Search Results (569)

Nowadays, speakers are essential components for message delivery in indoor environments, including lectures, public addresses, and emergency announcements. Their physical placement should ensure adequate direct-sound audibility across occupant service areas while maintaining installation feasibility. A digital twin is a technology that allows an infrastructure layout to be designed and evaluated virtually on a computer before physical installation by reconstructing an indoor environment as a 3D model. In previous studies, we have proposed a method to reconstruct a 3D indoor model of an indoor environment from its ${360}^{\circ }$ panoramic images using 3D Gaussian Splatting (3DGS) and a 3D point cloud, and applied it to surveillance camera placement. In this paper, we propose a digital twin-based speaker placement planning tool for indoor environments by generalizing the previous method to direct-sound acoustic simulation. This tool consists of four stages: (1) reconstructing a 3D indoor model from ${360}^{\circ }$ panoramic images and extracting floor and desk receiver surfaces, (2) assigning the initial speaker budget based on the reconstructed floor area, (3) determining speaker mounting positions on valid ceiling regions using K-means spatial clustering under obstacle and boundary constraints, and (4) simulating broadband direct-sound sound pressure level (SPL) across floor and desk receiver surfaces. For evaluation, the proposed tool was deployed across three real-world indoor scenarios: a basketball hall ($32.15 \mathrm{m}\times 21.99 \mathrm{m}$), a furnished office ($7.17 \mathrm{m}\times 6.17 \mathrm{m}$), and a non-convex L-shaped office ($23.00 {\mathrm{m}}^2$). The experimental results showed that in each scenario, the generated layout achieved complete direct-sound audibility compliance across all sampled physical test locations (≥60 dB floor/≥65 dB desk) with zero detected hotspots exceeding $85 \mathrm{dB}$, maintaining SPL values between $66.91 \mathrm{dB}$ and $77.88 \mathrm{dB}$. An on-site physical measurement campaign confirmed that the generated layouts satisfy target audibility thresholds under real room conditions, with mean absolute errors between $1.78 \mathrm{dB}$ and $2.41 \mathrm{dB}$. These results confirm the practical utility of our approach as an initial geometry-driven planning tool for indoor audio infrastructure deployment. Full article

This study examines the relevance of Terrestrial Laser Scanning (TLS) for documenting and reconstructing architectural heritage elements that have partially or completely disappeared over time, using the remains of the service staircase of the Infantado Palace (Guadalajara) as a case study. Approaches based solely on historical plans cannot verify their reliability without physical evidence for contrast. Based on a critical analysis of existing historical plans and an updated survey using a Terrestrial Laser Scanner (FARO Focus 3D S120), a reconstructive hypothesis is proposed for the 16th-century service staircase. The 3D scanning revealed initial evidence of the previous structure’s existence; within the Palace’s historical–constructive context (15th–16th centuries), the study analyzes the 19th-century floor plans by the Madrid School of Architecture and the Geographic and Statistical Institute, evaluating their correspondence with current spaces. The methodology integrates 3D point cloud surveying, documentary analysis, metric comparison, and the development of an HBIM (Heritage Building Information Modeling) model as the tool through which the reconstruction proposal is materialized and adjusted to empirical evidence, contrasting historical sources with physical evidence. The results reveal discrepancies between historical plans and a previously undocumented constructive reality, confirming the advantage of empirical contrast over documentary analysis. Consequently, the survey made it possible to precisely locate the elements needed to propose the reconstruction of the service staircase. The originality of this study lies in shifting TLS from mere graphic documentation toward the hypothetical reconstruction of disappeared elements, extending its use to research on the historical spatial functioning of buildings. Full article

