Search Results (2,374)

Social media has become an important tool for understanding visitor experiences in museums and cultural heritage sites. This literature review identifies, organizes, and thematically synthesizes existing studies on museum visitor experience based on social media data. It examines 41 studies retrieved from Scopus and Web of Science and published between 2017 and 2026. Furthermore, the review examines the selected studies across six dimensions: social media platforms, types of user-generated data, the role of digital interactions, the museums and cultural heritage sites studied, the analytical methodologies applied, and the main findings on visitor experience. The findings indicate that TripAdvisor is the most frequently used platform for collecting textual reviews and star ratings, whereas Instagram and Flickr are mainly used for visual and spatial data. Most studies rely on computational methods, often combined with quantitative techniques, while qualitative approaches are used less frequently. The identified methods include content analysis, statistical analysis, sentiment analysis, topic modeling, machine learning, image analysis, and spatial analysis. Across the reviewed studies, visitor experience is examined as a multidimensional phenomenon encompassing emotions, service quality, authenticity, historical connection, aesthetics, education, and social participation. Finally, the review identifies recurring themes across the dimensions and synthesizes them into broader research streams. These are brought together in an integrative synthesis framework that organizes existing research, highlights research gaps, and outlines directions for future studies. Full article

To deepen our understanding of human–ecosystem interactions, researchers need to be able to retrieve and analyze historical dark data such as plant and animal phenology, but there are often barriers to doing so. Despite the development of online digitization and other tools, including library search engines, digital collections, machine translation, OCR (optical character recognition), HTR (handwritten text recognition), and generative AI technologies, and the establishment of standards and frameworks (e.g., FAIR Principles and the International Image Interoperability Framework), barriers to converting analog records to digital records (“digital barriers”) and to translating local languages to an international common language (“language barriers”) still remain. We present a case study example of the use of historical dark data in phenology in Japan and the digital and language barriers encountered. We then briefly summarize factors and challenges hindering use of this data and describe the benefits of further removal of these barriers. Full article

Background: Population aging is intensifying the burden of cognitive decline and dementia, while sustainability is becoming central to how health and care systems respond; yet the research at the intersection of these two agendas has not been mapped. Methods: A bibliometric and thematic analysis was conducted on 1100 documents retrieved from the Web of Science Core Collection and Scopus (2000–2026) and analyzed with the bibliometric package, combining performance analysis, science mapping, and an interpretive labelling of the keyword co-occurrence clusters. Results: The literature grew rapidly (19.9% annual growth) and was concentrated in high-income countries, with the United States and the United Kingdom as the leading collaborators. Four themes were identified: multidomain lifestyle prevention of dementia and mental health; the cognitive health continuum; caregiving and sustainable care delivery for the oldest old; and sustainability as a transversal, foundational dimension. Sustainability appeared as a basic, transversal theme rather than an explicit research focus, and quality of life recurred across the themes. Conclusions: Research on elderly brain care rests on a largely implicit foundation of sustainability. Making sustainability explicit, linking brain health to healthy–longevity and brain–capital frameworks, and broadening participation beyond high-income settings are the field’s main priorities. Full article

Retrieving aerosol optical depth (AOD) over land remains challenging because the relatively weak aerosol signal in top-of-atmosphere (TOA) observations must be separated from strong and spatially heterogeneous surface reflectance. Here, we develop a physically informed random forest framework for global 1 km land AOD retrieval from MODIS. The framework integrates multispectral TOA reflectance, surface properties, observation geometry, meteorological conditions, topography, and physically informed aerosol–surface features. Long-term Aerosol Robotic Network (AERONET) observations from 2001 to 2017 were collocated with MODIS and ancillary datasets for model development and evaluation. Two physically informed features were introduced to improve retrieval robustness across diverse aerosol and surface conditions, including minimum AOD derived from long-term AERONET observations and time-series clear-sky reflectance (TSCR) in the blue, red, and shortwave-infrared bands derived using the 6S radiative-transfer model. Independent retrieval evaluation for 2013–2014 showed good agreement with AERONET observations, with R = 0.81, MAE = 0.063, RMSE = 0.096, and 74.93% of matched samples falling within the MODIS land expected-error envelope, although increasing underestimation was observed at high aerosol loading (AOD > 1). The proposed retrievals also showed better agreement with AERONET than the MOD04 Dark Target and Deep Blue products. These results demonstrate the value of incorporating physically interpretable aerosol-background and surface-reflectance information into data-driven retrievals for AOD over land surfaces. Full article

