Search Results (236)

This article examines training itineraries for early childhood education professionals in Portugal, focusing on promoting social and human development through music and the arts for infants. The training models discussed are categorized as short-term and long-term, encompassing both theory and practice through a transdisciplinary approach. Based on initiatives promoted by the Companhia de Música Teatral (CMT) and the Education and Human Development Group of the Centre for the Study of Sociology and Musical Aesthetics (CESEM) at NOVA University Lisbon, the article highlights projects such as: (i) Opus Tutti and GermInArte, developed between 2011 and 2018; (ii) the Postgraduate Course Music in Childhood: Intervention and Research, offered at the University since 2020/21, which integrates art, health, and education, promoting collaborative work between professionals; and (iii) Mil Pássaros (Thousand Birds), developed since 2020, which exemplifies the integration of environmental education and artistic practices. The theoretical basis of these training programs combines neuroscientific and educational evidence, emphasizing the importance of the first years of life for integral development. Studies, such as those by Heckman, reinforce the impact of early investment in children’s development. Edwin Gordon’s Music Learning Theory and Malloch and Trevarthen’s concept of ‘communicative musicality’ structure the design of these courses, recognizing music as a catalyst for cognitive, emotional, and social skills. The transformative role of music and the arts in educational and social contexts is emphasized, in line with the Sustainable Development Goals of the 2030 Agenda, by proposing approaches that articulate creation, intervention, and research to promote human development from childhood onwards. Full article

As the effective founder of Daoism’s Southern School, Bai Yuchan’s travels played a pivotal role in the sect’s early dissemination. Through a close analysis of his poems, prose, and letters, this study reconstructs the key itineraries and motivations of Bai Yuchan’s travels and examines how his itinerant practices shaped the early dissemination of the Southern School. His travels were divided into two phases: a pre-1212 period of Dao-seeking and a post-1216 phase of Dao-spreading, with the impetus for his later journeys arising from resolving internal alchemical cultivation dilemmas, which in reality, inaugurated his career of traveling to spread the Dao. Bai Yuchan established and disseminated the Southern School through sojourns and revisitations in various regions, with karmic opportunity (jiyuan 機緣) largely dictating the selection of sojourn locations during his journeys. Rooted in the Daoist philosophy of harmony, Bai Yuchan adhered to the principle of blending with the mundane while harmonizing one’s light (hunsu heguang 混俗和光) in his travels and interactions, maintaining active engagement within regional areas to foster harmonious relationships with local communities. This explains why Bai Yuchan was able to achieve the widespread dissemination of the Southern School through his itinerant activities over a short period. Full article

A Sanskrit manuscript of the Prajñāpāramitā or Perfection of Wisdom in eight thousand verses, now in the Cambridge University Library, Add.1643, is one of the most ambitiously designed South Asian manuscripts from the eleventh century, with the highest number of painted panels known among the dated manuscripts from medieval South Asia until 1400 CE. Thanks to the unique occurrence of a caption written next to each painted panel, it is possible to identify most images in this manuscript as representing those of famous pilgrimage sites or auspicious images of specific locales. The iconographic program transforms Add.1643 into a portable device containing famous pilgrimage sites of the Buddhist world known to the makers and users of the manuscript in eleventh-century Nepal. It is one compact colorful package of a book, which can be opened and experienced in its unfolding three-dimensional space, like a virtual or imagined pilgrimage. Building on the recent research focusing on early medieval Buddhist sites across Monsoon Asia and analyzing the representational potentials and ontological values of painting, this essay demonstrates how this early eleventh-century Nepalese manuscript (Add.1643) and its visual program document and remember the knowledge of maritime travels and the transregional and intraregional activities of people and ideas moving across Monsoon Asia. Despite being made in the Kathmandu Valley with a considerable physical distance from the actual sea routes, the sites remembered in the manuscript open a possibility to connect the dots of human movement beyond the known networks and routes of “world systems”. Full article

The need to promote proximity tourism in natural areas has made the destinations in the interior of the Iberian Peninsula a benchmark in the development of tourism in traditionally rural and depressed areas. In Ciudad Real province, a sustainable territorial management project was developed based on the uniqueness of its volcanic geoheritage, leading to its designation as the UNESCO Global Geopark “Volcanes de Calatrava, Ciudad Real”. Geoparks promote sustainable activities such as geotourism; thus, an urban geotourism itinerary was proposed in Piedrabuena (Ciudad Real, Castilla-La Mancha) in order to make the natural and cultural geoheritage of a part of the geopark known, which is also a part of the Campo de Calatrava Volcanic Region. For this purpose, 12 natural and cultural resources of the geoheritage of the Piedrabuena volcano, on which the urban centre is located, were identified, inventoried, characterized, and valorised as sites of geotouristic interest, and they were connected along a geotourism itinerary. This itinerary has a 4 km route and up to eight interpretive stops, which allows visitors and tourists to enjoy a geotourism experience based on the volcanic landscape of the geopark, laying the foundations for the creation of other similar experiences. Full article

