Geometric Shape Research Articles

ElementaryCQT: A New Dataset and its Deep Learning Analysis for 2D Geometric Shape Recognition

ElementaryCQT: A New Dataset and its Deep Learning Analysis for 2D Geometric Shape Recognition

THE STUDY OF ACCURACY OF AN OPERATOR’S PERCEPTION OF GEOMETRICAL OBJECT SIZES AND SHAPES IN THE VIRTUAL ENVIRONMENTS

This paper is devoted to the experimental comparison of accuracy of an operator’s perception of geometrical object sizes and shapes between the different conditions of information perception in the virtual environments and from the electronic displays. The experiments were conducted using a psychophysiological test for the accuracy of perceiving geometrical object sizes and shapes by an operator in the virtual environments and in the conditions of information perception from an electronic display. As a common metric of the accuracy of perceiving geometrical object sizes and shapes, an operator was offered to visually determine the object center of gravity. No significant differences in the measurement results of both the accuracy of perceiving geometrical object sizes and shapes and speed of this process were found based on the different methods of displaying the visual information to an operator.

Fractal Dimensional Analysis of Building Facades: The Case of Office Buildings in Erbil City

Fractal dimension is a characteristic parameter used to measure the complexity and irregularity of geometric shapes and patterns. It is applied in architecture to explore complexity and irregularity and to assess the aesthetic preferences in architectural design. Office building facade design pattern, as an observation unit, has a positive connection with the aesthetic value. This study aims to evaluate facade design styles in terms of two aesthetic qualities, visual complexity and visual diversity, via applying fractal dimension to three design styles of office building facades in Erbil City. The study uses a combination of qualitative and quantitative evaluations to achieve this goal. It employs box-counting analysis through the ImageJ plugin to FracLac and the mathematical perplexity equation to evaluate visual complexity and diversity. The results indicate that the neo-classical office facade style, with a visual complexity value of 1.7008 and visual diversity of 21.27, presents an elevated level of aesthetics similar to the saccadic pattern facade. This study concluded that a neo-classical architectural style for office building facades is the most aesthetically preferable. Modern facade design is considered a secondary architectural style aimed at achieving aesthetic value. Ultimately, the high-tech style is the least attractive facade style. This study contributes to avoiding designs of unattractive office building facades due to a lack of architectural design vocabulary while avoiding overly complex designs that prove visually upsetting for viewers.

Physics-based numerical evaluation of High-Temperature Aquifer Thermal Energy Storage (HT-ATES) in the Upper Jurassic reservoir of the German Molasse Basin

Abstract. Concepts of High-Temperature Aquifer Thermal Energy Storage (HT-ATES) (> 50 °C) are investigated in this study for system application in the Upper Jurassic reservoir (Malm aquifer) of the German Molasse Basin (North Alpine Foreland Basin). The karstified and fractured carbonate rocks exhibit favourable conditions for conventional geothermal exploitation of the hydrothermal resource. Here, we perform a physics-based numerical analysis to further assess the sustainability of HT-ATES development in the Upper Jurassic reservoir. With an estimated heating capacity of approx. 19.5 MW over half a year, our approach aims at determining numerically the efficiency of heat storage under the in situ Upper Jurassic reservoir conditions and projected operation parameters. In addition, the hydraulic performance of the HT-ATES system is further evaluated in terms of productivity and injectivity index. The numerical models build upon datasets from three operating geothermal sites at depths of approx. 2000–3000 m TVD, located in a subset of the reservoir dominated by karst-controlled fluid fluxes. Commonly considered as a single homogeneous unit, the 500 m thick reservoir is subdivided into three discrete layers based on field tests and borehole logs from the three considered sites. The introduced vertical heterogeneity with associated layer-specific enhanced permeabilities allows to examine potentially arising favourable heat transfer, and in combination with the facilitated high operation flow rates (100 kg s−1) to evaluate thermal recoveries in the multilayered reservoir. All simulations account for fluid density and viscosity variation based on thermodynamically consistent equations of state (EOS). Computation results reveal that the reservoir layering induces preferential fluid and heat migration primarily into the high-permeability zone, while thermal front propagation into the lower permeable rock matrix is inhibited. The simulations further display a gradual temperature increase in the warm wellbore and its surrounding host rock, and a consequent progressive improvement in the heat recovery efficiency. Despite the elevated permeability that may trigger advective heat losses, heat recovery factor values range from approx. 0.7 over the first year of operation to over 0.85 after 10 years of operation. An additional scenario is examined with fluid injection solely in the high permeable zone, in order to quantify potential enhancement in the recovery efficiency by omitting fluid injection in the lower-permeability layers where heat propagation is diminished. This is due to the geometrical shape of the thermally perturbed rock volume as heat losses occur during thermal equilibration between injected fluid and reservoir rock, as well as at the contact-surface area between propagating thermal front and adjacent rock matrix. Results suggest that under the stratified reservoir configuration, additionally constrained by the selected spatial distribution of rock properties, heat storage performed only into the upper high-permeability zone corresponds to an improved thermal performance. Simulation results further indicate that density-induced buoyant fluxes, which would considerably decrease thermal efficiencies are inhibited in the system, and the prevailing heat transport mechanism is forced convection.