Post-fire assessment of reinforced-concrete (RC) members is commonly based on concrete compressive strength measurements, a local property that may not fully represent the residual behaviour of flexure-governed members. This study addresses this limitation through a non-destructive methodology based on TLS, aimed at estimating the apparent relative loss of flexural stiffness from observed differences in residual geometry. The point clouds were filtered, rasterized, and subsequently normalized using regression planes fitted to each member. Orthogonal deviations from these planes were statistically analysed to define a geometric condition indicator (DI) based on the difference in point concentration between homologous members within a central deviation band. By comparing structurally equivalent members, increases in beam deflection and camber losses in prestressed joists were calculated and used to derive an apparent deformation-based stiffness-change index ($K_{\Delta }$). The methodology was applied to a one-way ribbed slab affected by a real fire. DI showed strong monotonic association with $K_{\Delta }$, with Spearman coefficients of −0.82 for the beams and 0.92 for the prestressed joists. Beams exhibiting increased deflection and joists exhibiting reduced camber relative to their homologous counterparts showed mean apparent relative stiffness losses of 32.7% and 36.7%, respectively. The results show that the statistical distribution of geometric deviations provides information on the global response of the member that is not captured by the local compressive strength of the concrete. The proposed indicators also enable the identification of geometric anomalies consistent with post-fire structural deformation, providing a non-destructive screening and prioritization tool that complements inspection, material testing, and structural analysis. Full article

Large language model agents can automate data science workflows, but cloud-centric deployment exposes sensitive context and edge-only deployment limits analytical capability. We present FinDS-Agent, a cloud–edge framework that keeps raw records and program execution at the trusted edge while providing a policy-screened, sanitized context to support cloud planning. FinDS-Agent integrates a Three-Stage Cascaded Privacy Gate (TCPG), a Multi-Dimensional Joint Router (MJR), contract-guided ToolGraph planning, edge-side verification, and bounded repair. On 222 DataSciBench tasks over three runs, FinDS-Agent achieved a 69.93% completion rate and 57.06% success rate, improving over Edge-Only by 9.50 and 5.71 percentage points while invoking the cloud for 32.27% of eligible task-runs. On FinDS-Privacy-Bench, TCPG increased sensitive-field recall from 58.20% to 98.10%; no payload-leakage event was observed in the full set (0/200; Wilson 95% CI: 0–1.8845%) or blind split (0/100; 0–3.6993%) under the specified audit and threat model. External evaluation gave pass rates of 33.2%, 53.1%, and 62.3% for Edge-Only, FinDS-Agent, and Cloud-Only on DS-1000. These empirical results support selective cloud planning while delimiting statistical, privacy, and transfer claims. Full article

Individual tree segmentation and diameter at breast height (DBH) estimation are fundamental to precision forest inventory. Light Detection and Ranging (LiDAR) technology has become an essential tool for achieving these objectives at the single-tree level. Different LiDAR platforms—notably unmanned aerial vehicle (UAV) and Mobile Laser Scanning (MLS)—each offer distinct advantages in capturing forest structural information. Multi-source LiDAR fusion has been proposed as a strategy to combine these complementary strengths. However, how to effectively select segmentation algorithms across different LiDAR data sources remains insufficiently understood, particularly for subtropical coniferous plantations with heterogeneous canopy structure. This study systematically compared four individual tree segmentation algorithms (Donager2021, Dalponte2016, Silva2016, and marker-controlled watershed segmentation [MCWS]) across three LiDAR data sources (UAV-only, MLS-only, and fused UAV–MLS) in Pinus massoniana plantations in subtropical China. DBH estimation models were then developed based on the best-performing segmentation results to examine whether data fusion simultaneously improves both detection and DBH retrieval accuracy. The main findings are as follows: (1) the three canopy height model (CHM)-based algorithms achieved a mean overall accuracy (OA) for individual-tree detection of approximately 64% on UAV data but fell below 30% on MLS data, failing to support effective detection; (2) The Donager2021 algorithm, which directly exploits trunk structure from three-dimensional point clouds, achieved the highest OA of 93.15% with MLS data and further improved to 94.82% with fused data; (3) DBH estimation reached a mean R² of 0.96 for both MLS and fused datasets, yet MLS LiDAR alone produced a lower RMSE (2.31 cm; rRMSE = 5.91%) than fused LiDAR (RMSE = 2.40 cm; rRMSE = 6.24%); and (4) higher detection accuracy did not necessarily lead to better DBH estimation, revealing a trade-off between the two objectives. These findings indicate that fusion does not universally improve all downstream tasks, and that the choice of LiDAR configuration and segmentation algorithm should be guided by specific inventory objectives. Full article