This study explores the impact of microclimatic variations on thermal perception and walking experience in Béjaïa, Algeria, focusing on two contrasting urban areas: the compact historic medina and the modern lower city. A mixed-method approach combined microclimatic measurements (Ta, Ts, Va, RH) with subjective evaluations from 70 participants. After urban morphological analysis, walking itineraries were designed and studied through accompanied walks. Participants reported their thermal sensations and walking comfort via questionnaires and mental maps, while environmental data were simultaneously collected (21–28 July 2022). Results show that transitions between urban fabrics significantly affect thermal sensation and walking thermal comfort (WTC). Strong correlations were observed between surface temperature (Ts) and sky view factor (SVF), and between ASV and WTC (Kendall’s τᵦ = 0.79, 95% CI [0.70, 0.88]). Beyond physical factors, perceptual variables like vegetation (OR = 1.50), maintenance (OR = 1.40), and views (OR = 1.30) significantly increased WTC, while fatigue (OR = 0.70) and safety concerns (OR = 0.80) reduced it. The findings highlight strong contrasts between the two areas and support planning strategies emphasizing vegetation, spatial optimization, and the integration of perceptual thermal factors in urban design. Full article

As low-cost carriers (LCCs) continue expanding their networks and enhancing profitability through connecting services, passenger demand has become a critical factor in flight connection planning. However, demand is inherently uncertain due to economic cycles, seasonal fluctuations, and external disruptions, creating challenges for network design. This study proposes a flight connection planning model tailored to LCC operations that explicitly accounts for demand uncertainty. The model determines the optimal set of connecting itineraries to introduce over the existing network of flights, identifies promising transfer airports, and provides passenger allocation strategies across flights. We apply the model to Spring Airlines’ real-world network to evaluate its effectiveness. Results show that the proposed model outperforms the deterministic benchmark in feasibility and stability under varying demand scenarios. Specifically, under the same constraint of selecting up to 10 transfer airports, our model increases the number of connecting itineraries by 59.5% compared to the deterministic model and achieves a more balanced passenger distribution. Across 10 representative demand scenarios, the average standard deviation of load factors is reduced by 26.1% compared to the deterministic benchmark. Moreover, the deterministic solution yields a 22.9% failure rate for planned connections, while our model maintains 100% feasibility. These findings highlight the model’s value as a resilient, practical decision-support tool for airline planners. Full article

Point-of-interest (POI) recommendation is a crucial task in location-based social networks, especially for enhancing personalized travel experiences in smart tourism. Recently, large language models (LLMs) have demonstrated significant potential in this domain. Unlike classical deep learning-based methods, which focus on capturing various user preferences, LLM-based approaches can further analyze candidate POIs using common sense and provide corresponding reasons. However, existing methods often fail to fully capture user preferences due to limited contextual inputs and insufficient incorporation of cooperative signals. Additionally, most methods inadequately address target temporal information, which is essential for planning travel itineraries. To address these limitations, we propose PSLM4ST, a novel framework that enables synergistic interaction between LLMs and a lightweight temporal knowledge graph reasoning model. This plugin model enhances the input to LLMs by making adjustments and additions, guiding them to focus on reasoning processes related to fine-grained preferences and temporal information. Extensive experiments on three real-world datasets demonstrate the efficacy of PSLM4ST. Full article

