Search Results (67)

In conservation work on historic built heritage, the transition from executive design to the worksite often requires the verification, integration and updating of information acquired during the design phase. This issue is particularly relevant for the documentation of the state of conservation since new accessibility conditions on site may allow previously unreadable areas to be observed and decay maps to be revised. This paper presents the preliminary development and testing of SMAT, an iPadOS application designed to support the in situ updating of decay maps and their reintegration within an HBIM workflow. The experimentation was carried out on the Oratory of San Giovanni Battista in Bussana Vecchia, starting from a previously developed HBIM model. The application allows operators to annotate decay phenomena on reference orthophotos using vector geometries, associate each annotation with semantic and temporal information, and store the data in JSON format. The workflow also includes a differential synchronization procedure based on Dynamo scripts, aimed at comparing successive annotation versions and updating the corresponding two-dimensional views in Autodesk Revit. The preliminary experimentation indicates the potential of the proposed procedure to support the traceable management of information updates between the design and worksite phases and to streamline the intermediate steps between direct observation, graphic restitution, and data reintegration, while maintaining an operational approach compatible with the requirements of the worksite phase in restoration processes. Full article

Cultural heritage faces accelerating threats from armed conflict and climate-induced disasters, generating emergency scenarios in which the window for documentation may be measured in hours. Traditional high-fidelity workflows such as Terrestrial Laser Scanning and survey-grade Structure-from-Motion campaign can be operationally incompatible with these constraints due to costly hardware and specialist-team requirements. Emergency image acquisition can be undertaken with consumer-grade equipment, while the same photographic dataset can subsequently support both an SfM-MVS mesh for metric and geometric analysis and a 3D Gaussian Splatting (3DGS) representation for view-dependent visual interpretation. This paper evaluates an accessible, entirely graphical workflow for producing these complementary outputs, with particular attention paid to the 3DGS stage and without requiring programming or command-line expertise. We develop a theoretical case drawing on Brandinian restoration theory and the international conservation framework, introducing the concept of informational amnesia, the irreversible loss of a site’s documentary record, as a distinct and undertheorized category of heritage failure. We further argue that the democratization of 3DGS documentation cannot be measured by licensing cost alone: a tool distributed as open-source code but requiring command-line expertise presents an effective accessibility barrier functionally equivalent to a commercial paywall. True accessibility requires installable, graphical software operable by non-specialist practitioners in the field. We present a proof-of-concept application of this accessible pipeline, namely DJI Mini 5 Pro, RealityScan for photogrammetric reconstruction, and Lichtfeld Studio for 3DGS generation, to the Fontana d’Ercole at the Reggia di Venaria Reale (UNESCO World Heritage, Turin, Italy). Visual results show photorealistic, navigable 3DGS surrogates of architecturally complex heritage assets achievable by non-specialist operators within hours of acquisition and establish the empirical foundation for future quantitative evaluation. Full article

Wireless channel state information (CSI) enables device-free industrial safety monitoring, but the same representation can expose worker identity and sensitive locations. Existing CSI privacy methods typically protect fixed semantic targets or perturb the entire representation, providing limited control over what is protected and where modification occurs. This paper proposes PCGM-Net, a policy-conditionedlocal generative masking framework for selective CSI semantic release. To the best of our knowledge, it is the first representation-level Wi-Fi CSI framework to jointly combine explicit semantic privacy policies, a learned position-wise soft mask over the time–subcarrier plane, bounded residual transformation, and trusted retention of the source CSI. The mask determines where intervention is applied, whereas the residual patch determines how the selected regions are transformed. A single model supports identity-only, location-only, and joint protection. Under a test-set-isolated protocol, raw CSI yielded identity and location accuracies of 97.95% and 100.00%, respectively. Across three independently trained protection models selected using validation data only, joint protection retained $74.79\pm 0.96\%$ activity accuracy while reducing identity and location accuracies for the validation-selected evaluator to $6.09\pm 2.40\%$ and $0.29\pm 0.14\%$. Removing the privacy-margin objective restored identity and location accuracies to 98.55% and 100.00%, confirming that suppression arose from targeted semantic optimization rather than incidental signal corruption. An independent temporal bidirectional gated recurrent unit (BiGRU) model recovered $94.09\pm 1.16\%$ activity accuracy after protected-domain adaptation, and the complete pipeline required 2.18 ms mean graphics processing unit (GPU) latency. PCGM-Net therefore provides low-latency, policy-selective inference-time semantic shielding under a bounded, evaluator-dependent threat model rather than irreversible anonymization. Full article

