Search Results (279)

This paper presents the integrated study of the funerary ara of Ofilius Ianuarius, discovered within the burial complex of Via Appia Antica 39, and explores its digital stratigraphic recontextualisation through two 3D semantic workflows. The research aims to evaluate the potential of stratigraphic 3D modelling as a tool for post-excavation analysis and transparent archaeological interpretation. Starting from a set of georeferenced photogrammetric models acquired between 2023 and 2025, the study tests two workflows: (1) an EMF-based approach using the Extended Matrix, Blender, and EMviq for stratigraphic relationship modelling and online visualisation; (2) a semantic integration method using the .gltf format and the CRMArcheo Annotation Tool developed in Blender, exported to the ATON platform. While both workflows enable accurate 3D documentation, they differ in their capacity for structured semantic enrichment and interoperability. The results highlight the value of combining reality-based models with semantically linked stratigraphic proxies and suggest future directions for linking archaeological datasets, ontologies, and interactive digital platforms. This work contributes to the ongoing effort to foster transparency, reproducibility, and accessibility in virtual archaeological reconstruction. Full article

The sparse iterative covariance-based estimation (SPICE) method has recently gained significant attraction in the field of scanning radar super-resolution imaging because of its angular resolution enhancement capability. However, it is unable to preserve the target profile, and the estimator is constrained by high computational complexity and memory consumption. In this paper, a grid-updating split SPICE-TV algorithm is presented. The method allows for the efficient updating of reconstruction results with both contour and resolution, and a recursive grid-updating implementation framework of the split SPICE-TV has the capability to reduce the computational complexity. First, the scanning radar angular super-resolution problem is transformed into a constrained optimization problem by simultaneously employing sparse covariance fitting criteria and TV regularization constraints. Then, the split Bregman method is employed to derive an efficient closed-form solution to the problem. Ultimately, the matrix inversion problem is transformed into an online iterative equation to reduce the computational complexity and memory consumption. The superiority of the proposed method is verified by simulation and experimental data. Full article

As part of the post-conflict reconstruction and recovery, the development of Kabul New City aims to bring relief to the existing capital city, Kabul, which has experienced exponential population growth, putting heavy pressure on its existing resources. Kabul New City is divided into four subsectors, and each of them is being developed and is expected to reach a target population by 2025, as defined by the master plan. The study’s objective is to determine which of the four zones are in demand and need to be prioritized for development, as per the model results. The data collection involves an online questionnaire, and the responses are collected from residents of Kabul and Herat. Agent-based modeling (ABM) is an emerging method of simulating urban dynamics. Cities are evolving continuously and are forming unique spatial patterns that result from the movement of residents in search of new locations that accommodate their needs and preferences. An agent-based model is developed using the weighted random selection process based on household size and income levels. The agents are the residents of Kabul and Herat, and the environment is the land use classification image using the Sentinel 2 image of Kabul New City. The barren class is treated as the developable area and is divided into four sub-sectors. The model simulates three alternative growth rate scenarios, i.e., ambitious, moderate, and steady. The results of the simulation reveal that the sub-sector Dehsabz South, being closer to Kabul city, is in higher demand. Barikab is another sub-sector high in demand, which has connectivity through the highway and is an upcoming industrial hub. Full article

Background/Objectives: The gold standard of treatment for both melanoma and non-melanoma skin cancers is wide surgical resection to obtain oncological radicality, which occasionally results in functional or aesthetic impairment, potentially affecting quality of life. Despite the increased complexity of the technique, extended duration of hospitalization, and prolonged surgical operative times, microsurgery can facilitate the reconstruction of locally invasive skin cancers following ablative surgery and may yield superior functional and aesthetic outcomes. Consequently, microsurgical reconstruction is more likely to be necessary if a large skin tumor requires excision. However, the impact of this extensive and complex procedure on patients with skin cancer has not yet been fully elucidated. The objective of this research was to critically analyze the utilization of free flap reconstruction subsequent to skin cancer therapy. Through a comprehensive examination of published data, this study aimed to assess the potential benefits and drawbacks associated with this reconstructive approach. Methods: A systematic review of studies that were published from January 2004 to May 2024 was conducted using the MEDLINE online database search. To present an evidence summary and provide a systematic approach and quality assessment, the GRADE^® rating was applied to the results. Results: This review summarizes the oncological and clinical data, including previous interventions, adjuvant and neoadjuvant therapies, nodal status, distant metastasis, and follow-up time. Surgical outcome parameters such as healing time, flap survival, revision rate success, and minor and major complications were documented. Along with the findings, a quality assessment of the studies was also provided. Conclusions: This systematic review underscores the extensive use and efficacy of microsurgery for reconstruction after skin cancer excision; however, the literature remains limited by inconsistent reporting of oncological outcomes and the lack of a standardized approach to evaluate the impact of free flap reconstruction on both immediate and long-term cancer-specific results. Full article