Accurate Short-term forecasting of gusts at 10 m above ground level is challenging because gridded meteorological variables exhibit persistence, nonstationarity, and abrupt transitions. This study proposes a Temporal Adaptive Difference Polynomial Network (TADPN) using 8760 hourly records from a representative Nanjing grid point in 2025. Surface and pressure-level predictors are temporally aligned ECMWF IFS HRES 9 km fields retrieved through the default Best Match option of the Open-Meteo Historical Weather API, with a 12 h input window. TADPN uses the current gust as a persistence anchor and decomposes the forecast increment into a basic-trend component and a difference polynomial perturbation component. The basic branch uses all 60 variables, whereas the perturbation branch constructs current states, first- and second-order differences, signed-square terms, and within-variable interactions from 18 wind-related variables, with variable- and term-level soft gates. Strictly chronological three-fold rolling validation yields mean MAE, RMSE, and $R^2$ values of 0.4197 m s⁻¹, 0.6079 m s⁻¹, and 0.9373. TADPN reduces MAE by 11.88–29.88% relative to nine baselines. Ablation and significance analyses support the perturbation branch and difference-based features, demonstrating a lightweight and interpretable framework for hourly single-grid gust forecasting. Full article

Intelligent agents are emerging as an important system-level paradigm for smart agriculture. This review focuses on modern agricultural intelligent agents driven by large language models and related multimodal foundation models and examines how this emerging field is reshaping the organization of intelligent agricultural systems. It first clarifies the conceptual boundaries of agricultural intelligent agents and distinguishes them from traditional multi-agent systems, agent-based modeling, agricultural foundation models, and static retrieval-augmented question-answering systems. It then synthesizes their architectural foundations, key capabilities, application scenarios, deployment challenges, and future research directions. The reviewed literature indicates that agricultural intelligent agents are moving beyond isolated perception, prediction, and response generation toward the goal-oriented coordination of agricultural knowledge, dynamic data, external tools, and decision-making processes across agricultural task chains. They are beginning to support more integrated forms of knowledge services, crop monitoring and diagnosis, decision support, and farm-level collaborative management. Nevertheless, their transition from prototype systems to dependable and deployable agricultural systems remains constrained by context-aware knowledge grounding, heterogeneous data and tool integration, long-horizon reliability, the stability of multi-agent collaboration, and system security. This review further introduces an assessment perspective based on evidence reported in the original studies, comparing representative agricultural intelligent agents in terms of task decomposition, agronomic evidence applicability, tool-use validity, workflow reliability, multi-agent coordination, and deployment-related evidence. By distinguishing demonstrated capabilities from unevaluated dimensions, this review provides a structured framework for understanding the current status of agricultural intelligent agents and for guiding their future development toward reliable, deployable, and domain-oriented intelligent systems for smart agriculture. Full article

Earth Observation (EO) analysis increasingly relies on large and heterogeneous satellite datasets, yet developing EO workflows often requires specialized expertise in data selection, geospatial programming, and cloud-based processing. Recent advances in Large Language Models (LLMs) offer new opportunities for natural-language interaction with EO systems, although challenges related to transparency, reproducibility, and domain-specific reasoning remain. This study presents SeaScope, an explainable AI framework that integrates LLMs, Retrieval-Augmented Generation (RAG), scientific knowledge retrieval, and Google Earth Engine (GEE) to transform natural-language requests into transparent and executable EO workflows. The framework combines knowledge retrieval, code generation, cloud execution, provenance tracking, and interactive visualization within a unified environment. A pilot implementation is demonstrated through maritime and coastal monitoring applications, including oil spill detection, vessel monitoring, water quality assessment, floating debris detection, and air quality analysis. Multiple state-of-the-art LLMs are evaluated under both RAG and non-RAG configurations using representative EO case studies. The results indicate substantial differences among model families and show that retrieval augmentation can significantly improve workflow generation quality and reliability for capable models, while providing more limited benefits for smaller models. The proposed framework demonstrates the potential of explainable AI agents to support transparent, reproducible, and scalable EO analysis. Full article

