Search Results (526)

Background/Objectives: Although lung cancer is the leading cause of cancer-related deaths in the United States, rates of screening have remained persistently low nationwide. This study sought to identify barriers, facilitators, and support strategies necessary for implementing a novel community health navigator workflow to improve lung cancer screening uptake in both rural and urban settings. Methods: Semi-structured interviews were conducted with primary care providers (n = 5), community scientists (n = 7), community health navigators (n = 4), and radiology staff (n = 2). Interview transcripts were analyzed using a rapid qualitative analysis approach. Three authors coded based on the Consolidated Framework for Implementation Research (CFIR) and the Expert Recommendations for Implementing Change (ERIC) frameworks using a hybrid deductive–inductive approach. Results: Participants highlighted several primary barriers: access to knowledge and information (e.g., knowledge of eligibility, knowledge of insurance coverage), IT infrastructure (e.g., quality of pack-year data), relative priority (e.g., need to discuss other conditions), and patient needs and resources (e.g., time off work, transportation, difficulty scheduling). Key facilitators for screening were again IT infrastructure (e.g., automated electronic health record alerts) as well as relational connections (e.g., trust between patients and providers). To address provider-level barriers, participants recommended educational meetings, using clinical champions, and providing feedback on current lung cancer screening rates. To address patient-level barriers, participants recommended health education tools, providing transportation vouchers, hosting weekend lung cancer screening clinics, and assisting with scheduling. Conclusions: A community navigator approach to lung cancer screening should address key barriers to implementation on both the patient and provider level, including knowledge, prioritization, and patient access. Full article

Background/Objectives: Children now spend a substantial part of daily life in digital environments, and their digital, media, and information literacies shape their online safety, social-emotional development, and mental health, making these competencies a concern for child public health and preventive paediatric care. This study applies a Synthetic Knowledge Synthesis (SKS) to map how digital, media, and information literacies (hereafter digital literacies) among children have evolved, comparing neurotypical and neurodivergent children. SKS is a semi-automated approach that combines descriptive bibliometrics, keyword co-occurrence mapping, and qualitative content analysis to map an entire research field or topic. Methods: We treat digital literacies as relevant to children’s health, well-being, and safe online participation, and we analyse Scopus-indexed literature from 1996 to 2025 using descriptive bibliometrics, keyword co-occurrence mapping, and qualitative content analysis. Results: The mapping shows strong growth in general digital-literacy scholarship alongside a small but persistent body of work on a “double digital divide” affecting children with autism spectrum disorder (ASD), ADHD, and dyslexia. The two bodies of work differ in emphasis: the general literature foregrounds social integration and critical agency, while research on neurodivergent children foregrounds inclusive pedagogy, implicit learning, and multimodal expression. It also shifts from general digital-safety awareness toward protective mediations for vulnerabilities such as social-emotional decoding differences and impulsivity, which bear on children’s online safety and mental health. Because bibliometric mapping reveals where research is concentrated rather than what works in practice, the synthesis points to evidence gaps rather than proven methods. Conclusions: On this basis, we argue for an integrated public-health and socio-educational framework that complements universal literacy standards with adaptive, assistive safety nets, so that all children can take part in digital life safely and on equal terms. Full article

Electricity theft remains a critical threat to power distribution infrastructure globally, with annual losses exceeding USD 89 billion and non-technical loss rates reaching 40% in developing economies. While machine learning has emerged as the dominant analytical approach for automated theft detection in smart grid environments, the field lacks a unifying framework that connects algorithm selection to the operational realities of Distribution System Operators (DSOs). Existing reviews catalogue methods and report benchmark metrics without addressing how detection paradigm selection should be aligned with data maturity, regulatory requirements, computational constraints, and institutional capacity. This review addresses that gap by systematically analysing 90 peer-reviewed studies published between 2015 and 2025, identified through structured multi-database searches, screened against explicit eligibility criteria, and graded with a formal five-criterion quality rubric, through a unified adversarial time-series formulation that provides a consistent analytical lens across all major learning paradigms. The analysis covers supervised ensemble methods, unsupervised and semi-supervised anomaly detection, deep learning architectures, including convolutional neural networks, long short-term memory networks and Transformer models, graph neural networks, federated learning, and explainable artificial intelligence. Key findings reveal that no single paradigm achieves optimality across all deployment dimensions simultaneously, that gradient boosting methods deliver near state-of-the-art performance with significantly lower computational overhead than deep learning, and that hybrid architectures achieve AUC-ROC scores of 0.95 to 0.98 on benchmark datasets but require complementary governance mechanisms to satisfy regulatory defensibility requirements. A lifecycle-aligned deployment framework and a layered detection architecture are proposed, offering practitioners a structured pathway from early AMI rollout through to advanced smart grid deployment. The principal outcomes of the review are a formal characterisation of which component of the detection problem each learning paradigm estimates, quality-graded and harmonised benchmark performance ranges, and a quantified illustrative analysis indicating that the proposed layered architecture can improve inspection productivity by roughly an order of magnitude at a fixed field budget. Four priority research challenges are identified: real-time edge detection, continual learning, multi-modal data fusion, and standardised benchmarking. Full article