The 3D Gaussian splatting technique demonstrates significant efficiency advantages in real-time scene reconstruction. However, when its initialization process relies on traditional SfM methods (such as COLMAP), there are obvious bottlenecks, such as high computational resource consumption, as well as the decoupling problem between camera pose optimization and map construction. This paper proposes an online 3DGS optimization system based on ROS. Through the design of a loose-coupling architecture, it realizes real-time data interaction between the frontend SfM/SLAM module and backend 3DGS optimization. Using ROS as a middleware, this system can access the keyframe poses and point-cloud data generated by any frontend algorithms (such as ORB-SLAM, COLMAP, etc.). With the help of a dynamic sliding-window strategy and a rendering-quality loss function that combines L1 and SSIM, it achieves online optimization of the 3DGS map. The experimental data shows that compared with the traditional COLMAP-3DGS process, this system reduces the initialization time by 90% and achieves an average PSNR improvement of 1.9 dB on the TUM-RGBD, Tanks and Temples, and KITTI datasets. Full article

In the wake of intensifying global Extreme Climate Incidents (ECIs), which have particularly noticeable effects on indigenous populations, integrating value-driven education has become a global imperative. While Education for Sustainable Development (ESD) for SDG 13 climate action has been widely adopted in science and engineering curricula, language and culture programs remain underexplored as venues for transformative sustainability learning in Global Competence (GC). Learners in these fields often demonstrate critical literacy and global awareness, but lack the interdisciplinary tools and strategies to translate values into climate-conscious behavior. This study responds to this gap by incorporating virtual reality (VR) into a modified Global Competence Framework (GCF) in an experiment study within intercultural communication courses (N = 303, VR explorative group = 152, control group = 151). A mixed methodological approach was adopted by evaluating pretest–posttest quantitative data of ESD knowledge, attitudes, behaviors, and qualitative data of critical online reflection. The results demonstrate increase localized ESD knowledge in terms of climate, attitudes, and ecological behaviors in both groups in the lived experience of GCF, yet the VR explorative group showed a greater improvement in knowledge and behavior because of their visualization of the 3D rotation of rarely acquired scientific knowledge in monsoonal movement and the local indigenous village reconstruction after destruction. By localizing the Typhoon Morakot tragedy that devastated Shiaolin Village, the intervention provided a culturally specific and interactive context for ESD concepts, enabling interdisciplinary learners to experience climate injustice firsthand with value-based strategies while critically reflecting on global responsibility and sustainable action. Full article

In the assembly of spacecraft cabins, the presence of uncertain and time-varying temperature environments can induce thermal deformation in bulkheads, potentially affecting dimensional stability. Online sensing of thermal deformation is critical for mitigating such risks. However, conventional finite element methods (FEMs) rely on cascading thermal and structural analyses, which suffer from inefficiency. To address this issue, we propose a methodology that integrates a physical model with a data-driven temperature field measurement technique, demonstrated through case studies involving a spacecraft porthole bulkhead. First, leveraging the geometric invariance of the bulkhead during assembly, a purely static FE model is established offline. Second, multi-point temperature measurements combined with Kriging estimation are employed to directly reconstruct the temperature field, circumventing the computationally intensive FEM-based thermal analysis process. Finally, by utilizing the precomputed inverse stiffness matrix and performing an online conversion from temperature to equivalent forces, thermal deformation is rapidly resolved. The numerical results demonstrate that the root-mean-square errors of the predicted full-field deformation are maintained at the micron level, with an average computation time of less than 0.14 s. Furthermore, a meticulously designed experiment was conducted, where the predicted thermal displacements of several key points showed good agreement with measurements by means of a laser tracker. This research provides a promising tool to achieve digital twinning of thermal deformation states for aerospace components. Full article

