Search Results (67)

Digital transformation has become an essential component of sustainable rural development, yet substantial inequalities persist in how communities access, adopt, and benefit from digital technologies. Understanding these disparities requires not only information about technological resources but also knowledge of the relational structures through which information, support, and opportunities circulate. This article presents a reproducible synthetic socio-digital network dataset designed to support the analysis of digital gaps in community-based tourism (CBT) environments. Rather than containing original respondent-level observations, the repository was computationally reconstructed from aggregate statistics derived from field studies conducted in three rural communities in the province of Guayas, Ecuador: Bucay (5 de Septiembre), Manglares Churute, and Ruta de los Chirijos. All node-level records, survey variables, and support relationships included in the repository were synthetically generated to preserve aggregate community characteristics while protecting participant confidentiality and preventing individual re-identification. The repository contains synthetic actor metadata, reconstructed socio-digital variables, directed support networks, graph representations in interoperable formats, and precomputed Social Network Analysis (SNA) indicators. The dataset includes 90 synthetic actors, more than one thousand generated support interactions distributed across multiple socio-digital dimensions, machine-readable metadata, and reusable scripts for preprocessing, validation, graph construction, and metric computation. The represented dimensions include financial assistance, training support, information exchange, technological support, social media promotion, institutional collaboration, trust, and emotional closeness. To facilitate reuse, all resources are distributed in standardized formats compatible with NetworkX, Gephi, Neo4j, and graph-learning frameworks. The repository follows FAIR principles and includes documentation intended to support transparency, reproducibility, and methodological benchmarking. Potential applications include social network analysis, graph mining, graph neural networks, digital inequality research, computational social science, community resilience studies, and educational activities. By providing an openly documented synthetic dataset and reproducible computational workflow, the repository contributes to the study of socio-digital systems, privacy-preserving data sharing, and community-level digital transformation processes. Full article

Phishing susceptibility reflects both stable psychological traits and transient user states, but confirmed victim cases remain rare in survey studies. This study evaluated BioHARP, a feasibility framework that pairs an outcome-independent psychometric prior with a prospective bio-adaptive fusion design. Using $N=136$ anonymized respondents (12 strict victims), we constructed 69 pre-incident predictors after excluding administrative metadata, exposure indicators, and post-incident response items. A cost-sensitive TabTransformer was trained without synthetic minority generation and benchmarked against six conventional tabular baselines and FT-Transformer under identical splits, unified preprocessing, and model-appropriate cost-sensitive imbalance handling. Out-of-sample performance was primarily assessed with a 60-seed repeated stratified hold-out protocol with fixed four-positive/thirty-negative test composition. Across the sixty splits, TabTransformer yielded a mean AUC of $0.534\pm 0.157$, whereas CatBoost yielded $0.736\pm 0.108$. On fixed Seed 100, TabTransformer reached AUC $=0.8167$ and CatBoost AUC $=0.775$; for the single-init TabTransformer, this was the best-observed split and was therefore interpreted as an optimistic upper-end point estimate. Threshold-dependent metrics were reported separately as an exploratory analysis with explicit leakage labeling. The physiological fusion layer was evaluated as an outcome-informed oracle upper bound, reaching AUC $=0.944$ on Seed 100 and $0.878\pm 0.058$, range [0.73, 0.98], across 70 alternative scenario RNG seeds. This result was interpreted strictly as theoretical headroom rather than deployment-calibrated performance. Overall, BioHARP was framed as a feasibility framework with a clearly bounded physiological-fusion design and explicit calibration and sensor requirements for future deployment-ready bio-adaptive detectors. Full article