Digital survey and three-dimensional modelling have become central tools for the documentation and interpretation of architectural heritage, yet their epistemic role in the study of early Christian architecture still requires critical assessment. This article examines three early Christian sacella in northern Italy—Sante Teuteria e Tosca in Verona, Santa Maria Mater Domini in Vicenza, and San Prosdocimo in Padua—in order to evaluate how digital models can support architectural analysis without replacing direct engagement with the material monument. The study combines terrestrial laser scanning, photographic documentation, point-cloud processing, orthophotos, and architectural drawings to investigate masonry evidence, spatial organisation, roofing traces, and volumetric relationships. The results show that digital models make it possible to compare architectural elements that are difficult to observe simultaneously on site, especially in relation to construction sequences, domed spaces, subsidiary vaulted compartments, and traces of earlier phases. At the same time, the analysis demonstrates that digital visibility remains partial, since material presence, light, scale, and bodily experience cannot be fully reproduced in a virtual environment. The article argues that digital models are most valuable when understood as interpretative mediators: tools that extend observation, support comparison, and encourage renewed attention to the architectural fabric itself. Full article

Building facades are continually exposed to weathering, material aging, hygrothermal cycling, and service-related disturbances. Cracks, delamination, spalling, and seepage can compromise durability and safety, while associated thermal anomalies provide indirect evidence of deterioration. Deep learning, UAV inspection, infrared thermography, LiDAR, building information modeling (BIM), and digital twins have shifted facade inspection toward automated sensing and spatially referenced assessment. Yet most studies remain focused on isolated measures of model accuracy and give limited attention to the full pathway from detection and quantification to component localization, condition rating, and repair. This review organizes the evidence along five dimensions: annotation granularity (A0–A2/A2*), visual task hierarchy (T1–T5), fusion level (F0–F3), spatial mapping level (S0–S3), and engineering maturity (M1–M3). The synthesis indicates that acquisition conditions, material heterogeneity, negative-sample composition, and annotation rules constrain model generalization. Single-modality methods cover classification, detection, segmentation, and partial geometric quantification, but facade-specific external validation remains limited. Multimodal gains depend on registration quality, thermophysical conditions, defect mechanisms, and sensor reliability. Mapping two-dimensional outputs to point clouds, BIM, and digital twins is technically feasible, but error propagation, component matching, and condition-rating protocols remain inconsistent. Interpretability tools and vision foundation models may support verification, annotation, and reporting but cannot replace dedicated systems with validated error bounds. Future work should prioritize cross-dataset benchmarks, facade-specific lightweight models, reproducible multimodal evaluation, standardized 2D-to-3D accuracy protocols, uncertainty-aware manual review, and engineering condition-rating frameworks. Full article

Water scarcity in semi-arid regions makes efficient irrigation scheduling a priority, yet farm-level adoption of soil-moisture monitoring remains limited by the cost, low portability and installation complexity of commercial sensing systems. This study presents the development and field validation of WaziSense, a low-cost, solar-powered Internet-of-Things (IoT) smart tensiometer, developed within the OSIRRIS platform for soil-water monitoring and irrigation scheduling. The device couples a Watermark granular-matrix sensor and a DS18B20 temperature probe to an ATmega328P microcontroller (Arduino Pro-Mini, 3.3 V, 8 MHz) with long-range LoRa communication and a maximum-power-point-tracking (MPPT) solar-charging stage, logging soil matric potential and soil temperature every 15 min. An open-source edge/cloud stack (WaziGate, WaziApp) retrieves weather forecasts from an open API and runs an automated machine learning (AutoML) regression pipeline that forecasts soil-water dynamics and the time to a user-defined threshold, from which irrigation is scheduled and its applied volume verified by a flow meter. The system was deployed at three bioclimatic sites in Tunisia (durum wheat at Cherfech, citrus at Nabeul, apple at Sbeitla), with tensiometers installed at 20 and 40 cm depths, and validated against commercial 10HS capacitive probes coupled to a ZL6 data logger, with which the co-located readings were significantly correlated (r = 0.81). Calibrated readings showed a strong relationship between soil–water content and soil–water potential (R² = 0.99), and the edge forecasting model reproduced soil–water dynamics on unseen data (Sbeitla apple site, 5-day horizon) with R² = 0.73, RMSE = 0.35, MAE = 0.23 and MPE = 12.52%. With a material cost under about 90 EUR per node and fully open-source hardware and software, WaziSense is one to two orders of magnitude cheaper than commercial monitoring stations, offering an affordable, reproducible and scalable tool for data-driven irrigation in water-limited agriculture. Full article

