Search Results (349)

The Lemaître–Tolman–Bondi (LTB) solution to the Einstein equations describes the dynamics of a self-gravitating spherically symmetric dust cloud with an arbitrary density profile and any distribution of initial velocities, encoded in three arbitrary functions $f\left(R\right)$, $F\left(R\right)$, and ${\tau }_0\left(R\right)$, where R is a radial coordinate in the comoving reference frame. A particular choice of these functions corresponds to a wormhole geometry with a throat defined as a sphere of minimum radius at a fixed time instant. In this paper we explore LTB wormholes and discuss their possible observable appearance, studying in detail the effects of gravitational lensing by such objects. For this aim, we study photon motion in wormhole space-time inscribed in a closed Friedmann dust-filled universe and find the wormhole shadow as it could be seen by a distant observer. Because the LTB wormhole is a dynamic object, we analyze the dependence of its shadow size on the observation time and on the initial size of the wormhole region. We reveal that the angular size of the shadow exhibits a non-monotonic dependence on the observation time. At early times, the shadow size decreases as photons with smaller angular momentum gradually reach the observer. At later times, the expansion of the Friedmann universe becomes a dominant factor that leads to an increase in the shadow size. Full article

The rapid spread of the global COVID-19 pandemic has rapidly changed people’s communication demands and shifted them to digital channels, thus increasing the use of social networks more than ever. However, the increased use of social networks has also led to emotional confusion that has emerged with the fake news problem. As a result of limited studies on fine-grained sentiment analysis of fake news, this study comprehensively presents a sentiment analysis of fake news across seven main categories. For this reason, this study aims to address this problem with a cloud-based system called SA-ES using the BERT algorithm to understand the emotional dimension of fake news spreading on Twitter (now X). In this context, the sentiment analysis of fake news has been examined in seven categories. The SA-ES system consists of 248,262 training datasets and 10,202 test datasets for testing and evaluation. The SA-ES system was trained with the BERT algorithm in two epochs during the modeling phase and reached 99% accuracy. We hope that the development of this SA-ES system will fill a gap in the literature and also help take measures toward a healthier society by analyzing the moods of people who share fake news on social networks. Full article

Land Surface Temperature (LST) is a critical variable for understanding land–atmosphere interactions and is widely applied in urban heat monitoring, evapotranspiration estimation, near-surface air temperature modeling, soil moisture assessment, and climate studies. MODIS LST products, with their global coverage, long-term consistency, and radiometric calibration, are a major source of LST data. However, frequent data gaps caused by cloud contamination and atmospheric interference severely limit their applicability in analyses requiring high spatiotemporal continuity. This study presents a seamless MODIS LST reconstruction framework that integrates multi-source data fusion and a multi-stage optimization strategy. The method consists of three key components: (1) topography- and land cover-constrained spatial interpolation, which preliminarily fills orbit-induced gaps using elevation and land cover similarity criteria; (2) pixel-level LST reconstruction via random forest (RF) modeling with multi-source predictors (e.g., NDVI, NDWI, surface reflectance, DEM, land cover), coupled with HANTS-based temporal smoothing to enhance temporal consistency and seasonal fidelity; and (3) Poisson-based image fusion, which ensures spatial continuity and smooth transitions without compromising temperature gradients. Experiments conducted over two representative regions—Huainan and Jining—demonstrate the superior performance of the proposed method under both daytime and nighttime scenarios. The integrated approach (Step 3) achieves high accuracy, with correlation coefficients (CCs) exceeding 0.95 and root mean square errors (RMSEs) below 2K, outperforming conventional HANTS and standalone interpolation methods. Cross-validation with high-resolution Landsat LST further confirms the method’s ability to retain spatial detail and cross-scale consistency. Overall, this study offers a robust and generalizable solution for reconstructing MODIS LST with high spatial and temporal fidelity. The framework holds strong potential for broad applications in land surface process modeling, regional climate studies, and urban thermal environment analysis. Full article