The wine industry aims to reduce pesticide use by utilizing disease-resistant grape varieties, although their oenological potential remains underexplored. This study aimed to evaluate their oenological potential compared to traditional ones. Musts from resistant (Souvignier Gris, Sauvignac, Voltis, and Floreal) and traditional (Chardonnay, Sauvignon Blanc, and Viognier) varieties were fermented at laboratory scale with online CO₂ monitoring, and two yeasts were used to study varietal responses to yeast impact. Wines were analyzed for metabolites from central carbon metabolism, aromas (varietal thiols, ethyl esters, acetate esters, and higher alcohols), and phenolic compounds (hydroxybenzoic acids, hydroxycinnamic acids, flavan-3-ols, and flavonols) using (U)HPLC methods. Principal component analysis (PCA) of all variables revealed Souvignier Gris grouped with a Sauvignon Blanc sample, partially due to varietal thiols. PCA of aromas (PC1: 37.7%, PC2: 17.8%) showed that Souvignier Gris and Sauvignac exhibited similar behavior to Sauvignon Blanc. The heat map of 19 phenolics showed Sauvignac and Sauvignon Blanc clustered, with lower phenolic abundance. This preliminary work contributes to a detailed characterization of the oenological potential of these new varieties and constitutes an essential step in identifying which traditional and well-known varieties they resemble. This will then enable the recommendation of cellar itineraries adapted to their profile. Full article

Personalized travel experiences have become a growing priority for tourists, while the built environment increasingly shapes tourists’ behavior. However, limited research has integrated behavioral drivers with algorithmic travel route optimization, particularly in the context of Generation Z tourists. To address this gap, this study proposes a hybrid framework that combines behavioral modeling with enhanced algorithmic techniques to generate customized travel itineraries for Generation Z. A behavioral influencing factors model is first constructed based on the Theory of Planned Behavior (TPB) and Social Influence Theory (SIT), identifying media influence (MI), subjective norms (SNs), and perceived built environment (PBE) as potential determinants of travel behavioral intention (BI). A Structural Equation Model (SEM) is then applied to empirically validate the hypothesized relationships. Results reveal that all three factors have a significant and positive impact on BI (p < 0.05). Building on this behavioral mechanism, an interest-based Ant Colony Optimization (ACO) algorithm is implemented by incorporating point-of-interest (POI) preferences and distance matrices to improve personalized route generation. Comparative analysis using social media keyword data demonstrates that the proposed method outperforms conventional travel route planning approaches in terms of route relevance and overall path satisfaction. This study offers a novel integration of psychological theory and computational optimization, providing both theoretical insights and practical implications for urban tourism planning and the development of smart tourism services. Full article

We propose Phase-Adaptive Federated Learning (PAFL), a novel framework for privacy-preserving personalized travel itinerary generation that dynamically balances privacy and utility through a phase-dependent aggregation mechanism inspired by phase-change materials. (1) PAFL’s primary objective is to dynamically optimize the privacy–utility trade-off in federated travel recommendation systems through phase-adaptive anonymization. The phase parameter φ ∈ [0, 1] operates as a tunable control variable that continuously adjusts the latent space geometry between differentially private (φ→1) and utility-optimized (φ→0) representations via a thermodynamic-inspired transformation. Conventional federated learning approaches often rely on static privacy-preserving techniques, which either degrade recommendation quality or inadequately protect sensitive user data; PAFL addresses this limitation through three key innovations: a latent-space phase transformer, a differential privacy-gradient inverter with mathematically provable reconstruction bounds (εt ≤ 1.0), and a lightweight sequential transformer. (2) PAFL’s core innovation lies in its phase-adaptive mechanism that dynamically balances privacy preservation through differential privacy and utility maintenance via gradient inversion, governed by the tunable phase parameter φ. Experimental results demonstrate statistically significant improvements, with 18.7% higher HR@10 (p < 0.01) and 62% lower membership inference risk compared to state-of-the-art methods, while maintaining εtotal < 2.3 over 100 training rounds. The framework advances federated learning for sensitive recommendation tasks by establishing a new paradigm for adaptive privacy–utility optimization. Full article

Personalized itinerary recommendations are essential as many people choose traveling as their primary leisure pursuit. Unlike model-based and optimization-based methods, sequential-pattern-mining-based methods, which are based on the users’ previous visiting experience, can generate more personalized itineraries and avoid the difficulties caused by the two methods. Although sequential-pattern-mining-based methods have shown promise in generating personalized itineraries, the following three challenges remain. First, they often overlook user diversity in time and category preferences, leading to less personalized itinerary suggestions. Second, they typically evaluate sequences only by POI preference, ignoring crucial factors of optimal visiting times and travel distance. Third, they tend to recommend feasible but not optimal itineraries without exploring extended combinations that could better meet user constraints. To solve the difficulties above, a novel personalized itinerary recommendation system for social media is proposed. First, the user preference, which contains time and category preferences, is generated for all users. Users with similar preferences are clustered into the same group. Then, the sequential pattern mining algorithm is adopted to create frequent sequential patterns for each group. Second, to evaluate the suitability of an itinerary, we define the itinerary score according to the considerations of the POI preference, time matching, and travel distance. Third, based on the tentative itineraries generated from the sequential pattern mining process, the Sequential-Pattern-Mining-based Itinerary Recommendation (SPM-IR) algorithm is developed to create more candidate itineraries under user-specified constraints. The top-N candidate sequences ranked by the proposed itinerary score are then returned to the target user as the itinerary recommendation. A real-life dataset from geotagged social media is implemented to demonstrate the benefits of the proposed personalized itinerary recommendation system. Empirical evaluations show that 94.82% of the generated itineraries outperformed real-life itineraries in POI preference, time matching, and travel-distance-based itinerary scores. Ablation studies confirmed the contribution of time and category preferences and highlighted the importance of time matching in itinerary evaluation. Full article