Traditional handicraft knowledge is not only embodied in final products and craft procedures but also embedded in the context formed by production spaces, tools, materials, practitioners, and their interrelationships. However, existing digital preservation practices often focus on individual objects or specific data types, making the spatial and semantic relationships among heterogeneous resources insufficiently represented and limiting public understanding of the broader context of craft practices. To address this issue, this paper proposes a digital resource organization and multi-terminal presentation framework. Using 3D point clouds as a unified spatial reference for the site, the framework links images, videos, interviews, craft records, and object-related materials to spatial locations through structured annotation, and visualizes relationships among practitioners, tools, materials, processes, and spaces through node-link representations. The Web-based viewer and CAVE immersive system access the same content dataset, enabling “input once, reuse across terminals”. User feedback suggests that the framework supports the integrated representation of spatial context, craft resources, and associated information within traditional handicraft sites, with relational visualization contributing to a more holistic understanding of craft practices. The framework provides a reusable workflow for organizing, linking, and presenting heterogeneous heritage resources in traditional handicraft transmission sites, offering digital support for a contextual understanding of craft practices. Full article

Three-dimensional facial scanning is an important tool in reconstructive surgery and burn medicine for objective documentation, treatment planning, and fabrication of patient-specific devices. While professional structured-light scanners provide high geometric accuracy, their cost limits routine implementation. Several low-cost consumer-grade alternatives are available; however, their accuracy for clinically relevant facial applications remains insufficiently validated. Thirty volunteers were initially recruited for facial scanning. Two participants withdrew from further scanning for personal reasons, resulting in a main study cohort of 28 participants. Complete datasets from all 28 participants were available for Artec Eva, Revopoint MIRACO, and Creality CR-Scan 01, whereas 22 Kiri Engine datasets met the predefined quality criteria for smartphone photogrammetry analysis. Facial scans acquired using Revopoint MIRACO, Creality CR-Scan 01, and Kiri Engine were compared with a professional structured-light scanner (Artec Eva) used as the reference system. Six clinically relevant inter-landmark distances were analysed together with global surface deviation relative to Artec Eva reference using Cloud-to-Mesh analysis. Agreement was evaluated using mean signed differences, mean absolute error (MAE), intraclass correlation coefficients (ICC), Bland–Altman analysis, and post-hoc statistical testing. Both MIRACO and CR-Scan demonstrated favourable geometric agreement with the Artec Eva reference. Global surface RMSE relative to Artec Eva reference was 0.72 ± 0.24 mm for MIRACO and 0.57 ± 0.17 mm for CR-Scan, compared with 1.41 ± 0.58 mm for Kiri Engine. More than 90% of facial surface points were within 1 mm of the reference model for MIRACO (91 ± 8%) and CR-Scan (93 ± 7%), whereas Kiri Engine achieved 68 ± 16%. Pooled ICC values ranged from 0.998 to 0.999 across the evaluated systems. Pairwise comparisons showed no significant difference between MIRACO and CR-Scan (p = 0.42), while both significantly outperformed Kiri Engine (p < 0.001). Consumer-grade structured-light scanners demonstrated submillimeter global surface deviations, supporting further investigation for facial surface documentation and monitoring applications. Smartphone photogrammetry showed substantially larger deviations and may be less suitable for applications requiring precise quantitative measurements. Low-cost structured-light systems demonstrated promising geometric performance in healthy volunteers; however, further validation in relevant clinical populations is required before their applicability to reconstructive surgery and burn care workflows can be established. Full article