Fast Reroute (FRR) after link failures is essential for carrier-grade Software-Defined Networking (SDN), yet hybrid deployments remain dominated by slow legacy routing convergence. This paper presents two port-state-driven FRR mechanisms for homogeneous and hybrid SDN networks. First, Port-State-Aware Fast Reroute (PSA-FRR) uses OpenFlow port-status events to trigger proactive, rule-based protection in the data plane. Second, Port-State-Aware Neural Fast Reroute (PSAR-FRR) formulates hybrid FRR as a controller-local multi-class classification problem and predicts the backup egress port from a port-centric state representation, enabling microsecond-scale decision latency. We evaluate the methods on the Abilene wide-area network (WAN) topology using Mininet with Open vSwitch (OVS) and a Ryu controller (homogeneous case) and Graphical Network Simulator-3 (GNS3) with Cisco IOS routers (hybrid baseline). In homogeneous SDN emulation, PSA-FRR restores connectivity within 30–100 ms under the evaluated configurations. In the hybrid baseline, conventional routing protocols converge in 13.8–256.1 s (Enhanced Interior Gateway Routing Protocol (EIGRP), Intermediate System to Intermediate System (IS-IS), Open Shortest Path First (OSPF), Border Gateway Protocol (BGP), and Routing Information Protocol (RIP)), confirming that control-plane recovery cannot meet a 50 ms target. Using the collected dataset, PSAR-FRR reduces controller decision time from 6.753 $\mathsf{\mu }$s (PSA-FRR rule evaluation) to 0.214 $\mathsf{\mu }$s (deep neural network (DNN) inference), a 31.5× speedup. These results show that port-state awareness combined with learned, controller-local policies can substantially reduce the decision-to-action latency of FRR, providing a practical path toward low-latency failure recovery in SDN migration scenarios. Full article

To explore the regulatory mechanisms of different vegetation types on soil crust grain-size characteristics in sandy lands, this study focused on five typical plant species (Haloxylon ammodendron, Artemisia ordosica, Nitraria tangutorum, Agriophyllum squarrosum, and Phragmites australis) in an artificial vegetation restoration area on the northeastern edge of the Ulan Buh Desert. Using laser granulometry and graphical methods, we systematically determined the soil particle size composition and parameters of the crust (Layer A) and sub-crust (Layer B) layers, and analyzed their correlations with plant morphological parameters (crown width, plant height, basal diameter). The results showed that (1) different vegetation types significantly increased the content of soil fine particulate matter (silt and clay), with fine sand accounting for 42.85% and silt accounting for 23.64%; (2) there are significant differences in the impact of different vegetation types on particle size parameters. The average particle size of soil crust under Phragmites australis is the smallest (1.91), and the sorting is the worst (standard deviation 2.01). Under the vegetation type of Nitraria tangutorum, the average particle size of the soil crust layer is the largest (5.25), and the fractal dimension is the highest (2.46). (3) The crown width, plant height, and basal diameter of vegetation are negatively correlated with mean particle size, kurtosis, and fractal dimension (r= −0.62 to −0.45), and positively correlated with standard deviation and skewness (r = 0.51 to 0.68). (4) The frequency curve indicates that vegetation types broaden the distribution range of soil particles, and Phragmites australis and Artemisia ordosica exhibit bimodal characteristics. This study reveals the impact of vegetation restoration on soil grain size parameters in arid regions. These findings provide actionable strategies for optimizing vegetation configuration in actual desert restoration projects, notably proposing a “herbs first, shrubs follow” approach that can be directly applied to enhance restoration efficiency. Full article

The cessation of recurrent anthropogenic activities can promote vegetation succession. In this paper, we report a case study of passive restoration of the herbaceous plant vegetation associated with cork oaks carried out by citizens in collaboration with local farmers in a suburban area of Rome (Italy). A sampling design has been carried out in two comparable patches using replicated plots: (i) a first patch corresponding to the passive restored area, evolving from an uncultivated field towards a cork oak forest, where the mowing activity was stopped in 2017, and (ii) a second patch corresponding to an uncultivated land periodically mowed as a control. We recorded 24 plant species in the restored patch and 9 in the control patch. The Shannon-Wiener diversity index was significantly higher in the restored patch when compared to the control. Whittaker diagrams, graphically representing evenness, showed significant differences among plotted values. The Chao 2 richness estimators evidence the differences between patches (52.17 species vs. 9), graphically observed in the sample rarefaction curves. An analysis in the 2017–2025 period showed a substantial increase in NDVI values in the restored patch (from 0.18 in 2017 to 0.28 in 2025; approximately +54% relative to 2017; mean NDVI increased from 0.181 in 2017 to 0.29 in 2025), indicating an increase in cover/biomass associated with the post-2017 restoration of the area. Suspending mowing, both humidity (due to the reduction in grass cover) and nutrients increase, and the pH is reduced (Ellenberg indices): it is possible that the young oak trees are comparatively more effective cation exchangers. Therefore, only a few years after mowing was suspended, we observed a marked recovery not only of the dominant cork oak component but also of the herbaceous species (Vulpio-Dasypyretum villosi association). Even young, isolated cork oak trees can act as nurse plants (or keystone structures), supporting many species and creating microhabitats for shade-tolerant plants. This passive restoration began when local citizens and a school asked landowners to stop mowing in an area where cork oaks were naturally regenerating, making it an example of autonomous citizen-led environmental management. Full article

