Search Results (6,866)

This paper presents an analytical method for the dimensioning and selection of the four glass tube collector types: single-glazed with an air layer, single-glazed with a vacuum layer, double-glazed with an air layer, and double-glazed with a vacuum layer. In the first part of the paper (dimensioning phase), the iterative thermal resistance calculation algorithms were developed for all glass tube collector types, whereby the iterative thermal resistance calculation algorithm of the single-glazed tube collector with an air layer was experimentally tested and validated. The second part of the paper (selection phase) uses a multi-criteria decision-making method to determine the optimal glass tube collector design. Unlike other papers, three indicator groups are taken into account in this case: geometric (mass, surface occupation, total surface occupation, volume occupation), economic (manufacturing and exploitation costs), and ecological (embodied energy and greenhouse gas emission). The proposed method is characterized by simple and fast calculations with satisfactory accuracy, which avoids high investment costs (experimental research), approximation and discretization of physical models (numerical research), and a large number of input parameters with boundary conditions (theoretical research). It should be noted that, with certain additions and changes, it can also be applied to other solar thermal collectors, so the authors believe such tools are handy for the global scientific public. Full article

A simplified column-buckling model is developed to understand the buckling mechanism of thin-walled strips restrained by uniform lateral pressure in the milling process. The strip is simplified as two rigid columns connected by a rotation spring, resting on a smooth surface, restrained by a uniform pressure and loaded by an axial force. Two loading cases are considered, i.e., the dead load and the follower load. Analytical solutions for the post-buckling responses of the two cases are derived based on the energy method. The minimum buckling force, Maxwell force and stability conditions for the two cases are established. It is demonstrated that the application of higher uniform pressure increases the minimum buckling force for the column and thus makes the column less likely to buckle. For the same pressure level, the dead load is found to be more effective than the follower load in suppressing the buckling of the system. The effect of initial geometric imperfection is also investigated, and the imperfection amplitude and critical restraining pressure that prevent buckling are found to be linearly related. The analytical results are validated by finite element simulations. This analytical model reveals the buckling mechanism of strips under lateral pressure restraint, which cannot be explained by the conventional bifurcation buckling theory, and provides a theoretical foundation for buckling-prevention strategies during the milling process of thin-walled strips, plates and shells commonly encountered in aerospace or automotive industries. Full article

With the rapid advancement in Building Information Modelling (BIM) technology to strengthen the Building and Construction (B&C) industry, effective methods are required for the analysis and improvement of as-built BIM, which reflects the completed building project and captures all deviations and updates from the initial design. However, most existing studies are focused on as-designed BIM, while the analysis and inspection of as-built BIM rely on labour-intensive visual and manual approaches that overlook interdependent relationships among components. To address these issues, we propose a network analysis-based approach for managing and improving as-built BIM. Networks are generated from geometric attributes extracted from Industry Foundation Classes (IFC) documents, and network analytical techniques are applied to facilitate BIM analysis. In addition, a practical dataset is utilised to verify the feasibility of the proposed approach. The results demonstrate that our method significantly enhances the analysis and comparison of as-built BIM from model analysis and matching. Specifically, the innovative contribution leverages global information and interdependent relations, providing a more comprehensive understanding of the as-built BIM for effective management and optimisation. Our findings suggest that network analysis can serve as a powerful tool for structure and asset management in the B&C industry, offering new perspectives and methodologies for as-built BIM analysis and comparison. Finally, detailed discussion and future suggestions are presented. Full article

Robust geometric fitting is one of the crucial and fundamental problems in computer vision and pattern recognition. While random sampling and consensus maximization have been popular strategies for robust fitting, finding a balance between optimization quality and computational efficiency remains a persistent obstacle. In this paper, we adopt an optimization perspective and introduce a novel maximum consensus robust fitting algorithm that incorporates the maximum entropy framework into the consensus maximization problem. Specifically, we incorporate the probability distribution of inliers calculated using maximum entropy with consensus constraints. Furthermore, we introduce an improved relaxed and accelerated alternating direction method of multipliers (R-A-ADMMs) strategy tailored to our framework, facilitating an efficient solution to the optimization problem. Our proposed algorithm demonstrates superior performance compared to state-of-the-art methods on both synthetic and contaminated real datasets, particularly when dealing with contaminated datasets containing a high proportion of outliers. Full article