Recent journalism and media research increasingly uses embeddings, transformer-based models, and large language models, yet the methodological functions remain unevenly conceptualized. This article examines how vector-based and semantically oriented methods are incorporated into journalism and media studies and proposes a theory-informed framework of four recurrent methodological functions: semantic mapping, interpretive assistance, cross-scale articulation, and reflexive auditing. We conducted a structured review of English-language journal articles and review articles indexed in Scopus and Web of Science between 2022 and April 2026. The review combined database retrieval, conservative LLM-assisted metadata screening, full-text analytical extraction, expert manual validation, descriptive analysis, exploratory association tests, and an embedding-based map built from article-level analytical profiles. The final corpus comprised 43 articles. Empirical studies of news texts predominated, and semantic mapping was the most frequent primary methodological function. The profile-based map identified four moderately differentiated and overlapping communities centered on reception and circulation, semantic mapping of news content, affective and evaluative discourse analysis, and methodological infrastructure-building. Community membership aligned more strongly with media domain than with study type or primary methodological function, although these patterns are interpreted heuristically given the small corpus and low expected cell counts. The findings show that these methods are best understood as components of hybrid research designs combining semantic formalization, interpretation, validation, and scale-sensitive analysis. Full article

Reliable retrieval-augmented generation requires consistency across query interpretation, evidence selection, and final answer generation. This study defines computational symmetry as bidirectional coverage among canonical query constraints, traceable evidence, and answer claims, with residual asymmetry triggering correction or abstention. The proposed framework integrates a source-linked raw text/entity/event knowledge graph, hybrid dense–sparse retrieval, cross-encoder reranking, pre-retrieval semantic alignment, and a post-retrieval verification gate. DeepSeek-V3 serves as the implementation backbone, while “ChatGPT-type” denotes the broader class of instruction-following conversational large language models. Experiments use T2Ranking for retrieval and reranking, ATIS for diagnostic intent–slot evaluation, and controlled dialogue scenarios derived from T2Ranking. The hierarchical representation improves retrieval F1 from 0.586 to 0.660, while the complete pipeline increases average answer correctness from 0.530 to 0.611 compared with direct LLM answering and from 0.559 to 0.611 compared with graph retrieval. On ATIS, the controller achieves 92.61% intent accuracy, below Joint BERT at 95.18%, and is therefore treated as a reusable orchestration module rather than a superior classifier. The results support the proposed verification correction framework within the tested settings, without claiming superiority over untested adaptive RAG systems. Full article

Synthetic Aperture Radar (SAR) enables high-resolution sea-surface wind speed retrieval. However, the enhanced spatial resolution of SAR imagery introduces substantial challenges, from small-scale contamination sources that significantly degrade retrieval accuracy. Particularly in coastal regions, non-wind-related backscatter signals, such as ships and oil slicks, can severely bias wind speed estimates at sub-kilometer scales. In this study, the first-order moment (average, m₁) and second-order moment (variance, m₂) are computed from the normalized radar cross-section (NRCS) within sub-images of Sentinel-1 SAR data acquired in Interferometric Wide (IW) mode. Analysis reveals that clean-sea-surface signals in both VV and VH polarizations cluster around an approximately linear empirical trend, m₂ = 2m₁ + b, in the m₁-m₂ statistical feature space, whereas the examined contamination types deviate from this trend and occupy separable regions. Based on this characteristic, a quality-control framework is proposed for the systematic separation of clean sea surface from image noise (border noise and inter-swath stripe noise) and non-ocean targets (land contamination, bright targets, and dark spots). Validation using independent SAR data from the Taiwan Strait was conducted separately for native 10 m and height-adjusted 3 m buoy observations. For the native 10 m observations, the RMSE and MBE were essentially unchanged at 1.5 m/s and −0.3 m/s, respectively. For the height-adjusted nearshore observations, the RMSE decreased from 3.2 m/s to 2.1 m/s and the MBE changed from −1.5 m/s to −1.1 m/s. Full article

