Search Results (11,989)

Adaptive reuse of industrial heritage from a tourism perspective presents a complex design challenge requiring a balance between heritage preservation, functional innovation, and diverse stakeholder expectations. However, current practices often face issues such as ambiguous demand interpretation and a disconnect between design generation and systematic evaluation. Addressing these limitations, this paper proposes and illustrates a human–machine collaborative design paradigm that integrates generative AI into a closed-loop process of “demand analysis–intelligent generation–comprehensive evaluation.” The method first employs Kansei Engineering and the KANO model to qualitatively extract and quantitatively prioritise heterogeneous user needs, translating subjective perceptions into structured design constraints and optimisation objectives. Next, these needs are encoded as text prompts to drive targeted spatial exploration by the generative AI tool Nano Banana AI. Finally, the TOPSIS method is applied for multi-criteria performance evaluation and solution selection. A case study of Shanghai Libo Brewery suggests that this paradigm can enhance design efficiency and show potential to outperform traditional methods across dimensions such as historical preservation, public accessibility, ecological integration, social inclusivity, and formal innovation. The research offers a quantifiable and systematically documented intelligent design methodology for industrial heritage renewal, while acknowledging the exploratory nature of the generative phase. Furthermore, it provides a visitor-demand-driven innovation pathway for developing industrial heritage tourism destinations, thereby potentially enhancing cultural experiences and tourism appeal at heritage sites. This research illustrates a move from an experience-driven paradigm toward a data- and value-driven approach, contributing theoretical methodologies to the intersection of cultural tourism and artificial intelligence. Full article

Rapid renewal and tourism-driven commercialization intensify tensions between heritage conservation and redevelopment in historic districts, and decision-oriented tool chains that translate Historic Urban Landscape (HUL) layering into change management remain limited. Taking Chengdu’s Kuanzhai Alley and Daci Temple historic districts as comparative cases, this study traces four benchmark time slices (1911, 1933, 1994, and 2025) using georeferenced historical maps, remote-sensing imagery, planning base maps, archival documents, and field checks. An auditable morphological-evidence coding manual is developed for street–alley skeletons, plot integrity, redevelopment intensity, interface commodification, connectivity, and heritage-anchor integrity, and it is triangulated with resident-population and commercial-mix evidence to interpret regeneration mechanisms. The results show that morphological continuity can coexist with social discontinuity. Kuanzhai Alley retains a legible street–alley backbone, while plot/operational consolidation and intensive commodification coincide with resident withdrawal. The Daci Temple district experiences broader street–plot reconfiguration and upscale clustering that heightens landmark visibility but challenges contextual integrity and community continuity. Based on these mechanisms, four renewal zoning prototypes and zone-specific monitoring indicator domains are proposed to operationalize HUL’s feedback loop and to support balanced governance of heritage, everyday life, and sustainable urban heritage tourism. Full article

Against the background of promoting coordinated development between conservation and activation, this paper examines the Qingming Bridge Historic District in Wuxi. Drawing on theories of urban morphology and spatial configuration, the study classifies the district into multiple spatial categories. To quantify the relationship between heritage attributes and functional vitality, we propose a “Spatial Value–Spatial Performance” dual-dimensional evaluation framework. Methodologically, this framework integrates space syntax to extract spatial potential, entropy weighting to determine objective indicators, and the Coupling Coordination Degree (CCD) model to measure systemic interactions. Furthermore, spatial units are classified into three distinct typologies: Performance-Lagging, Value-Lagging, and Low-Level Synchronous. In response to the imbalance in the results from the quantitative findings, this paper proposes targeted renewal strategies. The core innovation provides a replicable quantitative tool, offering both empirical evidence and methodological references for the sustainable conservation and regeneration of historic districts globally. Full article