Manufacturers of adhesives for industrial use determine the strength of adhesive joints during shear tests. Most often, components made of the same material are joined. In contrast, the roughness of the surfaces to be joined results from the use of a specific surface treatment technology. In adhesive manufacturers’ recommendations for metal-to-metal joints, surface technologies can be found without specifying numerical requirements for roughness. Modern techniques for shaping the geometric accuracy of components allow the formation of determined irregularities on the surface, which are characterised by their height and mutual distribution. Furthermore, regular irregularities can be obtained by using the appropriate tool and technological machining parameters. In this way, surfaces with similar load-bearing capacity, core volume, texture or expected hydrophobic properties can be produced by various methods. However, a basic prerequisite is the careful definition of the numerical requirements, both for the basic roughness indices and those of a complementary nature. As a rule, the strength of the adhesive joint is also lower than the strength of the adhesive itself. The strength of an adhesive joint depends on the ‘mechanical anchorage’ of the adhesive and the adhesion phenomenon on the surface. The research assumes that it is possible to induce an interaction between the geometric state of the surface and the properties of the adhesive, so as to guarantee the maximum strength of the adhesive joint. To verify this, a series of experimental tests were developed and carried out for two different adhesives characterised by different viscosities and offered bond strength. Based on the tests carried out, recommendations were made to the designers of adhesive joints, where, in addition to the height of the surface irregularities, the properties related to fluid retention and the shape of the irregularities in the valleys should be determined. Full article

Methods used in topological data analysis naturally capture higher-order interactions in point cloud data embedded in a metric space. This methodology was recently extended to data living in an information space, by which we mean a space measured with an information theoretical distance. One such setting is a finite collection of discrete probability distributions embedded in the probability simplex measured with the relative entropy (Kullback–Leibler divergence). More generally, one can work with a Bregman divergence parameterized by a different notion of entropy. While theoretical algorithms exist for this setup, there is a paucity of implementations for exploring and comparing geometric-topological properties of various information spaces. The interest of this work is therefore twofold. First, we propose the first robust algorithms and software for geometric and topological data analysis in information space. Perhaps surprisingly, despite working with Bregman divergences, our design reuses robust libraries for the Euclidean case. Second, using the new software, we take the first steps towards understanding the geometric-topological structure of these spaces. In particular, we compare them with the more familiar spaces equipped with the Euclidean and Fisher metrics. Full article

When an ultrasonic pulse propagates in a thin plate, nonlinear Lamb waves with higher harmonics and a zero-frequency component (ZFC) will be generated because of the nonlinearity of materials. The ZFC, also known as the static displacement or static component, has its unique application on the evaluation of early-stage damages in the elastic symmetrical undulated plate. In this study, analysis of the excitation mechanism of the ZFC and the second harmonic component (SHC) was theoretically and numerically investigated, and the material early-stage damage of a symmetrical undulated was characterized by studying the propagation of nonlinear Lamb waves. Both the ZFC and SHC can be effectively employed in monitoring the material damages of the undulated plate in its early stage. However, several factors must be considered for the propagation of the SHC in an undulated plate because of the geometric curvature and interference between the second harmonics during propagation, preventing efficient application of this technique. If the fundamental wave can propagate in the plate regardless of the plate boundary conditions, an accumulative effect always exists for the ZFC in a thin plate, indicating that the ZFC is independent of the structural geometry. This study reveals that the ZFC-based inspection technique is more efficient and powerful in characterizing the damages of a symmetrical undulated plate in the early stage of service compared to the second harmonic method. Full article

Overturning and death are common abnormalities in cage-reared ducks. To achieve timely and accurate detection, this study focused on 10-day-old cage-reared ducks, which are prone to these conditions, and established prior data on such situations. Using the original YOLOv8 as the base network, multiple GAM attention mechanisms were embedded into the feature fusion part (neck) to enhance the network’s focus on the abnormal regions in images of cage-reared ducks. Additionally, the Wise-IoU loss function replaced the CIoU loss function by employing a dynamic non-monotonic focusing mechanism to balance the data samples and mitigate excessive penalties from geometric parameters in the model. The image brightness was adjusted by factors of 0.85 and 1.25, and mainstream object-detection algorithms were adopted to test and compare the generalization and performance of the proposed method. Based on six key points around the head, beak, chest, tail, left foot, and right foot of cage-reared ducks, the body structure of the abnormal ducks was refined. Accurate estimation of the overturning and dead postures was achieved using the HRNet-48. The results demonstrated that the proposed method accurately recognized these states, achieving a mean Average Precision (mAP) value of 0.924, which was 1.65% higher than that of the original YOLOv8. The method effectively addressed the recognition interference caused by lighting differences, and exhibited an excellent generalization ability and comprehensive detection performance. Furthermore, the proposed abnormal cage-reared duck pose-estimation model achieved an Object Key point Similarity (OKS) value of 0.921, with a single-frame processing time of 0.528 s, accurately detecting multiple key points of the abnormal cage-reared duck bodies and generating correct posture expressions. Full article

