedic minority language in northern Russia. Criteria for assessing language endangerment are applied to Tundra Nenets while also taking into consideration the interplay between language identity, reactive ethnicity, negative attitudes, and state politics. The personal story of NC, a Tundra Nenets woman, serves as a case study and exemplifies the impact of decades of marginalization, stigmatization, and discrimination on the cultural and linguistic identity. NC’s narrative illustrates how negative attitudes are exacerbating the decline of Tundra Nenets, further threatening its survival. Because of its absence from schools and institutions, Tundra Nenets seems to be turning into a heritage language in its own homeland. This paper studies the complex interplay between identity, language, and societal pressures, illustrating the broader challenges faced by the Tundra Nenets and other minority communities in maintaining their linguistic and cultural heritage. While the situation remains dire and political action is called for, efforts in boosting language awareness, documentation, and revitalization offer potential pathways for the preservation of Tundra Nenets, drawing on successful examples from other endangered language communities. Full article

Language loss hinders the expression of Indigenous Peoples and their unique worldviews, impairing the intergenerational transfer of knowledge. In Hawai‘i, where a vast majority of the population was fluent and “universally literate” in ‘Ōlelo Hawai‘i from the mid to late 1800s, colonial impositions drastically reduced the number of fluent speakers to roughly 2000 by the 1970s. Efforts to revitalize the language since then have greatly increased the number of current ‘Ōlelo Hawai‘i speakers and resources. Building upon this great work, the Hā Kūpuna National Resource Center for Native Hawaiian Elders at the University of Hawai‘i has initiated projects to contribute to the reclamation of ‘Ōlelo Hawai‘i by increasing our contemporary understanding of ancestral Hawaiian perspectives on elders. To support these projects, significant changes in power structures within our organization were necessary. Insights gained from these projects include gaining clarity on the evolution of the usage of the word “kupuna”, identifying more nuanced perspectives on elders, understanding the importance of family relationships on caregiving outcomes, and understanding the importance of carefully translating English words into ‘Ōlelo Hawai‘i. Full article

There is an increasing number of children in the U.S. classified as English Learners (ELs). Accurately identifying and supporting ELs in their academic settings entails understanding their non-English language experiences. This study presents findings from language reporting practices from seven U.S. states by examining how states account for the linguistic diversity of Indigenous Mesoamerican languages. Our findings reveal varied state approaches and underscore the limitations of current federal guidance, which limits the recognition of students’ non-English language experiences. We advocate for updating language identification practices and policies and propose a new framework for accurate language identification and continuous monitoring of student linguistic diversity. Full article

This study introduces DAVE (Digital Assistant for Virtual Engineering), a Generative Pre-trained Transformer (GPT)-powered digital assistant prototype, designed to enable real-time, multimodal interactions within Building Information Modeling (BIM) environments for updating and querying BIM models using text or voice commands. DAVE integrates directly with Autodesk Revit through Python scripts, the Revit API, and the OpenAI API and utilizes Natural Language Processing (NLP). This study presents (1) the development of a practical AI chatbot application that leverages conversational AI and BIM for dynamic actions within BIM models (e.g., updates and queries) at any stage of a construction project and (2) the demonstration of real-time, multimodal BIM model management through voice or text, which aims to reduce the complexity and technical barriers typically associated with BIM processes. The details of DAVE’s development and system architecture are outlined in this paper. Additionally, the comprehensive process of prototype testing and evaluation including the response time analysis and error analysis, which investigated the issues encountered during system validation, are detailed. The prototype demonstrated 94% success in accurately processing and executing single-function user queries. By enabling conversational interactions with BIM models, DAVE represents a significant contribution to the current body of knowledge. Full article

In the field of Natural Language Processing (NLP), the lack of support for minority languages, especially Uyghur, the scarcity of Uyghur language corpora in the agricultural domain, and the lightweight nature of large language models remain prominent issues. This study proposes a method for constructing a bilingual (Uyghur and Chinese) lightweight specialized large language model for the agricultural domain. By utilizing a mixed training approach of Uyghur and Chinese, we extracted Chinese corpus text from agricultural-themed books in PDF format using OCR (Optical Character Recognition) technology, converted the Chinese text corpus into a Uyghur corpus using a rapid translation API, and constructed a bilingual mixed vocabulary. We applied the parameterized Transformer model algorithm to train the model for the agricultural domain in both Chinese and Uyghur. Furthermore, we introduced a context detection and fail-safe mechanism for the generated text. The constructed model possesses the ability to support bilingual reasoning in Uyghur and Chinese in the agricultural domain, with higher accuracy and a smaller size that requires less hardware. It (our work) addresses issues such as the scarcity of Uyghur corpora in the agricultural domain, mixed word segmentation and word vector modeling in Uyghur for widespread agricultural languages, model lightweighting and deployment, and the fragmentation of non-relevant texts during knowledge extraction from small-scale corpora. The lightweight design of the model reduces hardware requirements, facilitating deployment in resource-constrained environments. This advancement promotes agricultural intelligence, aids in the development of specific applications and minority languages (such as agriculture and Uyghur), and contributes to rural revitalization. Full article

This study explores the linguistic landscape (LL) of three Hispanic neighborhoods in Philadelphia, PA, aiming to document and measure the presence of the Spanish language in public spaces and understand the influence of time, location, and establishment type on language use. Based on 3437 signs analyzed from 2021 to 2023, our findings reveal that English dominates the LL at 61.65%, while Spanish constitutes 24.16%. The study employs chi-squared tests to confirm the impact of time and location on language use, highlighting a rise in bilingual and monolingual Spanish signs over time. Additionally, variables were combined into clusters using a heatmap to explore language use across different establishments. Bilingualism emerges as a sustained trend, underscoring inclusivity and linguistic diversity within these contexts. Location analysis exposes unique linguistic characteristics in each neighborhood, reflecting the cultural and linguistic diversity of their communities. The Golden Block exhibits bilingual prevalence, indicative of evolving demographics. Olney showcases language mixtures due to diverse ethnic and sociolinguistic influences, while South Philadelphia’s Italian Market area features prevalent Spanish and multilingual signage. The study underscores the growing presence of Spanish and minority languages, emphasizing the need to recognize and revitalize linguistic diversity in urban spaces. As cities evolve, continued exploration of the LL is crucial to understanding language dynamics in relation to identity, culture, and power. Full article