Habitat fragmentation is reshaping ecosystems worldwide, reducing connectivity, eroding genetic diversity, and limiting species’ capacity to adapt to rapid environmental change. Conservation management responses to fragmentation generally follow three pathways: restoring habitats to rebuild connectivity, translocating individuals to bolster declining populations, and, more recently, directly managing adaptive genetic variation. We synthesize the ecological and genetic consequences of fragmentation and evaluate these management pathways along a continuum from landscape-scale interventions to genome-level strategies. Habitat restoration can reconnect patches and improve demo-graphic stability, but its genetic outcomes remain uncertain without baseline and post-restoration monitoring. Translocation offers a more immediate means of restoring gene flow but introduces demographic risks, potential impacts on source populations, and uncertainties in establishment and long-term fitness. Emerging genomic technologies now support a third approach: Targeted Genetic Intervention (TGI), which aims to accelerate the spread of beneficial genetic variants or enhance adaptive potential directly. Although promising, TGI faces significant challenges, including polygenic trait architecture, risks to genome-wide diversity, and the need for robust ethical and governance frameworks. Across all pathways, genetic data are essential for prioritizing actions, diagnosing vulnerable species and populations, and restoring the evolutionary potential necessary for long-term persistence in increasingly fragmented landscapes. Full article

The PHEND (Past Has Ears at Notre-Dame) collaborative research project is being carried out by a team of multidisciplinary researchers interested in the acoustic history of Notre-Dame Cathedral in Paris. The project involved the creation of seven digital models representing the interior of the monument between 1182 and 2018. To support one of the virtual reconstructions, that of 1868, a technical report was drawn up based on the written and iconographic archives of the restorations carried out between 1845 and 1870 by the architects Eugène Viollet-le-Duc (1814–1879) and Jean-Baptiste-Antoine Lassus (1807–1857). The archives come mainly from the “Fonds Viollet-le-Duc”, from the work diary of the “Médiathèque du patrimoine et de la photographie” (MPP), and from the archives of the Notre-Dame chapter. In order to select the most relevant data for the digital reconstruction, the research addresses specific questions regarding the cathedral’s materiality, such as structural modifications, restorations, and the choice of materials and furnishings. To understand how the interior of the cathedral was transformed in the 19th century, a detailed inventory of its condition was compiled at two points in time: at the beginning of the restoration in 1848 and following its completion in 1868. In parallel with this work, to provide a graphic representation of the changes that had occurred in each area, comparative illustrations were produced showing the situation before and after restoration. The modifications were then detailed by area: general restoration (vaults, openings, paving), and redevelopment of the choir and the main body of the building (chapels, transept, nave). This research revealed the building’s profound structural changes and the fact that the renovations spared no space. These included mainly modifications to the high windows, a complete redesign of the decorative layout of the choir and chapels, the restoration of all the vaults and paving at different levels, and a complete restoration of the organ. Full article

In immersive digital devices, high environmental complexity can lead to rendering delays and loss of interactive details, resulting in a fragmented experience. This paper proposes a lightweight NeRF (Neural Radiance Fields) modeling and multimodal perception fusion method. First, a sparse hash code is constructed based on Instant-NGP (Instant Neural Graphics Primitives) to accelerate scene radiance field generation. Second, parameter distillation and channel pruning are used to reduce the model’s size and reduce computational overheads. Next, multimodal data from a depth camera and an IMU (Inertial Measurement Unit) is fused, and Kalman filtering is used to improve pose tracking accuracy. Finally, the optimized NeRF model is integrated into the Unity engine, utilizing custom shaders and asynchronous rendering to achieve low-latency viewpoint responsiveness. Experiments show that the file size of this method in high-complexity scenes is only 79.5 MB ± 5.3 MB, and the first loading time is only 2.9 s ± 0.4 s, effectively reducing rendering latency. The SSIM is 0.951 ± 0.016 at 1.5 m/s, and the GME is 7.68 ± 0.15 at 1.5 m/s. It can stably restore texture details and edge sharpness under dynamic viewing angles. In scenarios that support 3–5 people interacting simultaneously, the average interaction response delay is only 16.3 ms, and the average jitter error is controlled at 0.12°, significantly improving spatial interaction performance. In conclusion, this study provides effective technical solutions for high-quality immersive interaction in complex public scenarios. Future work will explore the framework’s adaptability in larger-scale dynamic environments and further optimize the network synchronization mechanism for multi-user concurrency. Full article