For hydraulic engineering, accurate geometric modeling is crucial for design, analysis, and maintenance. In recent years, point cloud technology, with its high precision and detailed data capture capabilities, has gradually become the preferred method for geometric modeling in hydraulic engineering. However, traditional point cloud processing methods exhibit slow data processing speeds and poor mesh quality when reconstructing 3D models. Therefore, we have studied the meshing method for the 3D reconstruction of large-scale discrete point cloud data and proposed a meshing method based on the Delaunay criterion and the growth method to achieve rapid meshing of discrete point cloud data. To demonstrate the efficiency and convenience of the proposed method, point cloud reconstruction experiments are conducted on various buildings. We compare our approach with the Poisson, BPA, and ONet methods. Additionally, we apply it to the Jiangsu Gaogang Water Conservancy Hub in China. The results show that our proposed method is superior in both reconstruction quality and efficiency, providing a promising solution for the reconstruction of 3D building models from point clouds. Full article

To improve the accuracy of in situ measurement of the standard volumes of pipe provers and to shorten the traceability chain, a new method of in situ pipe prover volume measurement was developed alongside a supporting measurement device. This method is based on the geometric dimension approach, which measures the inner diameter and length of a pipe prover to calculate its volume. For inner diameter measurement, a three-probe inner-diameter algorithm model was established. This model was calibrated using a standard ring gauge of Φ313 mm, with the parameters calculated through fitting. Another standard ring gauge of Φ320 mm was used to verify the inner diameters determined by the algorithmic model. A laser interferometer was employed for the segmented measurement of the pipe prover length. The comprehensive measurement system was then used for in situ measurement of the standard pipe prover. The newly developed system achieved an expanded uncertainty of 0.012% (k = 2) in volume measurement, with the deviation between the measured and nominal pipe prover volumes being merely 0.007%. These results demonstrate that the proposed in situ measurement method offers ultra-high-precision measurement capabilities. Full article

Linear particle accelerators (Linacs) are primarily composed of radio frequency cavities (cavities). Compared to traditional manufacturing, Laser Powder Bed Fusion (L-PBF) holds the potential to fabricate cavities in a single piece, enhancing Linac performance and significantly reducing investment costs. However, the question of whether red or green laser PBF yields superior results for pure copper remains a subject of ongoing debate. Eight 4.2 GHz single-cell cavities (SCs) were manufactured from pure copper using both red and green PBF (SCs R and SCs G). Subsequently, the surface roughness of the SCs was reduced through a chemical post-processing method (Hirtisation) and annealed at 460 ${}^{\circ }$C to maximize their quality factor ($Q_0$). The geometric accuracy of the printed SCs was evaluated using optical methods and resonant frequency ($f_R$) measurements. Surface conductivity was determined by measuring the quality factor ($Q_0$) of the SCs. Laser scanning microscopy was utilized for surface roughness characterization. The impact of annealing was quantified using Energy-Dispersive X-ray Spectroscopy and Electron Backscatter Diffraction to evaluate chemical surface properties and grain size. Both the SCs R and SCs G achieved the necessary geometric accuracy and thus $f_R$ precision. The SCs R achieved a 95% $Q_0$ after a material removal of 40 µm. The SCs G achieved an approximately 80% $Q_0$ after maximum material removal of 160 µm. Annealing increased the $Q_0$ by an average of about 5%. The additive manufacturing process is at least equivalent to conventional manufacturing for producing cavities in the low-gradient range. The presented cavities justify the first high-gradient tests. Full article