The digitization of tourism has made numerous platforms available, but there remains a significant shortage of tools capable of promoting local events and activities. This study hypothesizes that a decentralized digital interface can mitigate over-tourism. We conducted an experiment by deploying a digital platform to assess the synergy between local providers and visitors through the Tourism Open-ended Experimentation Platform (TOEP), a multi-interface solution designed to directly connect tourism activity providers with residents and visitors. The platform integrates a web portal for providers and a mobile app for users, supported by a recommendation system based on individual profiles and preferences. TOEP stands out for its focus on local and sustainable tourism, facilitating the promotion of smaller events and helping to reduce the concentration of tourist flows in already saturated destinations. Initial validation, conducted with a panel of industry experts, highlighted the ease of use and good organization of the interfaces, with scores above average. Preliminary results confirm the relevance of TOEP as a tool for the sustainable and digitized promotion of local tourism, opening prospects for development towards a smart, participatory tourism ecosystem that can be replicated in different territorial contexts. Full article

This study explores Serbia’s rich ethnopharmacological heritage by systematically documenting the traditional use of medicinal plants for treating skin diseases during the 19th and 20th centuries. Drawing on key ethnographic sources—including monographs, scholarly articles, and field reports—the review analyzes historical records of folk medicine practices and their cultural contexts. A total of 164 plant species from 63 botanical families, as well as one mushroom species, were identified as being used in the treatment of skin-related conditions classified according to the International Classification of Primary Care. Reported ailments were grouped into three main categories: hair and scalp disorders, bites, and various inflammatory skin conditions such as eczema and psoriasis. Remedies for wound healing were the most frequently documented, both in terms of application and diversity of plant species employed. By preserving and systematizing this historical knowledge, the study provides a valuable foundation for future pharmacological and dermatological research, highlighting the continued relevance of traditional remedies in modern clinical practice. Full article

This study provides a comparative evaluation of three state-of-the-art large language models (LLMs), namely OpenAI’s (San Francisco, CA, USA) GPT-4.0, Google’s Google LLC, Mountain View, CA, USA) Gemini 2.0 Flash, and Meta’s (Meta Platforms, Menlo Park, CA, USA) LLaMA-4-Scout-17B-16E, in a decision-oriented framework in which the models generate structured outputs based only on historical closing-price data. The evaluation covers 150 stocks sampled from three countries (India, the United States, and South Africa) across ten economic sectors, including Information Technology, Banking, and Pharmaceuticals. Unlike many prior studies that combine numerical and textual inputs, this study relies solely on three years of numerical time series data and examines model responses in terms of decision labels such as buy, sell, or hold. The LLMs were provided with historical closing-price sequences and prompted with three types of finance-related questions: (a) whether to buy a stock, (b) whether to sell or hold a stock, and (c) in a pairwise comparison, which stock to buy or hold. These prompts were evaluated across two investment horizons: 1 month and 3 months. Model outputs were compared against realized market outcomes during the corresponding test periods. Performance was assessed across four key dimensions: country, sector, annualized volatility, and question type. The models were not given any supplementary financial information or instructions on specific analytical methods. The results indicate that GPT-4.0 achieves the highest average accuracy (56%), followed by LLaMA-4-Scout-17B-16E (48%) and Gemini 2.0 Flash (39%). Overall performance remains moderate and varies across market conditions, with relatively higher accuracy observed in high-volatility regimes (51%). This work evaluates how LLMs behave when presented with structured numerical price sequences in a controlled decision-labeling setting and contributes to the broader discussion on the potential and limitations of LLMs for numerical decision tasks in finance. Full article