Southeast Asia (SEA) faces persistent gaps in regional understanding and control of ticks and tick-borne diseases (TBDs) despite recent advances (2023–2025). The second international symposium on ticks and TBDs in SEA (Singapore, August 2025), following the inaugural 2023 meeting in Cambodia, served as a catalyst for regional exchange that informed this perspective. SEA’s ecological and host diversity supports complex tick–host–pathogen networks, yet evidence remains fragmented due to uneven sampling that has largely focused on livestock and peri-urban environments. Key constraints include limited taxonomic resolution driven by outdated or incomplete identification keys, under-sampling of soft ticks (Argasidae), and the absence of harmonized, open-access regional reference resources (including DNA barcodes and MALDI-TOF MS spectral databases). While MALDI-TOF MS, proteomics, AI-assisted identification, and next-generation sequencing/metagenomics are increasingly applied, their broader regional uptake is limited by the absence of harmonized, open-access reference resources (including DNA barcodes and MALDI-TOF MS spectral databases). Broad ecological surveys and integrated animal and human surveillance remain limited, and vector competence studies are constrained by the scarcity of SEA-derived tick colonies and cell lines. Regional data and recent findings (2024–2026) confirm circulation of multiple TBPs (including Anaplasma, Babesia, Borrelia, Coxiella, Ehrlichia, Rickettsia, and Theileria) and highlight emerging viral findings, including southward reports of Bandavirus dabieense. Human infestations and non-communicable tick bite outcomes (e.g., tick paralysis and alpha-gal syndrome) are recognized but remain under-reported due to low clinical awareness and limited diagnostics. Importantly, the diagnostic chain is further disrupted by missed/insufficient specimen collection at the point of care, and by constrained capacity to identify (especially immature) ticks to species level—limitations compounded by the absence of harmonized, open-access regional reference resources. The symposium identified six priorities: (1) full completion and regional validation of tick identification keys for adults (in progress) and immatures (to be initiated), plus an open-access DNA barcode library anchored by curated, voucher-based collections from all SEA countries; (2) harmonization of molecular and proteomic diagnostic platforms, including expansion of regional MALDI-TOF MS and NGS protocols and reference databases; (3) development of tick colonies and cell lines from locally prevalent species to support vector competence, vaccine, and acaricide testing; (4) expansion of One Health surveillance with enhanced ecological sampling at wildlife–livestock–human interfaces; (5) establishment of open-access, region-wide data platforms for integrated tick, TBP, and ecological metadata sharing; and (6) sustained investment in human resources, training, and policy advocacy to raise research and public health visibility of ticks and TBDs. Full article

Student attrition remains a persistent challenge in higher education and is shaped by interacting socioeconomic, academic, institutional, and wellbeing-related mechanisms. Although learning analytics and educational data mining increasingly support early-warning and intervention workflows, dataset reuse is often limited by incomplete documentation and inconsistent variable definitions. This Data Descriptor presents a structured cross-sectional survey dataset on factors influencing student persistence at a Colombian public university campus (La Paz). Data were collected between August and December 2025 through an online questionnaire and subsequently cleaned to remove duplicate entries and personally identifiable information. The released dataset contains 333 student records and 33 variables covering demographics (e.g., age, gender, first-generation status), socioeconomic conditions (e.g., residential stratum, housing, financial aid), academic experience and satisfaction (multiple 1–5 Likert items), perceived dropout intention across personal/socioeconomic/academic domains, thematically coded open-ended items describing challenges and motives, and a self-allocation of 0–100 weights across three dropout-factor domains. We provide a machine-readable codebook, a transparent preprocessing description, and technical validation checks (value ranges, category consistency, and composite-score integrity). The dataset is intended to support reproducible retention research, equity-oriented analyses, and benchmarking of predictive models, while encouraging responsible reuse through privacy-preserving release practices and FAIR-aligned metadata, repository deposition, and versioning. Full article