Illegal coal mining is prevalent worldwide, leading to extensive ground subsidence and land collapse. It is crucial to define the location and spatial dimensions of these areas for the efficient prevention of the induced hazards. Conventional methods for goaf locating using the InSAR technique are mostly based on the probability integral model (PIM). However, The PIM requires detailed mining information to preset model parameters and does not account for the layered structure of the coal overburden, making it challenging to detect underground goaves in cases of illegal mining. In response, a novel method based on the InSAR technique and the Stratified Optimal Okada Dislocation Model, named S-ODM, is proposed for locating goaves with basic geological information. Firstly, the S-ODM employs a numerical model to establish a nonlinear function between the goaf parameters and InSAR-derived ground deformation. Then, in order to mitigate the influence of nearby mining activities, the goaf azimuth angle is estimated using the textures and trends of the InSAR-derived deformation time series. Finally, the goaf’s dimensions and location are estimated by the genetic algorithm–particle swarm optimization (GA-PSO). The effectiveness of the proposed method is validated using both simulation and real data, demonstrating average relative errors of 6.29% and 7.37%, respectively. Compared with the PIM and ODM, the proposed S-ODM shows improvements of 19.48% and 52.46% in geometric parameters. Additionally, the errors introduced by GA-PSO and the influence of ground deformation monitoring errors are discussed in this study. Full article

In the construction of super high-rise buildings, building machines (BMs) are increasingly replacing traditional climbing frames. Building machine jacking operation (BMJO) is a high-difficulty and high-risk stage in the construction of the top mold system. To guarantee the operational safety of the BMJO and to enhance its intelligent control level, a digital twin (DT)-based monitoring method for the operation status of the BMJO is proposed. Firstly, a DT framework for monitoring the operation status of the BMJO is presented, taking into account the operational characteristics of the BM and the requirements of real-time monitoring. The functions of each part are then elaborated in detail. Secondly, the virtual twin model is created using Blender’s geometric node group function; artificial neural network technology is used to enable online prediction of the structural performance of the BMJO and a motion model is established to realize a real-time state mapping of the BMJO. Finally, taking a BM project as an example, the DT system is established in conjunction with the project to verify the feasibility of the DT framework for monitoring the state of the BMJO. It is proved that the prediction results have high accuracy and fast analysis speed, thus providing a new way of thinking for monitoring and controlling the safe operation of the BMJO. Full article

Background: Measles and rubella are vaccine-preventable diseases targeted for elimination in most World Health Organization regions, and China is considered to have momentum towards measles elimination. Therefore, this study aimed to assess the population immunity levels against measles and rubella in Zhejiang Province in China in order to provide valuable insights for informing future public health measures and contributing to the ongoing global campaign against these diseases. Materials and methods: A cross-sectional serological survey was conducted in 2022. A total of 2740 blood samples were collected from healthy individuals spanning the age range of 0–59 years, representing diverse demographic strata across 11 prefectures in Zhejiang Province in China. The sera were tested for measles and rubella IgG antibodies to determine positivity rates and geometric mean concentrations (GMCs). Results: The overall positivity rate for the measles IgG antibody was 85.3%, with a GMC of 588.30 mIU/mL. The positivity rate for the rubella IgG antibody was 70.9%, and the GMC was 35.30 IU/mL. Measles IgG antibody positivity rates across the 0–11 months, 12–23 months, 24–35 months, 3–5 years, 6–9 years, 10–14 years, 15–19 years, 20–29 years, and 30–59 years age groups were 63.1%, 92.5%, 97.0%, 94.0%, 85.8%, 77.3%, 86.9%, 84.9%, and 88.7%, respectively (trend χ² = 118.34, p < 0.001). Correspondingly, rubella antibody positivity rates for these same age brackets were 55.9%, 87.9%, 94.7%, 88.2%, 69.9%, 54.2%, 72.6%, 67.5%, and 74.3% (trend χ² = 199.18, p < 0.001). Both univariate and multivariate analyses consistently demonstrated that age, immunization history, and differing economic levels were significant factors contributing to variations in antibody levels. Conclusions: The seroprevalence of measles and rubella was lower than that required for herd immunity. Periodic vaccination campaigns should be launched to increase immunity. Full article

The volume of fluid (VOF) method is a popular technique for the direct numerical simulations of flows involving immiscible fluids. A discrete volume fraction field evolving in time represents the interface, in particular, to compute its geometric properties. The height function method (HF) is based on the volume fraction field, and its estimate of the interface curvature converges with second-order accuracy with grid refinement. Data-driven methods have been recently proposed as an alternative to computing the curvature, with particular consideration for a well-balanced input data set generation and symmetry preservation. In the present work, a two-layer feed-forward neural network is trained on an input data set generated from the height function data instead of the volume fraction field. The symmetries for rotations and reflections and the anti-symmetry for phase swapping have been considered to reduce the number of input parameters. The neural network can efficiently predict the local interface curvature by establishing a correlation between curvature and height function values. We compare the trained neural network to the standard height function method to assess its performance and robustness. However, it is worth noting that while the height function method scales perfectly with a quadratic slope, the machine learning prediction does not. Full article