The viae romanae (Roman roads) were constructed according to precise designs and exceptional engineering techniques, ensuring their strength and durability. They represent an immeasurably important factor in human history. Their impact has been universal, facilitating the movement of people, goods, ideas, beliefs and religions over the centuries. The Via Appia Regina Viarum, built between the end of 4th and 1st centuries BCE, connected Rome to Brundisium, spanning the region of Latium and Apulia. The road initially crossed the coastal plains of the Tyrrhenian Sea (in Latium) before cutting through the reliefs and river valleys of the southern Apennines (in Campania) and finally crossing the regio Apulia et Calabria via Tarentum, to the harbor of Brundisium, along the Adriatic coast. In 2024, the Italian Ministry of Culture proposed the ‘Via Appia Regina Viarum’ for inscription on the Unesco World Heritage List, recognizing its unique and exceptional testimony to Roman civilization. Later that same year, the nomination was accepted, and today, the Via Appia is part of the UNESCO World Heritage List. A significant contribution to this nomination came from the multidisciplinary studies and research conducted along the Via Appia between the ancient cities of Beneventum and Aeclanum in the Campanian Apennine, including: (1) geoarcheological investigation aimed at identifying the ancient path of the road, which was not well documented in the area between Beneventum and Aeclanum; (2) studies focused on cultural and geological heritage along the road and its surrounding landscapes, enhancing the value of the nomination; and (3) the organization of social and cultural events designed to disseminate scientific findings and raise awareness among scientists, students, local and national administrators, local food and wine producers, and the general public. This paper highlights the pivotal role of geoscience at all stages of the project: from preliminary field surveys and mapping of landforms and lithofacies, to targeted field and geophysical surveys, to archaeological excavation and geoarchaeological consideration, and to the dissemination of new data through cultural events. Full article

In recent years, Visual Education has emerged as an innovative and interdisciplinary teaching approach aimed at promoting meaningful learning through the conscious use of visual tools and languages. This educational paradigm helps to facilitate the understanding of complex concepts, translating them into clear and intuitive visual representations, while enhancing memorisation skills, critical information processing and the practical application of acquired knowledge. This systematic review, conducted according to the PRISMA (2020) protocol, analyses the most recent empirical evidence on the effectiveness of Visual Education in educational contexts. The main objective is to assess how the intentional use of visual tools—images, concept maps, educational videos, interactive digital materials, and virtual manipulatives—contributes to enhancing learning processes and developing transversal skills. Through a comparative analysis of fourteen international contributions published between 2020 and 2025, selected from the Scopus, Web of Science and EBSCO databases, the research highlights how Visual Education significantly influences the improvement of academic performance, motivation and cognitive and emotional engagement of students. The results also confirm the inclusive function of visual teaching, which can encourage participation, self-esteem and cooperation even in individuals with special educational needs. The discussion emphasises the need for the systematic integration of Visual Education into school curricula as a strategy to enhance soft skills and promote more equitable, effective learning geared towards the integral development of the individual. Full article

The accurate interpretation of structural edges requires distinguishing geometry-driven discontinuities from reflectance- and illumination-induced variations. Conventional photogrammetric pipelines rely primarily on radiometric and geometric cues, which often lack physical interpretability under complex material and lighting conditions. This study proposes a photogrammetry–polarimetry fusion framework for physics-guided semantic classification of 3D structural edges. Radiometric, geometric, and polarimetric features are integrated within a noise-normalized representation to enable modality-independent interpretation. A rule-based classification scheme is introduced to assign edges to physically meaningful categories, including geometric, material, specular, illumination, and polarization-driven phenomena. The method is evaluated on a calibrated geometric object and a cultural heritage statue. Results show that polarization provides complementary information that reduces ambiguity between geometry-driven and reflectance-driven edge responses while preserving the underlying reconstructed geometry. On the calibrated dataset, edge detection achieves 88.4% precision, 95.5% recall, and an F1-score of approximately 0.92. Multi-view integration further improves the completeness of geometry-dominant 3D edges. The proposed framework introduces a physics-guided semantic sensing layer for multi-modal 3D perception, enabling more robust and interpretable structural analysis in photogrammetric workflows. Full article

High-resolution Köppen–Geiger projections indicate that several cold desert (BWk) regions are likely to transition toward hot desert (BWh) regimes during the twenty-first century, challenging the environmental logic of vernacular architecture. Despite extensive simulation-based research on passive cooling in established BWh contexts, limited attention has been given to climate-type transition zones and to the morphological continuity of traditional housing systems. This study investigates the adaptive capacity of Bukhara’s courtyard houses under projected BWk–BWh reclassification. Employing an analytical generalization approach, the research integrates systematic literature mapping, typological morphological analysis, and a threshold-based compatibility matrix. Findings reveal that climate transition produces a form of morphological discontinuity by weakening diurnal discharge assumptions embedded in high thermal mass systems. However, courtyard typologies retain a resilient passive core when recalibrated through microclimatic amplification strategies. The proposed staged adaptation framework contributes a heritage-sensitive decision model that reconciles climatic performance with spatial integrity, offering transferable guidance for cli-mate-intensifying desert regions. Full article