Real aperture radar (RAR) can acquire the forward-looking target scene of interest continuously in scanning mode by arbitrary imaging geometry; however, the achievable angular resolution is predominantly governed by the physical dimensions of the antenna’s aperture. In contemporary radar imaging methodologies, the reconstruction of sparsely distributed targets can be effectively formulated as an $L_1$-regularized optimization framework through the exploitation of a priori sparsity constraints, thereby enabling the generation of enhanced-resolution forward-looking radar imagery. Nevertheless, traditional target reconstruction methods based on the sparse regularization framework are implemented after batch data collection, which comes at the cost of significant operational complexity and storage space. To address this challenge, an online sparse reconstruction method based on a beam recursive-sliding (BRS) updating framework is proposed to achieve fast target reconstruction. First, the antenna measurement matrix is repaired to reduce the imaging edge information error. Then, due to the independence of the echo data within two beamwidths, a beam recursive updating method is proposed for each two beamwidths echo data by the structural properties of the repaired antenna measurement matrix. Finally, based on the proposed beam recursive updating method, a sliding updating approach is proposed for the whole imaging region to reduce the computational redundancy and storage requirement. Simulation and experimental data demonstrate the effectiveness of the proposed BRS updating framework. Full article

With the increasing service duration of dams, the analysis of seepage pressure monitoring data plays a crucial role in ensuring the safety of seepage behavior. However, seepage pressure monitoring systems are often subject to environmental disturbances, sensor failures, and other interfering factors, leading to anomalous measurements during data acquisition. To objectively reflect the true operational state of dams and address the limitations of conventional detection and identification methods—such as low efficiency, high subjectivity in evaluation, and ineffective recognition of multi-category outliers—this study constructed an online detection, identification, and correction method for multi-category anomalous values. Specifically, an enhanced particle filter incorporating a Bernoulli probability model is constructed to characterize multi-category outliers in seepage pressure monitoring data, building upon the traditional particle filter framework. Following online detection and identification, the MissForest imputation method is employed to rectify the anomalous values. In the case study, both the false detection rate and missed detection rate ranged between 0% and 10%. Comparative experiments with three alternative methods revealed significant differences in data reconstruction performance, with the proposed method achieving the highest R² score (0.861) and the lowest RMSE (0.050) and MAE (0.052). The results demonstrate that the proposed method effectively identifies outliers, achieves superior reconstruction of seepage pressure data, and minimizes errors. Furthermore, this research provides a novel approach for detecting anomalous seepage pressure measurements and evaluating dam safety conditions. Full article

Aiming to solve the problems of low precision and poor efficiency caused by relying on manual experience during the manual polishing of blades, a multi-view spark image detection method based on YOLOv8 transfer learning is proposed. A multi-pose spark image dataset including front, side, and 45° angle views is constructed, and the cross-view detection task is achieved for the first time. The generalization ability of the model is enhanced through the following innovative strategies: (1) a cross-view transfer learning framework based on dynamic anchor box optimization is designed, and the parameters of the front spark detection model YOLOv8 are transferred to the side and 45°-angle detection tasks; (2) an attention-guided feature alignment module is introduced to alleviate the feature distribution shift caused by view differences; and (3) a curriculum learning strategy is adopted, where the datasets of different views are trained separately first and then sampled to reconstruct the dataset for further training, gradually increasing the weight of samples from complex views. The experimental results show that on the self-built multi-view dataset (containing 3000 annotated images), this method achieves an average detection accuracy of 98.7%, which is 14.2% higher than that of the original YOLOv8 model. The inference speed reaches 55 FPS on an NVIDIA RTX 4090, meeting the requirements of industrial online monitoring. The research results provide key technical support for the intelligent prediction of the material removal rate in the precision machining of blades and have the potential for rapid deployment in industrial scenarios. Full article

Virtual reality (VR) offers immersive and interactive experiences that enable users to explore and understand heritage sites beyond the constraints of physical preservation. This study examines the integration of VR in historical architecture, focusing on its potential to enhance education, engagement, and preservation efforts. Perspectives from professionals in architectural history, interior design, architecture, and digital humanities are analysed to assess the fidelity, challenges, and opportunities of the utilisation of VR in preserving historical sites. A quantitative methodology was employed through an online survey distributed to 60 experts, in which 57 responses were recorded. The survey examined VR reconstruction accuracy, integration challenges and opportunities for enhancing educational engagement and site preservation. The findings indicate a strong positive perception of VR’s potential, emphasising accuracy and expert collaboration. Challenges include hardware limitations and insufficient historical data, while balancing fidelity with usability emerged as a key concern. The study concludes that VR holds significant potential to enrich historical preservation through interactive learning, contributing valuable insights into effective and engaging VR applications in heritage conservation. Full article