Research in the field of cultural heritage has grown due to the need to preserve cultural assets that serve as witnesses to history and culture. In conservation and restoration, research on traditional papers is extensive, but translucent papers have received less attention. These documents, of proteinaceous, cellulosic, or synthetic origin, achieve transparency through processes that modify their structure, which makes them more vulnerable to aging. Their degradation is aggravated by inadequate storage and handling, posing challenges because they do not respond well to conventional treatments. This study analyzes these issues using documents from the late nineteenth and primarily the twentieth century, sourced from the Provincial Historical Archive of Granada and the Archive of the Higher Technical School of Architecture in Granada. Through visual, photographic, and bibliographic study, a theoretical and graphic catalogue of the most significant deteriorations has been developed. Concurrently, a physicochemical analysis was applied using techniques such as colorimetry, X-ray Fluorescence Spectroscopy (XRF), and Fourier-Transform Infrared Spectroscopy (FTIR). These tools make it possible to relate the material composition of the documents to their state of preservation. This work provides deeper knowledge about the degradation mechanisms of these supports and lays the foundations for the development of specific restoration strategies for this documentary typology. Full article

Aiming at the problems of complex image background and numerous interference factors faced by visual navigation systems in orchard environments, this paper proposes an orchard navigation line recognition method based on U-Net. Firstly, the drivable areas in the collected images are labeled using Labelme (a graphical tool for image annotation) to create an orchard dataset. Then, the Spatial Attention (SA) mechanism is inserted into the downsampling stage of the traditional U-Net semantic segmentation method, and the Coordinate Attention (CA) mechanism is added to the skip connection stage to obtain complete context information and optimize the feature restoration process of the drivable area in the field, thereby improving the overall segmentation accuracy of the model. Subsequently, the improved U-Net network is trained using the enhanced dataset to obtain the drivable area segmentation model. Based on the detected drivable area segmentation mask, the navigation line information is extracted, and the geometric center points are calculated row by row. After performing sliding window processing and bidirectional interpolation filling on the center points, the navigation line is generated through spline interpolation. Finally, the proposed method is compared and verified with U-Net, SegViT, SE-Net, and DeepLabv3+ networks. The results show that the improved drivable area segmentation model has a Recall of 90.23%, a Precision of 91.71%, a mean pixel accuracy (mPA) of 87.75%, and a mean intersection over union (mIoU) of 84.84%. Moreover, when comparing the recognized navigation line with the actual center line, the average distance error of the extracted navigation line is 56 mm, which can provide an effective reference for visual autonomous navigation in orchard environments. Full article

The palatial architecture of the Nasrid Alhambra in Granada was organized around courtyards that have been restored or transformed over the centuries. This research analyzes and graphically recreates a wooden gallery that was built in the Patio del Cuarto Dorado (Courtyard of the Golden Room) by the Catholic Monarchs, which disappeared around 1872. The methodology is based on the compilation of documentary sources and graphic analysis as the basis for new manual and digital drawings. Although no archival documentation detailing its construction or demolition has been identified, a large set of historical images (plans, views, and photographs) has been gathered, analyzed, and arranged chronologically. From these, freehand sketches were drawn to understand its construction elements, using other preserved galleries as a reference. Using this graphic documentation and measurements of the current courtyard, scale drawings were made. All of this allowed for the creation of a reconstructed digital model, the digital fabrication of a small-scale model, and the development of new representational graphics using advanced media. In this way, the aim is to understand and introduce the gallery that occupied this courtyard for centuries, offering a new view of the complex transformations of an architectural complex included on the UNESCO World Heritage List. Full article