We announce the U.S. Geological Survey (USGS) Rotating X-ray Computed Tomography (RXCT) Coral-Core Archive, a digital resource derived from ~360 coral reef cores curated at the USGS Pacific and St. Petersburg Coastal and Marine Science Centers. The archive delivers calibrated 3-dimensional image volumes that enable reproducible values of skeletal density, linear extension, and calcification from decadal- to centennial-scale records of coral growth and bioerosion. Cross-study comparability within the archive is supported by a unified RXCT workflow that minimizes imaging artifacts. This includes rejecting image-intensity–density calibrations with r² < 0.95, back-calculating standard densities to verify a ±10% target precision, and confirming that band-averaged density values fall within published species- and site-specific ranges. Our release of data under FAIR (Findable, Accessible, Interoperable, Reusable) principles is important given global coral reef decline and the rarity of physical coral archives. Calibrated imagery and scan metadata are distributed through CoralCache/CoralCT for analysis (DOI: 10.5194/essd-2025-598), while core locations and collection metadata are published through the USGS Geologic Core and Sample Database (DOI: 10.5066/F7319TR3) with links to CT imagery in a USGS ScienceBase repository (DOI: 10.5066/P139Y9H4). This archive provides a powerful dataset for evaluating environmental controls on coral growth, establishing restoration baselines, and improving coastal hazard assessments in the face of global coral reef declines. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) equipped with multispectral imaging sensors has revolutionized data collection in precision agriculture. These platforms provide high-resolution, temporally dense data crucial for monitoring crop health, optimizing resource management, and predicting yield. However, the development and validation of robust data-driven algorithms, from vegetation index analysis to complex deep learning models, are contingent upon the availability of high-quality, standardized, and publicly accessible datasets. This review systematically surveys and characterizes the current landscape of available datasets containing multispectral imagery acquired by UAVs in agricultural contexts. Following guidelines for reporting systematic reviews and meta-analyses (PRISMA methodology), 39 studies were selected and analyzed, categorizing them based on key attributes including spectral bands (e.g., RGB, Red Edge, Near-Infrared), spatial and temporal resolution, types of crops studied, presence of complementary ground-truth data (e.g., biomass, nitrogen content, yield maps), and the specific agricultural tasks they support (e.g., disease detection, weed mapping, water stress assessment). However, the review underscores a critical gap in standardization, with significant variability in data formats, annotation quality, and metadata completeness, which hampers reproducibility and comparative analysis. Furthermore, we identify a need for more datasets targeting specific challenges like early-stage disease identification and anomaly detection in complex crop canopies. Finally, we discuss future directions for the creation of more comprehensive, benchmark-ready open datasets that will be instrumental in accelerating research, fostering collaboration, and bridging the gap between algorithmic innovation and practical agricultural deployment. This work serves as a foundational guide for researchers and practitioners seeking suitable data for their work and contributes to the ongoing effort of standardizing open data practices in agricultural remote sensing. Full article

Wetlands have been recognized as nature-based solutions to the climate crisis. This study evaluates the state of standardization in nationwide inland wetland survey datasets and analyzes terrestrial vertebrate patterns by integrating datasets with public environmental data. Species richness data for amphibians/reptiles (432 wetlands), birds (1183 wetlands), and mammals (72 wetlands) were compiled from 134 reports published between 2000 and 2021. Using generalized linear models (GLMs) and generalized additive models (GAMs), we assessed how 15 explanatory variables (climate, topography, wetland information, land use, and water quality) relate to species richness. Model families were chosen for each taxonomic group, and variables were selected using the Akaike information criterion (AIC) and ecological plausibility. Deviance explained was 55.5% for amphibians/reptiles, 60.1% for birds, and 52.4% for mammals. Wetland area and Normalized Difference Vegetation Index (NDVI) were positively associated with species richness across all groups. Despite the large volume of survey data, inconsistent reporting formats and limited metadata constrain longitudinal and time series analyses. Standardized protocols and metadata management are therefore needed to build a systematic national database that can support wetland ecological modeling and conservation policy. Full article

This study presents DeepVisionAnalytics, an integrated framework that combines eye tracking, OpenCV-based computer vision (CV), and machine learning (ML) to support objective analysis of consumer behaviour in visually driven tasks. Unlike conventional self-reported surveys, which are prone to cognitive bias, recall errors, and social desirability effects, the proposed approach relies on direct behavioural measurements of visual attention. The system captures gaze distribution and fixation dynamics during interaction with products or interfaces. It uses AOI-level eye tracking metrics as the sole behavioural signal to infer candidate choice under constrained experimental conditions. In parallel, OpenCV and ML perform facial analysis to estimate demographic attributes (age, gender, and ethnicity). These attributes are collected independently and linked post hoc to gaze-derived outcomes. Demographics are not used as predictive features for choice inference. Instead, they are used as contextual metadata to support stratified, segment-level interpretation. Empirical results show that gaze-based inference closely reproduces observed choice distributions in short-horizon, visually driven tasks. Demographic estimates enable meaningful post hoc segmentation without affecting the decision mechanism. Together, these results show that multimodal integration can move beyond descriptive heatmaps. The platform produces reproducible decision-support artefacts, including AOI rankings, heatmaps, and segment-level summaries, grounded in objective behavioural data. By separating the decision signal (gaze) from contextual descriptors (demographics), this work contributes a reusable end-to-end platform for marketing and UX research. It supports choice inference under constrained conditions and segment-level interpretation without demographic priors in the decision mechanism. Full article