A star tracker is widely used as a high-precision attitude measurement device for spacecraft. It calculates attitude by extracting the magnitude and the position of presumed detected stars by a CCD/CMOS sensor and matching them with stars in the star catalog. The traditional star identification methods typically require the selection of specific anchor stars, which may cause insufficient identification accuracy as the number of stars used in the rough search is limited. In this paper, we propose a star identification method based on spatial projection, which starts with preprocessing. Then, a method for online expansion and reconstruction of the star catalog is proposed, which provides more stored star data. After the rough recognition and coordinate system transformation, the final identification is realized in the polar coordinate system. All the star points in the star image are identified, and the attitude information is obtained at the same time. The performance of the identification method is verified by real night sky experiments. Stray light experiments are also carried out to prove good noise immunity capabilities. Compared with the traditional subgraph isomorphism method, the proposed method makes it easier to adjust the number of recognizable stars in the field of view and better recognition of specific areas. The method is of great significance for future tasks such as attitude measurement, celestial navigation, remote sensing measurement, and space target observation and tracking. Full article

Threshold multi-party fully homomorphic encryption (TMFHE) schemes enable efficient computation to be performed on sensitive data while maintaining privacy. These schemes allow a subset of parties to perform threshold decryption of evaluation results via a distributed protocol without the need for a trusted dealer, and provide a degree of fault tolerance against a set of corrupted parties. However, existing TMFHE schemes can only provide correctness and security against honest-but-curious parties. We construct a compact TMFHE scheme based on the Learning with Errors (LWE) problem. The scheme applies Shamir secret sharing and share resharing to support an arbitrary t-out-of-N threshold access structure, and enables non-interactive reconstruction of secret key shares using additive shares derived from the current set of online participants. Furthermore, the scheme implements commitment and non-interactive zero-knowledge (NIZK) proof techniques to verify the TMFHE operations. Finally, our experiments demonstrate that the proposed scheme achieves active security against malicious adversaries. It overcomes the limitation of existing TMFHE schemes that can only guarantee correct computation under passive semi-honest adversaries. Full article

In laser-directed energy deposition (LDED) additive manufacturing, stress-induced deformation and cracking often occur unexpectedly, and, once initiated, they are difficult to remedy. To address this issue, we previously proposed the Dynamic Counter Method (DCM), which monitors internal stress based on deposition layer shrinkage, enabling real-time stress monitoring without damaging the component. To validate this method, we used AlSi10Mg material, which has a low melting point and high reflectivity, and developed a high-precision segmentation network based on DeeplabV3+ to test its ability to measure shrinkage in high-exposure images. Using a real-time reconstruction model, stress calculations were performed with DCM and thermal–mechanical coupling simulations, and the results were validated through XRD residual stress testing to confirm DCM’s accuracy in calculating internal stress in aluminum alloys. The results show that the DeeplabV3+ segmentation network accurately extracted deposition-layer contours and shrinkage information. Furthermore, DCM and thermal–mechanical coupling simulations showed good consistency in residual stress distribution, with all results falling within the experimental error range. In terms of stress evolution trends, DCM was also effective in predicting stress variations. Based on these findings, two loading strategies were proposed, and, for the first time, DCM’s application in online stress monitoring of large LDED components was validated, offering potential solutions for stress monitoring in large-scale assemblies. Full article

The development of restorative tourism in post-war countries is crucial to economic recovery, cultural preservation, and social stabilization. While various nations have adopted different reconstruction strategies following conflicts, Ukraine’s situation requires an innovative and large-scale approach due to the extensive damage inflicted on infrastructure, cultural heritage, and tourism assets. This study explores the role of virtual and augmented reality technologies in restoring tourism potential, particularly in preserving destroyed cultural heritage through digitalization. Virtual tourism is increasingly relevant to maintaining cultural identity, attracting investment, and fostering international engagement. This study examines the evolution of digital tourism solutions, consumer behaviour shifts towards online leisure, and the integration of geoinformation systems for post-crisis planning. The findings emphasize that Ukraine’s tourism sector must adapt to digital trends while developing physical infrastructure, ensuring a comprehensive, resilient, and future-oriented restoration strategy. This study provides recommendations for leveraging innovation in post-crisis tourism development. It explains how the change in the paradigm of consumption of recreation and leisure services in the modern world impels the restoration of the destroyed tourism infrastructure. Furthermore, it highlights the importance of strategic migration policies to rebuild the labour market, which is essential for sustainable recovery. Full article