The Automated Building Information Modeling (BIM) reconstruction of existing building curtain walls is crucial for promoting digital Operation and Maintenance (O&M). However, existing 3D reconstruction technologies are mainly designed for general architectural scenes, and there is currently a lack of research specifically focused on the BIM reconstruction of curtain walls. This study proposes a BIM reconstruction method from unmanned aerial vehicle (UAV) images based on neural radiance field (NeRF) and deep learning-based semantic segmentation. The proposed method compensates for the lack of semantic information in traditional NeRF methods and could fill the gap in the automatic reconstruction of semantic models for curtain walls. A comprehensive high-rise building is selected as a case study to validate the proposed method. The results show that the overall accuracy (OA) for semantic segmentation of curtain wall point clouds is 71.8%, and the overall dimensional error of the reconstructed BIM model is less than 0.1m, indicating high modeling accuracy. Additionally, this study compares the proposed method with photogrammetry-based reconstruction and traditional semantic segmentation methods to further validate its effectiveness. Full article

Background/Objectives: Digital Smile Design software tools facilitates aesthetic planning and improves communication between clinicians, patients, and dental laboratories. These software tools have been developed to support facial and dental analysis and to assist users in creating an ideal smile integrated with the patient’s appearance. This study aimed to compare the usability of three DSD software tools—Preteeth AI Pro (version 6.0.0), SmileCloud, and Medit Link (version 3.4.3)—using the System Usability Scale. Methods: Twenty-three prosthodontists and prosthodontics residents evaluated each tool following a standardized usage protocol. After completing Digital Smile Designs in each application, participants filled out a 10-item System Usability Scale questionnaire (score 0–100). Descriptive statistics were calculated, and intergroup comparisons were performed using one-way ANOVA (p < 0.05). Results: Mean System Usability Scale scores were 74.24 (Preteeth AI Pro), 80.33 (SmileCloud), and 73.15 (Medit Link). SmileCloud obtained the highest score (A−grade, Curved Grading Scale), indicating “good to very good” usability. No statistical significances were found between the three software tools (F = 1.04, p = 0.36). Conclusions: All three Digital Smile Design software tools achieved System Usability Scale scores above the usability benchmark of 68, with SmileCloud demonstrating the most favorable user experience. These findings may assist clinicians in selecting intuitive and efficient Digital Smile Design platforms to optimize aesthetic treatment workflows. Full article

Active galactic nuclei (AGNs) often exhibit broad-line regions (BLRs), populated by high-velocity clouds in approximately Keplerian orbits around the central supermassive black hole (SMBH) at subparsec scales. During episodes of intense accretion at super-Eddington rates, the accretion disk can launch a powerful, radiation-driven wind. This wind may overtake the BLR clouds, forming bowshocks around them. Two strong shocks arise: one propagating into the wind, and the other into the cloud. If the shocks are adiabatic, electrons and protons can be efficiently accelerated via a Fermi-type mechanism to relativistic energies. In sufficiently dense winds, the resulting high-energy photons are absorbed and reprocessed within the photosphere, while neutrinos produced in inelastic $pp$ collisions escape. In this paper, we explore the potential of super-accreting AGNs as neutrino sources. We propose a new class of neutrino emitter: an AGN lacking jets and gamma-ray counterparts, but hosting a strong, opaque, disk-driven wind. As a case study, we consider a supermassive black hole with $M_{\mathrm{BH}}={10}^6{M}_{\odot }$ and accretion rates consistent with tidal disruption events (TDEs). We compute the relevant cooling processes for the relativistic particles under such conditions and show that super-Eddington accreting SMBHs can produce detectable neutrino fluxes with only weak electromagnetic counterparts. The neutrino flux may be observable by the next-generation IceCube Observatory (IceCube-Gen2) in nearby galaxies with a high BLR cloud filling factor. For galaxies hosting more massive black holes, detection is also possible with moderate filling factors if the source is sufficiently close, or at larger distances if the filling factor is high. Our model thus provides a new and plausible scenario for high-energy extragalactic neutrino sources, where both the flux and timescale of the emission are determined by the number of clouds orbiting the black hole and the duration of the super-accreting phase. Full article