Public deployment of generative AI in cultural institutions is constrained by small, rights-restricted datasets, strict latency and runtime-stability requirements, and limits on visitor-data collection. This study presents a deployment-oriented framework for adapting a pre-trained text-to-image diffusion foundation model to a heritage-specific visual domain using Low-Rank Adaptation (LoRA). A Stable Diffusion v1.5 backbone is specialized through data-centric curation and LoRA fine-tuning, then served through an asynchronous edge architecture that links a Unity client and a local Python (version 3.10) inference server for public-facing operation on a native 400 × 1080 vertical canvas. To support deployment decisions without collecting personally identifiable information, the system records only anonymous operational logs and evaluates sustained-load behavior under repeated inference. In a 1000-iteration profiling test, the proposed configuration maintained stable runtime behavior without observable upward memory drift, with a peak allocated VRAM of 3.04 GB and an average end-to-end latency of 3.12 s. An 8 h field deployment further indicated service continuity under public interaction, while a CLIP-based proxy analysis under matched prompts and seeds suggested improved relative style controllability after adaptation (0.848 vs. 0.799). Rather than claiming cultural authenticity or visitor-level effects, this study offers a data-centric, deployment-oriented methodology for operating public-facing generative AI under small-data, latency, and privacy constraints. Full article

In the context of digital tourism development, artificial intelligence has become one of the major techniques for tourists’ information acquisition and interaction in the field of intangible cultural heritage (ICH) tourism. However, whether AI with anthropomorphism elements attracts tourists to visit cultural heritage sites and how AI anthropomorphism design affects visitors’ visit intentions remains unclear. Therefore, based on the stimulus–organism–response (S–O–R) theory, this study proposes an “AI anthropomorphism–AI trust–visit intention” model and investigates the role of AI anthropomorphism in visit intention. In particular, this study tests the effects of perceived intelligence and perceived risk on AI anthropomorphism, as well as the role of AI trust and perceived cultural sustainability on the relationship between AI anthropomorphism and visit intention. With a sample of 478 Chinese respondents who are intangible cultural heritage (ICH) tourists, the hypothesized relationships are tested by employing structural equation modeling. The results show that perceived intelligence exerts a positive effect on AI anthropomorphism, while perceived risk exerts a negative effect on AI anthropomorphism. Moreover, AI anthropomorphism exerts an effect on AI trust, which in turn yields a great influence on visit intention. In addition, further analysis shows that AI type intensifies the effect of anthropomorphism on AI trust, and the relationship between AI trust and visit intention is regulated by perceived cultural sustainability. This study reveals how AI anthropomorphism functions in ICH tourism, and the findings provide practical guidance for advancing intelligent services and giving cultural sustainability top priority in order to support the sustainable growth of ICH tourism. Full article

The importance of heritage in global development is a well-established fact, and according to UNESCO the preservation and protection of the cultural and natural heritage of the world should be among the priorities of the global community. This is necessitated by the fact that heritage has been continuously destroyed over time by war and terrorism, modernisation, urbanisation, and climate change. World Heritage sites are global treasures of unique value, and their protection requires efforts on the international scale. Countries may raise resources for maintaining and preserving their endangered World Heritage sites, and for those inscribed on the List of World Heritage in Danger they may apply for international assistance from UNESCO as well. Case studies and examples show that economic affluence and state stability considerably influence the conservation of World Heritage sites. This research investigates the correlation between national economic development and the conservation status of UNESCO World Heritage sites. By analysing data from 31 countries as homes of sites inscribed on the List of World Heritage in Danger, this study examines the relationship between GDP per capita, the volume of international assistance, and the “In Danger” status of sites, adding control factors such as the fragile state index, encompassing threats of war and internal conflicts, and extreme climate events. To the author’s best knowledge, no such comparative assessment between development level and international assistance to sites inscribed on the list has been attempted before, so this quantitative analysis is a novel approach of the outlined problem area. The findings aim to demonstrate whether the current global heritage framework effectively supports lower-income nations or if financial disparity remains the primary driver of heritage loss in the face of global climate change and political factors. The main research question is whether the socio-economic status of a nation will remain a stronger predictor of heritage survival than the actual severity of environmental threats, maintaining a “heritage divide” between the Global North and the Global South. This research concludes with recommendations for a more equitable financial model to safeguard universal values in an age of increasing uncertainty. Full article