While Thermal Infrared (TIR) sensors are standard for monitoring volcanic activity, their efficacy is severely compromised by meteorological clouds and dense volcanic ash. To overcome these optical limitations, we present the first ground-based application of a passive microwave radiometer for continuous volcano monitoring. Operating in the 10–12 GHz band, our Total Power Microwave Receiver is stationed 12 km from Mount Etna’s active craters to measure thermal emissions from eruptive hotspots. Unlike traditional TIR imaging, this low-cost, automated system exploits the atmospheric transparency of microwave wavelengths, enabling uninterrupted observation regardless of weather or solar illumination. We detail the system’s design and report its successful detection of volcanic phenomena during the 2023–2025 eruptive cycles, including the transit of a high-temperature ash cloud that triggered a significant radiometric peak. Our findings demonstrate that fixed-point microwave radiometry provides a reliable thermal signature of eruptive activity, offering a pioneering and highly accessible tool for the next generation of global volcanic early warning systems. Full article

This paper proposes a novel Topological Data Analysis (TDA) pipeline to extract robust structural features from functional near-infrared spectroscopy (fNIRS) signals for the classification of Alzheimer’s Disease (AD) stages. Alzheimer’s disease is increasingly understood as a disconnection syndrome, where the disruption of functional brain networks precedes gross anatomical atrophy. However, traditional graph-theoretic approaches rely on arbitrary connectivity thresholds, which can obscure critical multi-scale topological information, and are sensitive to noise. To address this, our framework leverages Persistent Homology (PH) to analyse the topological evolution of brain networks across a continuous range of scales. By modeling 48-channel hemoglobin concentration time-series as high-dimensional point clouds via Granger causality metrics, we construct filtration sequences of Vietoris–Rips complexes. The resulting topological invariants, including 0—dimensional connected components, 1—dimensional loops, and 2—dimensional voids, are first examined through Persistence Diagrams. For classification, significant H0 and H1 features are converted into Persistence Images using Gaussian kernel smoothing, while H2 features are retained for qualitative topological interpretation. This transformation enables the integration of complex topological features into standard machine learning workflows. Our experimental results were evaluated on a subject-level held-out test set consisting only of original, non-augmented recordings. Data augmentation was applied only to the training set to alleviate class imbalance. The proposed topology-driven feature extraction method achieved 86% accuracy in multi-class diagnosis (NC vs. MCI vs. AD). This study validates the efficacy of TDA as a sophisticated signal processing tool for revealing intrinsic neurodegenerative patterns in hemodynamic data, offering an exploratory methodological proof-of-concept for AD stage classification. Full article

Urban coastal cities face compounded flood hazards driven by sea-level rise, intense precipitation, and dense impervious surfaces. This study develops and demonstrates a cloud-native Python 3.12 GIS framework for flood susceptibility screening and critical facility exposure analysis in Miami, Florida, one of the most flood-exposed coastal cities in the United States. Defined here as a geospatial workflow that retrieves data dynamically from cloud-hosted APIs and executes entirely within a hosted computing environment, the framework integrates three open-source spatial indicators: terrain elevation from the USGS 3D Elevation Programme via py3dep; Euclidean distance to water bodies from OpenStreetMap via OSMnx; and building footprint density as an impervious surface proxy, also from OpenStreetMap. Indicators were standardised and combined using literature-informed MCDA weights (water proximity: 0.40; elevation: 0.35; building density: 0.25) into a continuous flood susceptibility index, classified at the 33rd- and 66th-percentile thresholds. In this proof-of-concept application, high-susceptibility zones cover 48.66 km² (34.0%) of the city, concentrated along coastal waterfronts and inland canal corridors. Overlaying critical facility locations on the classified surface indicates that 9 of 16 hospitals (56.2%), 61 of 244 schools (25.0%), and 5 of 17 fire stations (29.4%) fall within high-susceptibility zones; because this overlay uses centroid-based facility points that have not been cross-checked against official municipal or state facility registries, these counts should be read as indicative rather than definitive. Exact binomial testing shows that the school exposure deficit is statistically significant (p = 0.00), while elevated hospital exposure, although substantively notable, does not reach significance at the current sample size (p = 0.07). The susceptibility surface itself has not been quantitatively validated against external benchmarks such as FEMA flood maps or historical inundation records, the MCDA weights have not been sensitivity-tested, and spatial autocorrelation in the index has not been assessed; concrete protocols for each of these steps are specified as subsequent calibration work rather than as prerequisites for the architecture demonstrated here. The contribution of this paper is the reproducible, cloud-native workflow architecture and its proof-of-concept application, not a validated operational assessment tool; we present it explicitly as a methodological protocol and workflow demonstration, not as an evaluation of flood risk. The framework is fully reproducible, low-cost, and transferable to other US coastal cities. Full article