Various fusion methods of optical satellite images have been proposed for monitoring heterogeneous farmlands requiring high spatial and temporal resolution. In this study, a three-meter normalized difference vegetation index (NDVI) was generated by applying the spatiotemporal fusion (STF) method to simultaneously generate a full-length normalized difference vegetation index time series (SSFIT) and enhanced spatial and temporal adaptive reflectance fusion method (ESTARFM) to the NDVI of Sentinel-2 (S2) and PlanetScope (PS), using images from 2019 to 2021 of rice paddy and heterogeneous cabbage fields in Korea. Before fusion, S2 was processed with the maximum NDVI composite (MNC) and the spatiotemporal gap-filling technique to minimize cloud effects. The fused NDVI image had a spatial resolution similar to PS, enabling more accurate monitoring of small and heterogeneous fields. In particular, the SSFIT technique showed higher accuracy than ESTARFM, with a root mean square error of less than 0.16 and correlation of more than 0.8 compared to the PS NDVI. Additionally, SSFIT takes four seconds to process data in the field area, while ESTARFM requires a relatively long processing time of five minutes. In some images where ESTARFM was applied, outliers originating from S2 were still present, and heterogeneous NDVI distributions were also observed. This spatiotemporal fusion (STF) technique can be used to produce high-resolution NDVI images for any date during the rainy season required for time-series analysis. Full article

As an important representation form of three-dimensional scenes, the point cloud contains rich geometry and attribute information. The video-based point cloud compression standard (V-PCC) divides and projects three-dimensional data directionally onto a two-dimensional plane. The generated geometric and attribute graphs contain occupied pixels obtained by projection and unoccupied pixels used for smooth filling. Among them, the non-occupied pixels have no practical effect on the reconstructed point cloud. However, in the process of encoding bitrate allocation, V-PCC still uses the original bitrate control method, resulting in insufficient bitrate utilization efficiency. To this end, this paper proposes a method for optimizing the unoccupied pixels of point cloud videos based on V-PCC and jointly controlling the coding rate of geometries and attribute graphs. For geometric graphs, this paper improves the allocation of bitrate weights based on whether the encoded blocks contain non-occupied pixels and the proportion of occupied pixels, and stops allocating bitrates to encoded blocks that are all non-occupied pixels. For the attribute graph, the input pixel improvement algorithm is designed by using the occupation map, and the invalid unoccupied pixel information is cavitation. Experiments show that under the All Intra configuration, compared with the original scheme, this method reduces the Geom.BD-GeomRate by an average of 15.67% and 16.68%, respectively, in the point-to-point D1 and point-to-face D2 metrics. The end-to-end BD-AttrRate is reduced by an average of 4.38%, 0.68%, and 1.74%, respectively. Overall, the average savings are 29.88%, 31.50%, 5.50%, 2.66%, and 3.34%, respectively, achieving bitrate optimization and effectively controlling encoding loss. Full article

Accurate extraction of the arch profile, the key spatial geometric parameter of the core load-bearing component in arch bridges, is crucial for construction process control and for achieving the designed final bridge configuration. To overcome the limitations of existing methods—geometric information loss, sensitivity to noise, and inefficiency—when extracting continuous, precise profiles from point clouds of complex spatially curved arch ribs, this paper proposes a multi-step point cloud processing workflow. The approach integrates geometric feature constraints specific to arch bridges to enable automated, high-precision extraction of the arch profile during construction. The approach comprises three steps. First, arch point cloud subset partitioning: the primitive arch point cloud is efficiently divided using parameters from down-sampling arch point cloud data. Second, component segmentation: a Random Sample Consensus (RANSAC) algorithm, optimized with cylindrical geometric constraints, is then employed to precisely segment the point cloud of individual arch tube components from each subset point cloud. Third, arch profile extraction: the geometric invariance of the bottom edge of each arch tube is leveraged to identify feature points via local coordinate system transformation and longitudinal constraints. These feature points are then spliced together to reconstruct the complete arch profile. The proposed method is employed in multiple construction stages of a concrete-filled steel tubular (CFST) arch bridge and quantifies the vertical deformation between adjacent stages. Compared with Total Station (TS) measurements, the average error ranged from 0.24 mm to 4.13 mm, with an overall average error of 2.105 mm, demonstrating accuracy and reliability. Full article