A universal approach to managing a person’s digital identity may be the single most important advancement to the Internet since its inception, promising the seamless flow of information, averting cybercrime, eliminating login credentials, and restoring privacy and trust through greater control of one’s identity online. However, this advancement brings significant risks, especially regarding personal privacy. It demands the meticulous development of digital identity infrastructure that balances robust data security measures with ethical handling of sensitive information, thereby safeguarding against misuse and unauthorised access. Currently, a consolidated vision for digital identity implementation remains unresolved, and aligning the different stakeholders’ motives and expectations is a challenging task. This article reviews and analyses the perspectives and expectations of four key stakeholder groups—government, business, academia, and consumers—regarding a digital identity ecosystem, aiming to increase trust in an eventual design framework. Using an online survey stratified across government, business, academia, and consumers, we identify areas of alignment and divergence regarding privacy, trust, usability, and governance expectations. We then encode these stakeholder expectations into a layered conceptual structure and illustrate its use as metadata for context-layered retrieval-augmented generation (RAG) in digital identity scenarios. Full article

Digital identity operates as the connective infrastructure of the digital age, linking individuals, organisations, and devices into networks through which services, rights, and responsibilities are transacted. Despite this centrality, the field remains fragmented, with technical solutions, disciplinary perspectives, and regulatory approaches often developing in parallel without interoperability. This paper presents a systematic survey of digital identity research, drawing on a Scopus-indexed baseline corpus of 2551 publications spanning full years 2005–2024, complemented by a recent stratum of 1241 publications (2023–2025) used to surface contemporary thematic structure and inform the ontology-oriented synthesis. The survey contributes in three ways. First, it provides an integrated overview of the digital identity landscape, tracing influential and widely cited works, historical developments, and recent scholarship across technical, legal, organisational, and cultural domains. Second, it applies natural language processing and subject metadata to identify thematic patterns, disciplinary emphases, and influential authors, exposing trends and cross-field connections difficult to capture through manual review. Third, it consolidates recurring concepts and relationships into ontological fragments (illustrative concept maps and subgraphs) that surface candidate entities, processes, and contexts as signals for future formalisation and alignment of fragmented approaches. By clarifying how digital identity has been conceptualised and where gaps remain, the study provides a foundation for progress toward a universal digital identity that is coherent, interoperable, and socially inclusive. Full article

This study presents the digitization and integration of Kazakhstan’s legacy gravimetric maps at a scale of 1:200,000 into a modern geospatial database using ArcGIS. The primary objective was to convert analog gravity data into a structured, queryable, and spatially analyzable digital format to support contemporary geoscientific applications, including geoid modeling and regional geophysical analysis. The project addresses critical gaps in national gravity coverage, particularly in underrepresented regions such as the Caspian Sea basin and the northeastern frontier, thereby enhancing the accessibility and utility of gravity data for multidisciplinary research. The methodology involved a systematic workflow: assessment and selection of gravimetric maps, raster image enhancement, georeferencing, and digitization of observation points and anomaly values. Elevation data and terrain corrections were incorporated where available, and metadata fields were populated with information on the methods and accuracy of elevation determination. Gravity anomalies were recalculated, including Bouguer anomalies (with densities of 2.67 g/cm³ and 2.30 g/cm³), normal gravity, and free-air anomalies. A unified ArcGIS geodatabase was developed, containing spatial and attribute data for all digitized surveys. The final deliverables include a 1:1,000,000-scale gravimetric map of free-air gravity anomalies for the entire territory of Kazakhstan, a comprehensive technical report, and supporting cartographic products. The project adhered to national and international geophysical mapping standards and utilized validated interpolation and error estimation techniques to ensure data quality. The validation process by the modern gravimetric surveys also confirmed the validity and reliability of the digitized historical data. This digitization effort significantly modernizes Kazakhstan’s gravimetric infrastructure, providing a robust foundation for geoid modeling, tectonic studies, and resource exploration. Full article

The Lactobacillaceae family encompasses microorganisms of exceptional ecological and biotechnological importance, serving as central agents in food fermentations, health applications, and nutrient cycling across diverse environments. Despite their broad functional and phylogenetic diversity, the global distribution and ecological specialization of Lactobacillaceae are not yet fully understood. In this study, we performed a comprehensive analysis of over 2 million records from the NCBI database to survey and trace the ecological landscape of Lactobacillaceae across thousands of distinct habitats. Our results reveal that food products and animal hosts represent the primary ecological niches for members of this family. The examined taxa exhibit a broad spectrum of ecological strategies, ranging from generalists with wide environmental adaptability to specialists with strict niche preferences. Notably, our findings highlight a profound geographical and ecological sampling bias, with unclassified taxids frequent in animal gastrointestinal tracts, soils, and especially in living plant tissues—habitats identified as promising frontiers for discovering novel biodiversity. The obtained results emphasize the urgent need for expanded sampling efforts in underexplored geographic regions such as Africa, Antarctica, the Arctic, South America, and Central Asia to capture a more complete picture of Lactobacillaceae diversity. The study underscores the necessity of implementing standardized, metadata-rich data deposition practices to enable unbiased, large-scale ecological and evolutionary analyses. Ultimately, these insights not only deepen our fundamental knowledge of Lactobacillaceae diversity but also provide a strategic framework for future bioprospecting, fostering the discovery of novel strains and expanding the biotechnological potential of this influential bacterial family. Full article

The increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge. This review addresses the organisation, indexing, and access strategies, which enable scalable and responsive retrieval of structured patterns. We examine the underlying types of data and pattern outputs, common retrieval operations, and the variety of query types encountered in practice. Key indexing structures are surveyed, including prefix trees, inverted indices, hash-based approaches, and bitmap-based methods, each suited to different pattern representations and workloads. Storage designs are discussed with attention to metadata annotation, format choices, and redundancy mitigation. Query optimisation strategies are reviewed, emphasising index-aware traversal, caching, and ranking mechanisms. This paper also explores scalability through parallel, distributed, and streaming architectures, and surveys current systems and tools, which integrate mining and retrieval capabilities. Finally, we outline pressing challenges and emerging directions, such as supporting real-time and uncertainty-aware retrieval, and enabling semantic, cross-domain pattern access. Additional frontiers include privacy-preserving indexing and secure query execution, along with integration of repositories into machine learning pipelines for hybrid symbolic–statistical workflows. We further highlight the need for dynamic repositories, probabilistic semantics, and community benchmarks to ensure that progress is measurable and reproducible across domains. This review provides a comprehensive foundation for designing next-generation pattern retrieval systems, which are scalable, flexible, and tightly integrated into analytic workflows. The analysis and roadmap offered are relevant across application areas including finance, healthcare, cybersecurity, and retail, where robust and interpretable retrieval is essential. Full article

This study proposes a practical integration of an existing deep learning model (YOLOv9-E) and social participation GIS using multi-source remote sensing data to identify asbestos-containing materials located on the side of a building affected by light occlusions. These objects are often undetectable by traditional vertical or oblique photogrammetry, yet their precise localization is essential for effective removal planning. By leveraging the mobility and responsiveness of citizen investigators, we conducted fine-grained surveys in community spaces that were often inaccessible using conventional methods. The YOLOv9-E model demonstrated robustness on mobile-captured images, enriched with geolocation and orientation metadata, which improved the association between detections and specific buildings. By comparing results from Google Street View and field-based social imagery, we highlight the complementary strengths of both sources. Rather than introducing new algorithms, this study focuses on an applied integration framework to improve detection coverage, spatial precision, and participatory monitoring for environmental risk management. The dataset comprised 20,889 images, with 98% being used for training and validation and 2% being used for independent testing. The YOLOv9-E model achieved an mAP50 of 0.81 and an F1-score of 0.85 on the test set. Full article

Nowadays, accessibility to academic papers has been significantly improved with electric publications on the internet, where open access has become common. At the same time, it has increased workloads in literature surveys for researchers who usually manually download PDF files and check their contents. To solve this drawback, we have proposed a reference paper collection system using a web scraping technology and natural language models. However, our previous system often finds a limited number of relevant reference papers after taking long time, since it relies on one paper search website and runs on a single thread at a multi-core CPU. In this paper, we present an improved reference paper collection system with three enhancements to solve them: (1) integrating the APIs from multiple paper search web sites, namely, the bulk search endpoint in the Semantic Scholar API, the article search endpoint in the DOAJ API, and the search and fetch endpoint in the PubMed API to retrieve article metadata, (2) running the program on multiple threads for multi-core CPU, and (3) implementing Dynamic URL Redirection, Regex-based URL Parsing, and HTML Scraping with URL Extraction for fast checking of PDF file accessibility, along with sentence embedding to assess relevance based on semantic similarity. For evaluations, we compare the number of obtained reference papers and the response time between the proposal, our previous work, and common literature search tools in five reference paper queries. The results show that the proposal increases the number of relevant reference papers by 64.38% and reduces the time by 59.78% on average compared to our previous work, while outperforming common literature search tools in reference papers. Thus, the effectiveness of the proposed system has been demonstrated in our experiments. Full article