Vineyard monitoring requires efficient methods for detecting plant stress and disease while limiting flight time, data volume, and labour effort. This paper presents a satellite-enabled two-tier UAV workflow for semi-automated vineyard inspection. The proposed approach combines high-altitude multispectral scanning, NDVI-based point-of-interest identification, adaptive flight path planning, low-altitude RGB inspection, and downstream vision-based disease analysis. Processing tasks are offloaded to a remote server accessed through an emulated Low Earth Orbit satellite communication environment, allowing the UAV-side system to remain lightweight while receiving multispectral analysis results during the mission. A simulation framework was developed to evaluate mission behaviour under controlled and repeatable conditions, using both pseudo-random point generation and real multispectral vineyard images processed through the satellite emulation testbed. A flight with a real drone was also conducted to validate adaptive flight optimisation. Experimental results focus on the impact of path-adaptation strategies and communication bandwidth on mission efficiency. The results show that route optimisation can reduce mission time by up to $15\%$ when new low-altitude waypoints emerge, while bandwidth bottlenecks affect performance once image transmission can no longer keep pace with acquisition. The findings highlight the need to consider sensing, communication, and mission planning jointly in adaptive UAV-based crop monitoring. Full article

High-speed flow visualization provides imaging data containing quantitative information about cavitating-flow dynamics. Accurate determination of attached-cavity length is essential for characterizing cavitation regimes and validating mathematical models. In this study, an advanced image-processing algorithm for semi-automated analysis of cavitation patterns near hydrofoils is proposed. High-speed visualization data obtained for cavitating flow around a NACA0012 hydrofoil in a slit channel were used as input to the algorithm. The developed approach includes hydrofoil suppression, Otsu-based image binarization with threshold correction, filtering, and automated cavity-boundary detection. The initial search region for the cavity inception point is specified manually, whereas subsequent boundary tracking and cavity-length calculation are performed automatically. A dimensionless threshold correction coefficient was introduced to improve cavity identification, and its optimal range was determined. Additional geometric criteria were proposed to identify the cavity inception and closure locations and to separate attached cavities from detached vapor structures. The analysis showed that the optimal range of the threshold correction coefficient was $0.5$ < $th$ < $0.7$, while a geometric connectivity criterion based on a distance of 7 px between neighboring boundary pixels provided stable detection of the cavity closure location. The developed algorithm enables the determination of both instantaneous and time-averaged attached-cavity lengths, with a total estimated uncertainty not exceeding 3.5%. Comparison with previously published experimental and analytical data demonstrated good agreement and supported the reliability of the proposed approach. The method provides an explainable and training-free computer-vision pipeline that can potentially be adapted to other bluff-body geometries under comparable imaging and contrast conditions. It can also support automated annotation and the generation of reference datasets for the development and validation of future machine-learning methods for cavitation-flow analysis. Full article

When implementing collaborative robot applications, it is paramount to use a human-centered development approach to ensure a positive user experience and increase acceptance. User Experience (UX) design methods involve validating user experience through evaluations as a basic principle. The earlier UX evaluations are carried out, the greater the added value that can be achieved. For collaborative robot applications, especially those involving large robot systems, these early evaluations are challenging since the entire application will not be available until the final stages of development. The proposed methodological approach to this problem utilizes a rudimentary, scaled test setup for early UX evaluations of the entire robot application, enabling user feedback to be incorporated into the development process early on. The method was applied in a project that developed a collaborative robot application for semi-automated liquid cargo handling in inland navigation. UX assessments were carried out using both the rudimentary test setup and a full-scale prototype in a later development phase. The comparison of both assessments proves the applicability of the proposed methodology. Serving as an overarching framework, this methodology encourages developers to test the entire collaborative robot application in user studies at an early stage, thereby gathering valuable user feedback. Full article