As Industrial Control Systems (ICSs) increasingly adopt cloud–edge collaborative architectures, they face escalating risks from complex cross-domain cyber threats. To address this challenge, we propose a novel threat assessment framework specifically designed for cloud–edge-integrated ICSs. Our approach systematically identifies and evaluates security risks across cyber and physical boundaries by building a structured dataset of ICS assets, attack entry points, techniques, and impacts. We introduce a unique set of evaluation indicators spanning four key dimensions—system modules, attack paths, attack methods, and potential impacts—providing a holistic view of cyber threats. Through simulation experiments on a representative ICS scenario inspired by real-world attacks, we demonstrate the framework’s effectiveness in detecting vulnerabilities and quantifying security posture improvements. Our results underscore the framework’s practical utility in guiding targeted defense strategies and its potential to advance research on cloud–edge ICS security. This work not only fills gaps in the existing methodologies but also provides new insights and tools applicable to sectors such as smart grids, intelligent manufacturing, and critical infrastructure protection. Full article

Reliable monitoring of snow cover in mountainous regions remains a challenge due to frequent cloud cover and the revisit limitations of optical satellites. This study compares satellite snow-cover records with >99,000 ground-based time-lapse camera observations across northern Spain (2003–2025). Cloud cover caused major data loss, with up to 57% of satellite images affected. Effective revisit intervals (the average time between usable images) diverge substantially from nominal values: 2.3 days for MODIS, 6.9 days for Sentinel-2, and over 21 days for Landsat. A hierarchical multisensor approach with 5-day gap-filling reduced this to just 1.3 days. On dates when cameras confirmed snow, satellites underestimated snow presence by 61.6% (Sentinel-2), 71.5% (Landsat), and 79.7% (MODIS), though gap-filling approaches reduced underestimation to 49.4%—deficits largely attributable to cloud-obscured scenes. When both satellite and camera provided cloud-free observations for the same date and location, classification agreement exceeded 85%. Despite this, satellites consistently failed to detect short-lived snow events and introduced temporal biases. On average, Snow Onset Dates were detected 13–52 days later, and Snow Melt-Out Dates differed by up to 40 days compared to camera-derived records. These results have implications for snow-cover monitoring using satellite images and highlight the need for integrating ground-based observations to compensate for satellite limitations and improve snow cover seasonality assessments in complex terrains. Full article

This paper presents an innovative multi-criteria visual quality control algorithm designed for deployment on cost-effective Edge devices within the Industrial Internet of Things environment. Traditional industrial vision systems are typically associated with high acquisition, implementation, and maintenance costs. The proposed solution addresses the need to reduce these costs while maintaining high defect detection efficiency. The developed algorithm largely eliminates the need for time- and energy-intensive neural network training or retraining, though these capabilities remain optional. Consequently, the reliance on human labor, particularly for tasks such as manual data labeling, has been significantly reduced. The algorithm is optimized to run on low-power computing units typical of budget industrial computers, making it a viable alternative to server- or cloud-based solutions. The system supports flexible integration with existing industrial automation infrastructure, but it can also be deployed at manual workstations. The algorithm’s primary application is to assess the spread quality of thick liquid mold filling; however, its effectiveness has also been demonstrated for 3D printing processes. The proposed hybrid algorithm combines three approaches: (1) the classical SSIM image quality metric, (2) depth image measurement using Intel MiDaS technology combined with analysis of depth map visualizations and histogram analysis, and (3) feature extraction using selected artificial intelligence models based on the OpenCLIP framework and publicly available pretrained models. This combination allows the individual methods to compensate for each other’s limitations, resulting in improved defect detection performance. The use of hybrid metrics in defective sample selection has been shown to yield superior algorithmic performance compared to the application of individual methods independently. Experimental tests confirmed the high effectiveness and practical applicability of the proposed solution, preserving low hardware requirements. Full article