LiDAR (Light Detection and Ranging) and RGB-D camera imaging have emerged as essential tools in agricultural applications, particularly for plant size and distance measurements, enabling non-destructive, cost-effective, and precise estimation. The objective of this study was to measure the plant canopy dimensions and distance between apples using commercial LiDAR, and an RGB-D camera with a speed sprayer platform was used to determine whether LiDAR provides a higher measurement accuracy under field conditions. Data were collected in an apple orchard in Muju, Republic of Korea. Commercial 3D LiDAR, a terminal box, an RGB-D camera, a microcontroller, a power supply, and individual display monitors were integrated into a customized data acquisition (DAQ) box for LiDAR point cloud (PCD), RGB, and depth imagery data collection. Commercial software was used for data acquisition, data conversion (pcap to PCD), segmentation of regions of interest (ROI), and pre-processing of data. PCD processing and measurement consisted of data frame selection, data conversion, outlier removal, downsampling, denoising, ground point removal by filtering, voxelization, and density map generation using an open access programming language script. Depth image processing included importing raw data, shaping metadata using intrinsic camera parameters, visualizing depth images, extracting depth points, and measuring the plant canopy at the pixel level. RGB image analysis involved grayscale conversion, thresholding, segmentation of ROI, contour preparation, noise removal, and binary masking for eliminating the background. Estimated results were compared to measured results. LiDAR measurements showed the closest agreement with the measured results for plant height, canopy volume, plant spacing, and row distance, outperforming both RGB and depth imaging. Under field conditions, plant spacing and row distance were estimated with accuracies of 97.5% and 94.7%, respectively, exhibiting higher measurement accuracies than RGB and depth imagery data results. Despite some discrepancies due to complex plant geometry and dynamic data collection, the results support data collection strategies critical for precision horticulture. Full article

Aging residential buildings constructed under outdated design standards create an urgent need for scalable, evidence-based indoor safety assessment methods. Conventional manual inspections rely on subjective checklists, lack audit trails, and are impractical for widespread deployment. This study presents a vision-based digital twin and AI agent framework that converts a single continuous smartphone video into an explainable, evidence-constrained safety assessment. The pipeline employs MASt3R-SLAM to reconstruct a metric-scale 3D point cloud from monocular video, calibrated with AprilTag fiducials for absolute scale. SpatialLM parses the geometry to extract semantic entities and spatial relationships. Risk guidelines are formalized into a computable Risk Prototype structure, unified within a hierarchical SceneState data structure that binds geometric measurements, semantic labels, image observations, and regulatory knowledge. A LangGraph-based AI agent conducts a dual-pathway assessment: an initial whole-dwelling scan followed by iterative follow-up queries invoking tool calls for measurement, knowledge retrieval, or visual cross-checking. In a pilot validation across five heterogeneous residences, with detailed manual comparison in two representative cases, the framework achieved risk recall rates of 77.8–100% and precision rates of 45.0–70.0% against the single-assessor manual reference. The average judgment closure rate was 71.7%, with spatial granularity enhancement of up to 2.2× in complex environments. These results suggest that the framework can achieve risk coverage comparable to manual checklist inspection while offering enhanced granularity in complex environments and quantitative precision in well-defined spaces. Full article