Autonomous parking is one of the most demanding manoeuvres a vehicle can be asked to perform. The available space is small, the margins for error are narrow, and the vehicle must achieve an exact final pose while respecting non-holonomic constraints. A persistent gap between detection and execution yields infeasible trajectories or terminal positioning errors. This systematic review examines how bird’s-eye view (BEV) reconstruction supports each link in the perception–planning–control chain, from slot detection through trajectory generation and manoeuvre execution, and identifies where those links remain weakest. No registered review protocol was used. Between 7 January and 10 February 2026, we retrieved 338 records through a single automated engine (OpenAlex) reaching multiple indexed venues, complemented by manual citation chasing; collection was semi-automated and screening was manual. Seventy-two studies passed eligibility screening, of which 45 provide primary, parking-specific evidence; the remaining 27 are prior surveys or generic methodological contributions retained as background rather than as primary evidence. We included studies addressing BEV reconstruction, slot detection, manoeuvre planning, or control with simulated or experimental validation, and excluded duplicates and works reporting no performance evaluation. The synthesis follows five axes: multi-camera homography-based BEV reconstruction, automatic parking-slot detection, geometric and kinematic modelling, planning in confined spaces, and nonlinear control with explicit constraint handling. Across the 45 primary studies, BEV-based methods are reported to improve geometric consistency and support reliable slot detection, and Hybrid A* combined with NMPC recurs as the most frequently reported route to dynamically feasible trajectories, although no study compares these approaches under identical conditions and no quantitative comparison across studies was performed here. The weakest point of the field is integration: perception and control are rarely coupled in a closed loop, and no standardised evaluation framework has yet gained wide acceptance. Heterogeneity in scenarios, metrics, and sensing configurations limits the strength of the evidence, and modular architectures show gaps in perception–planning–control coupling and reproducible transfer to platforms such as Gazebo. No formal risk-of-bias assessment was performed. Full article

Soil sealing is a major driver of urban transformation, generating significant environmental impacts through the loss of soil functions and Ecosystem Services (ES). In this context, economic valuation of ES has emerged as an effective tool for communicating the importance of soil within decision-making processes largely influenced by market dynamics. Despite extensive research on ES, reviews focusing on how economic valuation can support local planning remain limited. As a consequence, this study presents a semi-systematic literature review addressing two research questions: (1) how ES assessment can be integrated into local planning tools and practices, and (2) which monetary metrics can ethically and effectively represent ES values within planning frameworks. Conducted in RStudio, the review combined automated screening of 2500 records with further manual selection and an analytical framework to examine ES definitions, valuation methods, spatial assessment units, and planning applications. The findings indicate that the rigor, methods, and level of detail of ES assessments should be adapted to planning objectives, available resources, territorial context, and stakeholders involved, supporting site-specific decisions. A cross-scale approach emerges as the most suitable for integrating ecological processes with administrative planning, while robust, flexible, and participatory governance across administrative levels is essential. Finally, despite the need for a shared methodological framework, standardized approaches such as the System of Environmental Economic Accounting–Ecosystem Accounting (SEEA EA) are still rarely adopted, particularly for local-scale and monetary ES assessments. Overall, the review provides a transparent and operational approach for integrating ES into spatial planning through interdisciplinary and participatory approaches and strengthening planning systems to prevent soil sealing and promote long-term community benefits. Full article

Three-dimensional (3D) building reconstruction plays an important role in urban planning, disaster resilience, and sustainable infrastructure development. Conventional approaches based on airborne Light Detection and Ranging (LiDAR) data can be computationally intensive and often require large labeled datasets, particularly for large-scale applications. This study presents a semi-automated workflow for reconstructing multi-level 3D building models by integrating airborne LiDAR point cloud data with building footprints extracted from National Agriculture Imagery Program (NAIP) imagery using a Mask Region-Based Convolutional Neural Network (Mask R-CNN). The extracted footprints were used to spatially isolate building-specific LiDAR subsets for 3D reconstruction. The proposed methodology generated building models at multiple Levels of Detail (LOD), ranging from two-dimensional (2D) footprints to volumetric representations with detailed roof structures. Building footprint extraction was quantitatively evaluated against LiDAR-derived footprints generated using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which served as the reference dataset and detected buildings obscured by tree canopy. The reconstruction workflow was implemented in Open3D and incorporated a boundary-aware mesh refinement strategy based on ear-clipping triangulation to improve rooftop continuity in LOD2 models. The proposed footprint extraction framework achieved a mean Intersection over Union (IoU) of 0.8226 relative to LiDAR-derived reference building footprints, indicating reliable building delineation that supports the proposed multi-level 3D building reconstruction workflow. Full article