Surface solar radiation (SSR) is a critical component of the Earth’s energy balance and plays a pivotal role in climate modelling, hydrological processes, and solar energy planning. In data-scarce regions like India, where dense ground-based radiation networks are limited, reanalysis and satellite-derived SSR datasets are often utilized to fill observational gaps. However, these datasets are subject to systematic biases, particularly under diverse sky and seasonal conditions. This study presents a comprehensive evaluation of four widely used SSR datasets: ERA5, IMDAA, MERRA2, and CERES, against high-quality in situ observations from 27 India Meteorological Department (IMD) stations, for the period 1985–2020. The assessment incorporates multi-scale temporal analysis (daily/monthly), spatial validation, and sky-condition stratification via the clearness index (K_t). The results indicate that CERES exhibits the best overall performance with the lowest RMSE (16.30 W/m²), minimal bias (–2.5%), and strong correlation (r = 0.97; p = 0.01), particularly under partly cloudy conditions. ERA5, with a finer spatial resolution, also performs robustly (RMSE = 20.80 W/m²; MBE = –0.8%; r = 0.94; p = 0.01), showing consistent agreement with observed seasonal cycles, though slightly underestimating SSR during monsoonal cloud cover. MERRA2 shows moderate overestimation (+4.4%) with region-specific bias variability, while IMDAA demonstrates persistent overestimation (+10.2%) across all conditions, highlighting limited sensitivity to atmospheric transparency. Importantly, this study reconciles apparent contradictions between monthly and sky condition-based bias analyses, attributing them to aggregation differences. While reanalysis datasets overestimate SSR during the monsoon on average, they tend to underestimate it under heavily overcast conditions. These insights are critical for guiding the selection and application of SSR datasets in solar energy modelling, SPV system design, and climate diagnostics across India’s heterogeneous atmospheric regimes. Full article

Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp transitions between preference levels. These assumptions can lead to decision outcomes with insufficient differentiation, limited discriminatory capacity, and potential issues in consistency and sensitivity. To overcome these limitations, this study proposes a novel fuzzy decision-making framework by integrating Quasi-D-Overlap functions into the fuzzy MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution) method. Quasi-D-Overlap functions represent a generalized extension of classical overlap operators, capable of capturing partial overlaps and interdependencies among criteria while preserving essential mathematical properties such as associativity and boundedness. This integration enables a more intuitive, flexible, and semantically rich modeling of real-world fuzzy decision problems. In the context of real-time health monitoring, a case study is conducted using a hybrid edge–cloud architecture, involving sensor tasks such as heartrate monitoring and glucose level estimation. The results demonstrate that the proposed method provides greater stability, enhanced discrimination, and improved responsiveness to weight variations compared to traditional fuzzy MCDM techniques. Furthermore, it effectively supports decision-makers in identifying optimal sensor alternatives by balancing critical factors such as accuracy, energy consumption, latency, and error tolerance. Overall, the study fills a significant methodological gap in fuzzy MCDM literature and introduces a robust fuzzy aggregation strategy that facilitates interpretable, consistent, and reliable decision making in dynamic and uncertain healthcare environments. Full article

Aiming at addressing the defect of the data blindness of a LiDAR point cloud in transparent media such as glass in low illumination environments, a new method is proposed to realize covert target reconnaissance, identification and ranging using the fusion of a shimmering polarized image and a laser LiDAR point cloud, and the corresponding system is constructed. Based on the extraction of pixel coordinates from the 3D LiDAR point cloud, the method adds information on the polarization degree and polarization angle of the micro-light polarization image, as well as on the reflective intensity of each point of the LiDAR. The mapping matrix of the radar point cloud to the pixel coordinates is made to contain depth offset information and show better fitting, thus optimizing the 3D point cloud converted from the micro-light polarization image. On this basis, algorithms such as 3D point cloud fusion and pseudo-color mapping are used to further optimize the matching and fusion procedures for the micro-light polarization image and the radar point cloud, so as to successfully realize the alignment and fusion of the 2D micro-light polarization image and the 3D LiDAR point cloud. The experimental results show that the alignment rate between the 2D micro-light polarization image and the 3D LiDAR point cloud reaches 74.82%, which can effectively detect the target hidden behind the glass under the low illumination condition and fill the blind area of the LiDAR point cloud data acquisition. This study verifies the feasibility and advantages of “polarization + LiDAR” fusion in low-light glass scene reconnaissance, and it provides a new technological means of covert target detection in complex environments. Full article