The conservation of traditional villages has shifted from isolated site-by-site protection to regional collaboration, and exploring pathways for their sustainable development has become a key focus of research. Existing research still falls short in areas such as the integration of heritage value into decision-making mechanisms and the establishment of systematic conservation frameworks, leading to prominent issues of isolated conservation and homogeneous development. Taking traditional villages in Yanchuan County, China, as a case study, this research aims to establish a clustered conservation system and achieve a transition towards networked collaborative governance. The study utilised field surveys and literature review to establish a database and systematically catalogue heritage resources; it combined the Analytic Hierarchy Process (AHP) and the Delphi method to construct a value evaluation system and identify distinctive features; and it integrated cluster theory with GIS spatial analysis to construct a clustered conservation framework across three dimensions: classification and grading, symbiotic models, and the overall spatial pattern. The results indicate that: (1) the spatial distribution of villages in Yanchuan County is uneven, and the villages themselves exhibit significant homogeneity in their characteristics; (2) core characteristics include Loess culture, cave dwellings and revolutionary heritage sites, with comprehensive scores ranging from 0.4437 to 0.9116; these are classified into three protection levels, identifying five categories of villages of value. (3) Five major cluster zones were delineated based on resource and spatial characteristics. By integrating river basins and transport corridors, a comprehensive protection framework of ‘one belt, two wings, two centers and five zones’ was established, alongside three types of cluster symbiosis models, thereby achieving regional resource integration and enhancing collaborative efficiency. The cluster-based protection system proposed in this study can effectively address the challenges facing the conservation and development of traditional villages, providing a feasible solution for regional collaborative protection, and holds practical significance for cultural heritage management and sustainable development. Full article

The extensive utilisation of chemical fertilisers and pesticides in agricultural contexts has precipitated substantial environmental degradation, thereby amplifying the repercussions of climate change. Furthermore, this overuse poses a threat to the sustainability and resilience of global food production systems. The utilisation of microalgae-based biostimulants is a novel and sustainable approach that has the potential to enhance crop productivity and resilience, while reducing dependence on chemical pesticides and their negative effects. The present study evaluated the effectiveness of two novel microalgae-based formulations on the performance of processing tomato (Solanum lycopersicum) crops under field conditions in Spain and Portugal. The formulation comprised enzymatically hydrolysed biomass from L. platensis, N. gaditana and A. obliquus, in combination with olive mill wastewater (alpechin) and aromatic plant extracts. The mixture was applied through drip irrigation and foliar spraying. The application of combined foliar and drip treatments resulted in a substantial enhancement in gross yield up to 51.9%. Concurrently, the acceptable raw material yield demonstrated a notable increase up to 44.9%. Furthermore, an increase in average fruit weight by 2–9 g was recorded. A subsequent foliar nutrient analysis revealed elevated concentrations of N, P, K, Ca, Mg, Fe, and Cu in the plants treated with biostimulants, achieving 3.61, 52.94, 5.96, 36.53, 22.28, 60.41 and 71.32% respectively in the plot L4 with foliar treatment. Although the efficacy of pest control measures was slightly lower than that of conventional pesticides, no significant increase in the incidence of diseased was observed. These findings indicated that microalgae-based biostimulants have the potential to function as sustainable agricultural inputs capable of enhancing crop yields and quality while reducing dependence on chemical fertilisers and pesticides. The outcomes of the study demonstrate the efficacy of microalgae-based formulations in enhancing the yield and quality of tomato crops. This is achieved while maintaining optimal plant health and reducing the reliance on synthetic fertilisers and pesticides. Full article