Fruit and vegetable supply chains generate heterogeneous data across production, storage, logistics, and sales, creating challenges for trusted data sharing, privacy protection, and real-time traceability across distributed supply-chain information systems. Conventional single-chain blockchains suffer from limited scalability, data redundancy, and low retrieval efficiency, making them inadequate for high-frequency full-process information management. This study proposes a multi-chain blockchain framework for trusted full-process information management of fruit and vegetable supply chains. The framework integrates traceability, enterprise, notary, and regulatory chains to support hierarchical data management and privacy isolation. A reputation-based notary node election mechanism and a threshold-signature scheme based on Shamir secret sharing are designed to enhance cross-chain security and distributed regulatory consensus. To improve retrieval efficiency, a Cuckoo-Augmented Merkle Tree (CMerkle) and a skip-list-based block index are developed. Simulation results show that all malicious nodes were restricted by the 19th round, signature aggregation required 70.16 ms in a 500-node setting, and CMerkle achieved retrieval speedups of 14.7 and 153 times at data scales of 500 and 10,000 records, respectively. The framework supports trusted data governance, real-time traceability, privacy-preserving sharing, and regulatory decision support in blockchain-enabled supply-chain information systems. Full article

Atmospheric water vapor is a major driver of Earth’s climate, yet despite its vital role in driving extreme weather and informing Numerical Weather Prediction (NWP) models, precise quantification of Precipitable Water Vapor (PWV) remains a challenge due to its high spatial and temporal variability. To provide an upgraded evaluation reflecting the most recent data and next-generation instrumentation, this study evaluates the accuracy, relative to radiosonde measurements, in calculating PWV values across six different instruments: Microwave Radiometer (MWR), NOAA-21, TROPOMI, Pandora spectrometer, AERONET sun-photometer, and GNSS/GPS against the bias-corrected Vaisala RS-92 and RS-41 radiosondes over Beltsville, Maryland, utilizing an updated 2024–2025 database. As a certified GRUAN site, HUBC adheres to strict international observation standards designed specifically to provide reference-quality data and comprehensive corrections for systematic errors. This rigorous framework justifies their application as the definitive ‘referent truth’ benchmark for remote sensing validation. RMSE and bias were the primary metrics used to assess relative accuracy. GPS measurements provided the highest level of relative accuracy, yielding the lowest RMSE (1.50 mm) and a near-unity linear fit (y = 0.98x). NOAA-21 and TROPOMI exhibit higher random noise when compared to ground-based instrumentation, yet both obtain high relative accuracy retrievals with negligible biases. AERONET and Pandora also showed strong performance with low RMSEs and R² values of 0.986 and 0.988, respectively, while slightly underestimating PWV. In contrast, the Radiometer performed with the lowest relative accuracy, characterized by the highest RMSE (6.19 mm) and a significant negative bias (−5.55 mm). Although all instruments maintained high correlation coefficients (R² ≥ 0.904), these results indicate that satellite and ground-based remote sensing provide reliable PWV retrievals, while GPS presents the most robust benchmark for high-accuracy PWV retrievals relative to radiosonde measurements. The findings also underscore the need for instrument-specific calibration constants to better align remote sensing retrievals with in situ observations. Full article