Accurate characterization of riverbed substrate from remote sensing imagery is essential for applications in fluvial geomorphology, habitat modeling, and river management. While recent advances in computer vision, particularly deep learning, have improved sediment mapping capabilities, their reliance on large annotated datasets and computational resources limits their broader applicability. This study presents a scalable workflow for categorical substrate classification using ultra-high-resolution aerial RGB orthoimagery in clear-water river environments. The approach integrates spectral information with statistical and structural texture descriptors derived from Gray-Level Co-occurrence Matrices (GLCM) and Local Binary Patterns (LBP), combined within a Random Forest classification framework. The methodology is structured as a semi-automated, five-stage workflow: (1) expert-based ground-truth substrate annotation; (2) feature set generation; (3) spatially aware model optimization; (4) full-domain classification; and (5) design-based validation for independent accuracy assessment. Model performance is evaluated using spatially aware cross-validation and design-based probability sampling to account for spatial autocorrelation and provide unbiased accuracy estimates. The method was applied in four geomorphologically distinct alpine river reaches, achieving design-based overall accuracy ranging from 70% to 88%. These results demonstrate that RGB-based approaches can achieve reliable reach-scale categorical substrate classification when combined with appropriate feature representation and rigorous validation strategies. However, limitations remain for visually similar or transitional substrate classes, particularly fine sediments such as sand and clay, which are difficult to distinguish consistently even during manual annotation. The workflow is implemented using open-source tools and is applicable to clear-water conditions where the riverbed remains optically visible. Full article

Coral reef ecosystems are increasingly threatened by climate change, pollution, and overfishing, causing major declines in marine megafauna and high-trophic-level fishes. Monitoring these species is vital for conservation, yet traditional survey methods are slow and resource intensive. This study presents a semi-automated monitoring pipeline that integrates deep learning (DL) with human-in-the-loop validation to streamline Baited Remote Underwater Video (BRUV) analyses in the Gita Nada Marine Protected Area (MPA), Indonesia. A total of 244 BRUV deployments from SORCE’s long-term monitoring program in the Gita Nada MPA, comprising 328 h of footage, collected 2023–2025 under Indonesian research oversight through Yayasan SORCE Konservasi Indonesia, were processed using a DL workflow. To address long-tailed species distributions, focal taxa were grouped into six Morphological Groups and detected using a YOLOv12x model trained via transfer learning from the Community Fish Detector. A custom temporal-tracking framework extracted ecological metrics including N, Time to First Visit ($T_{1^{st}}$), and Visit Duration ($T_{visit}$). The pipeline achieved moderate to high detection and tracking performance for several Morphological Groups, achieving object detection F1-scores of up to 0.873 and an overall tracker recall and precision of 0.80 and 0.76, respectively, although performance varied substantially among groups and was substantially limited for data-deficient taxa. As a proof-of-concept, we applied the framework to assess ecological shifts in Cheloniidae and Carangidae across coral-cover gradients. Overall, this semi-automated approach reduces BRUV processing effort and provides a scalable foundation for generating the large datasets needed to detect subtle ecological change. Full article

The increasing convergence of enterprise architecture and IT governance frameworks, such as TOGAF 9.2, COBIT 5, and NIST CSF 1.1, with LegalTech regulations including GDPR 2016/679, eIDAS 910/2014, and NIS2 2022/2555, has created a growing need for rigorous and automated governance-validation mechanisms. However, to date, no formal approach exists to assess semantic conformance between a normative ontological model and its operational implementation in microservice-based systems, leaving critical governance gaps difficult to detect in legally sensitive environments. This paper proposes MALTG (Multidimensional Architecture for LegalTech Governance), a configurable and reusable formal framework that combines OWL 2 ontology engineering, a JSON-LD-based SDT (Structural Digital Twin), semantic conformance mapping, a hierarchical coverage function, and a conformance gap metric to support automated governance assessment and prioritised remediation. The framework accepts any organisational architecture as input, enabling application to arbitrary LegalTech case studies by replacing the reference SDT. The proposed framework models nine governance dimensions through an ontology of 59 classes and 15 properties and validates them against a semi-real, public-source SDT composed of 39 microservice components and 54 directed connections. The reference SDT was populated through an ontology-driven data-collectionprocess: a scraping campaign guided by the MALTG ontology (data/MALTG_Ontology.owl) harvested public information from the official portal of Ecuador’s Council of the Judiciary (Consejo de la Judicatura, CJ)—probing its technological-maturity level and digital-governance compliance—which was condensed into a single JSON-LD artefact, enforcing domain rigour and traceability on the search for LegalTech governance evidence. Experimental results demonstrated an overall ontological score of 82.6 and an SDT score of 73.7, with a mean conformance gap of 8.9. Five dimensions achieved full conformance, while the LegalTech dimension presented the largest gap. Graph-theoretic validation further confirmed monotonic improvement throughout the remediation sequence. Overall, the findings suggest that MALTG provides a formally grounded and reproducible approach for automating multi-framework LegalTech governance conformance assessment while maintaining semantic and structural traceability between normative models and operational architectures. As a proof of concept, this validation relies on a configurable, ontology-driven public-source (semi-real) SDT; external validity in real production LegalTech organisations remains untested and is left for future work. Full article