Accurate modeling and simulation of lower limb rehabilitation exoskeleton (LLRE) enables effective control resulting in enhanced performance and ensuring efficient rehabilitation. There are two primary objectives of this study. First is to validate the existing models and second is to identify the optimal modeling approach for exoskeletons. For validation, firstly a lower limb rehabilitation exoskeleton is modeled using three different modeling approaches which include analytical modeling, bond graph modeling, and modeling through Simscape (SS). Thereafter, dynamic responses of analytical and graphical modeling are compared with SS model using key dynamic response parameters, including rise time, peak time, and others. The SS-based physical model can be employed for validation because SS, unlike mathematical modeling, uses unit-consistent physical domain data and, therefore, serves as an intermediate step between mathematical modeling and hardware validation. Secondly, to identify the most suitable modeling approach, a structured and comprehensive comparison of different modeling approaches based on aspects such as control domain, complexity, ease of use, and other relevant factors is carried out. The results highlight the qualitative strengths and limitations of the three approaches. Previous studies focus on individual methods and lack such comparison. This work contributes to the validation of models and identification of an efficient and effective modeling methodology for LLRE. The findings reveal that Simscape™ is the most suitable approach for modeling LLREs as it provides multidisciplinary system modeling and supports real-time simulation. The validated model can now be employed for advancements in model-based control design. Moreover, the identified optimal approach provides an insight to researchers and engineers for model selection in early-stage design and control development of complex mechatronic systems. Future work includes comparison of dynamic responses with actual hardware responses to experimentally validate the effectiveness of the model for real-world patient assistance and mobility restoration. Full article

Background/Objectives: Auricular epitheses play a vital role in restoring facial symmetry and patient confidence following congenital or acquired defects. Traditional wax-based fabrication is labor-intensive and lacks reproducibility. This study proposes and evaluates a simplified, digitally driven workflow for auricular prosthesis manufacturing, integrating virtual patient creation, mirrored ear design, and three-part mold fabrication using two design platforms—CATIA V5R21 (industrial CAD) and Blender (open-source graphics software). Key outcomes include mold reusability, patient-centered evaluation, and workflow feasibility. Methods: A 28-year-old female patient with right-sided microtia was selected. A light-based 3D facial scan was performed, and the intact contralateral ear was mirrored and adapted virtually to the defect site. Molds were designed in both CATIA and Blender using a standardized three-parallelepiped approach and printed using FDM technology (Elegoo Neptune 4 Plus). Five silicone epitheses were carefully fabricated with each mold. Surface trueness was evaluated with Geomagic Control X, while subjective assessments were conducted by an independent prosthetic team and the patient using Visual Analogue Scales (VAS). Results: Both design workflows resulted in clinically usable prostheses. No statistically significant difference in RMS deviation was observed (p = 0.53), although CATIA-derived epitheses achieved higher subjective scores (mean 9.22/10) compared to Blender (mean 7.67/10). The preferred prosthesis (CATIA #4) was selected for final pigmentation and delivery to the patient. All molds were reused five times without any structural damage or significant signs of wear. Conclusions: This study demonstrates that both CATIA and Blender can support an effective, patient-centered digital workflow for auricular prosthesis fabrication. The methodology enables reproducibility, mold reusability, and improved clinical accessibility of custom prosthetics. Full article

Gaussian Splatting (GS) methods, including 3DGS and 2DGS, have demonstrated significant effectiveness in real-time novel view synthesis (NVS), establishing themselves as a key technology in the field of computer graphics. However, GS-based methods still face challenges in rendering high-quality image details. Even when utilizing advanced frameworks, their outputs may display significant rendering artifacts when relying solely on a few input views, such as noise and blurriness. A reasonable approach is to conduct post-processing in order to restore clear details. Therefore, we propose GaussianEnhancer, a general GS-agnostic post-processor that employs a degradation-driven view blending method to improve the rendering quality of GS models. Specifically, we design a degradation modeling method tailored to the GS style and construct a large-scale training dataset to effectively simulate the native rendering artifacts of GS, enabling efficient training. In addition, we present a spatial information fusion framework that includes view fusion and depth modulation modules. This framework successfully integrates related images and leverages depth information from the target image to enhance rendering details. Through our GaussianEnhancer, we effectively eliminate the rendering artifacts of GS models and generate highly realistic image details. Based on GaussianEnhancer, we introduce GaussianEnhancer++, which features an enhanced GS-style degradation simulator, leading to improved enhancement quality. Furthermore, GaussianEnhancer++ can generate ultra-high-resolution outputs from noisy low resolution GS rendered images by leveraging the symmetry between high-resolution and low resolution images. Extensive experiments demonstrate the excellent restoration ability of GaussianEnhancer++ on various novel view synthesis benchmarks. Full article