Smart city initiatives increasingly claim to be citizen-centric, yet governance frameworks persistently conflate two analytically distinct concepts, citizen participation and citizen engagement, in ways that undermine the design and evaluation of civic involvement mechanisms. This conceptual ambiguity represents a structural problem in the field, not a transitional oversight, and carries direct consequences for how urban managers design governance instruments and measure their effectiveness. This study systematically examines the scope, definition, and operationalization of both concepts in smart city research through a Systematic Literature Review (SLR) following the SPAR-4-SLR protocol. A corpus of 43 peer-reviewed articles published between 2011 and 2025, retrieved from Scopus and Web of Science, was subjected to qualitative content analysis using a structured coding framework. Findings reveal that 53% of reviewed studies use participation and engagement interchangeably, a pattern that remains stable across all publication periods, confirming the structural rather than incidental nature of the ambiguity. Only 26% of articles establish a rigorous conceptual distinction and operationalize both terms through distinct analytical frameworks. The review further identifies a critical mechanism design gap: 30% of articles report no engagement mechanism whatsoever, and only 23% report outcomes with verifiable indicators. Based on these findings, this study proposes a conceptual framework that explicitly distinguishes participation, as a behavioral, often episodic act, from engagement, as a sustained, intrinsically motivated process characterized by genuine influence over governance outcomes. The framework offers researchers a theoretically grounded basis for construct differentiation and provides urban managers with actionable criteria for designing governance mechanisms that move beyond symbolic consultation toward authentic co-creation. Implications for digital governance research and smart city policy are discussed, with particular attention to underrepresented contexts in the Global South. Full article

Leaf area index (LAI) is widely used to characterize foliage amount and seasonal canopy development, but it captures only selected aspects of forest structure and can be difficult to retrieve reliably in heterogeneous, multilayered stands. This study evaluates Sentinel-2-based LAI information across eight sites in the Slovenian Classical Karst encompassing post-disturbance regeneration and established forest stands in dolines and relatively level inter-doline terrain. Field effective LAI measured during six periods in 2021 was compared with six Sentinel-2 spectral variables, LAI derived using the Sentinel Application Platform (SNAP), and the Copernicus Land Monitoring Service High-Resolution LAI product. The analysis explicitly distinguished two dimensions of retrieval performance that are often conflated: seasonal fidelity within sites and preservation of structural differences among sites. Most satellite-derived variables and LAI products captured the broad phenological progression from canopy development to senescence. However, strong temporal agreement within sites did not consistently translate into preservation of the ordering or magnitude of structural differences among sites. Several methods compressed the range of high effective LAI values at dense regeneration sites with substantial lower-layer vegetation. The study therefore provides a more informative framework for evaluating LAI products by identifying which component of variation drives apparent agreement. These findings indicate that Sentinel-2 can support phenological monitoring and broad screening of post-disturbance vegetation development. However, quantitative comparisons of canopy density or structural recovery across heterogeneous stands require consideration of canopy heterogeneity, potential spectral saturation, and plot-to-pixel support. The evaluation framework and the observed retrieval limitations are relevant beyond karst forests, particularly to post-disturbance stands, fragmented forests, open woodlands, and sites with dense understory or regeneration layers. Full article

Selecting an appropriate visualization technique for multidimensional and multivariate financial and accounting (F&A) data remains a complex, user-dependent task. The TaxUI&BV4FADA taxonomy previously organized this problem along four dimensions—visualization techniques, user intentions and analytical goals, interaction possibilities, and user groups—but as a human-readable structure, it could not be queried, validated, or integrated into semantic decision-support pipelines. This paper presents an ontological framework that extends TaxUI&BV4FADA into a machine-readable OWL DL artifact authored in WebProtégé, with OWL used for semantic structuring and SPARQL used for score-based recommendation retrieval. The framework formalizes the four taxonomy dimensions and adds a decision-support layer and an evaluation layer. An explicit F&A semantic mapping is provided, and two contrasting worked scenarios—a financial analyst testing a gross-margin hypothesis and a CFO seeking a quarterly overview—show that the framework discriminates between F&A roles and analytical tasks. The evaluation demonstrates logical consistency, competency-question satisfaction, and internal consistency of the populated recommendation matrix against taxonomy-derived expectations, rather than independent empirical recommendation accuracy. This constitutes an internal, artifact-centered validation rather than an external empirical study with end users, and a protocol for future empirical validation with financial and accounting professionals is outlined. The framework provides a domain-oriented semantic and matrix-based decision-support foundation on which executable F&A visualization recommenders can be built. Full article