Background/Objectives: Chronic kidney disease is a growing global health concern, necessitating effective tools for early detection and monitoring. While non-contrast T1-weighted magnetic resonance imaging offers a non-invasive means to assess kidney morphology, robust automated segmentation remains challenging due to limited annotated data, high inter-patient variability, and low signal-to-noise ratios. Methods: In this study, we address these obstacles by developing and evaluating a series of frugal learning methodologies for kidney segmentation in non-contrast MRI. Building upon the U-Net architecture, we aim to maximize segmentation accuracy despite scarce labeled data. Our experimental framework leverages three diverse datasets to evaluate performance-boosting strategies such as transfer learning: a clinically relevant local cohort (the Barlicki dataset) as the primary target domain and two auxiliary public datasets (AMOS22 and AbdomenCT). Utilizing these data streams, we systematically compare seven frugal learning strategies incorporating data augmentation, semi-supervised learning, and weak supervision against a fully supervised baseline. Results: The results demonstrate that frugal learning methods enable accurate and reliable kidney segmentation while substantially reducing the need for manual annotations. The best-performing semi-supervised and transfer learning approaches achieved a Dice similarity coefficient of 0.89, which was only moderately lower than that of the fully supervised model (Dice = 0.92). Conclusions: This work highlights the potential of data-efficient deep learning techniques to accelerate the adoption of automated kidney segmentation in clinical workflows, particularly in settings where annotated medical images are limited. Full article

Coptic represents the final stage of the Ancient Egyptian language and remains an important component of Christian and Mediterranean cultural heritage. Although several digital resources exist for Coptic textual corpora, Greek-oriented computational tools for the interpretation of Coptic inscriptions on artifacts remain limited. This study presents the design and early implementation of a semi-automated software tool for the computer-assisted translation of Coptic inscriptions into Greek, with optional English support. The tool combines a Coptic–Greek digital dictionary, an interactive character-selection interface, and two dictionary-search strategies: a length/alphabetically structured linear search and a weighted linear search based on expected word frequency. The application is intended to support scholars working with inscriptions on fragile or fragmented cultural heritage objects, where full automation is not realistic and human supervision remains essential. The paper describes the linguistic and material challenges of Coptic inscriptions, the structure of the lexical database, the interface design, and the planned use of Coptic corpora for improving retrieval efficiency. The proposed approach contributes to cultural heritage digitization by offering a practical, expandable, and user-oriented framework for supporting the study, interpretation, and preservation of Coptic inscriptions. Full article

Port operations play a strategic role in global trade but are associated with significant environmental impacts due to intensive energy use, equipment operation, and cargo handling activities. In this context, Life Cycle Assessment (LCA) emerges as an essential tool to quantify these impacts and support decarbonization strategies in the maritime-port sector. This study aims to evaluate the environmental performance and energy transition implications of fertilizer import operations in a multi-cargo port by comparing semi-automated and non-automated scenarios through a Life Cycle Assessment (LCA) approach. The methodology followed the standard LCA framework, including goal and scope definition, inventory analysis, impact assessment, and interpretation. Primary data collected in situ were combined with secondary data from the Ecoinvent database, ensuring consistency and representativeness. The results indicate that post-port logistics is the main driver of environmental impacts. In the semi-automated scenario, rail transport consumes approximately 18,800 L of diesel, showing higher efficiency due to its greater load capacity. In contrast, the non-automated scenario relies on 100 trucks, each consuming about 238.75 L per trip, resulting in higher total fuel consumption and emissions. It is concluded that the non-automated system presents higher environmental impacts across all categories analyzed, highlighting the importance of modal choice and operational efficiency in reducing emissions and supporting the energy transition in the port sector. Full article