Urban mass tourism and rural depopulation increasingly threaten heritage sites worldwide, leading to socio-economic and environmental challenges. This study adopts a holistic approach to sustainable tourism planning by examining 84 cultural and natural heritage sites in and around Safranbolu and Amasra, two cities in Türkiye that are listed on the UNESCO World Heritage List and the Tentative List. Inspired by historical travelers’ itineraries, it proposes an eco-cultural tourism route to create a resilient heritage network. A participatory methodology integrates charettes within Erasmus+ workshops, crowdsourcing, various analysis methods while engaging stakeholders, and AI-powered clustering for route determination. The study follows a four-stage framework: (1) data collection via collaborative GIS, (2) eco-cultural route development, (3) stakeholder participation for inclusivity and viability, and (4) assessments and recommendations. Results highlight the strong potential of heritage assets for sustainable tourism while identifying key conservation risks. Interviews and site analysis underscore critical challenges, including the absence of integrated site management strategies, insufficient capacity-building initiatives, and ineffective participatory mechanisms. Moreover, integrating GIS-based crowdsourcing, machine learning clustering, and multi-criteria decision-making can be an effective planning support system. In conclusion, this study enhances the sustainability of heritage and tourism by strengthening participatory eco-cultural development and mitigating mass tourism and abandonment’s negative impacts on the heritage sites. Full article

The itinerary computation approach for the N-type map is proposed. Based on this itinerary approach, the topological conjugation between the N-type map and the shift map on the three-symbol space is proven. Additionally, the calculation of periodic points and the demonstration of the chaotic property of the N-type map are presented. As a practical application in the field of image security, two image scrambling encryption algorithms are proposed and implemented. Full article

Balancing tourism seasonality remains a significant challenge in the management of tourist attractions. Despite existing research on the impact of seasonality from the perspectives of tourist intention cognition and spatial theory, gaps still exist in the relevant literature. This study examines 16 5A-level scenic spots in China with peak season, flat season, and off-season themes, utilizing 8385 tourist reviews from Ctrip.com as data. The LDA topic model is employed to analyze tourism destination image (TDI) under seasonality of destination, and the spatial level (SLs) model is combined to analyze the spatial hierarchy of these images. The findings reveal an association between TDI and SLs under seasonality of destination. For instance, peak season TDI themes (e.g., ‘viewing the scenery’) exhibit a support level of 0.789, while off-season themes (e.g., ‘relaxed itinerary’) reach 0.682, reflecting tourists’ prioritization of functional versus psychological dimensions across seasons. The proposed TDI-SLs correlation theory bridges supply-side spatial resource allocation with tourists’ perceptual dynamics, offering a novel framework to rebalance seasonal demand–supply gaps through strategic spatial planning and image recalibration. Practically, this framework guides destination managers to design season-specific strategies, such as optimizing crowd management in peak seasons or promoting immersive experiences in off-seasons. Full article

The tourism sector, a major economic contributor, seeks innovative approaches to enhance the user experience. In this evolving landscape of global tourism, using augmented reality (AR) technology can be a way to increase the engagement of tourists, but current AR applications often overwhelm (and bore) the users with excessive information. This study addresses the challenge of designing an AR solution that increases the efficiency of exploring and navigating tourist routes, while minimizing information overload by only offering contextualized information about those itineraries. The AR application delivers personalized details about historical monuments, points of interest (POIs), and cultural landmarks, enriching educational experiences and promoting local tourism. The solution was tested on two historic routes, one in Porto and the other in Chaves (both in Portugal), chosen for their cultural richness. The study’s conclusions, derived from experimentation and statistical analysis, confirm the AR technology’s effectiveness in enhancing the efficiency, immersion, and contextualization of tourist routes. Full article