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This piece discusses about the societal expectations and how they “shaped” individuals in an abstract but figurative way, using just simple geometrical shape and color to create expressive visual effect. The blue mimicks the influences by internet. The background is based on street views in Shibuya, Tokyo. The concept is an abstract expression of society expectations and the conflict between individuals and how the human bodies are politicized.

VARIATIONAL-DIFFERENCE SOLUTION OF DEFORMATION AND BUCKLING PROBLEMS OF ELASTOPLASTIC SHELLS OF REVOLUTION WITH ELASTIC FILLER UNDER COMBINED QUASI-STATIC AND DYNAMIC AXISYMMETRIC LOADINGS

The paper suggests a formulation and method for a numerical solution of deformation and buckling of elastoplastic shells of revolution with elastic filler under quasi-static and dynamic loadings. The problem is solved in a two-dimensional plane or generalized axisymmetric formulation with torsion. The governing system of equations is written in a Cartesian or cylindrical coordinate system. Modeling of deformation of an elastic-plastic shell is carried out based on the hypotheses of the theory of shells of the Timoshenko type, taking into account geometric nonlinearities. Kinematic relations are written in velocities and formulated in the metric of the current state. The elastoplastic properties of the shell are described by the flow theory with nonlinear isotropic hardening. Filler modeling is based on continuum mechanics hypotheses. The filler material is assumed to be linearly elastic. The variational equations of motion of structural elements (both shells and filler) are reduced from the three-dimensional equation of the balance of virtual powers of the work of continuum mechanics taking into account the accepted hypotheses of the theory of shells or a flat deformed state or generalized axisymmetric deformation with torsion. The modeling of the contact interaction between the shell and the filler is based on the condition of nonpenetration along the normal and slippage along the tangential. The finite-difference method and an explicit time integration scheme of the cross type are used to solve the defining system of equations. Approbation of the technique was carried out on the problem of buckling of a steel cylindrical shell with an elastic filler under quasi-static and dynamic compression by an external pressure that linearly increases with time. The results of the numerical study are compared with calculations performed using two other approaches developed earlier by the authors. The first approach is based on full-scale modeling of the process of deformation of the shell and filler within the framework of continuum mechanics. In the second approach, a simplified formulation is used, in which the deformation of the shell is modeled according to the hypotheses of the theory of non-sloping shells of the Timoshenko type taking into account geometric nonlinearities, and the filler is modeled according to the Winkler foundation hypothesis. The developed approaches make it possible to model the nonlinear subcritical deformation of shells of revolution with an elastic filler, to determine the ultimate (critical) loads in a wide range of loading rates taking into account geometric shape imperfections, to study buckling in axisymmetric and non-axisymmetric shapes under dynamic and quasi-static combined loadings in plane and axisymmetric deformations.

Tetrascatt Model: Born Approximation for the Estimation of Acoustic Dispersion of Fluid-Like Objects of Arbitrary Geometries

Abstract Modeling the acoustic scattering response due to penetrable objects of arbitrary shapes, such as those of many marine organisms, can be computationally intensive, often requiring high-performance computing equipment when considering a completely general situation. However, when the physical properties (sound speed and density) of the scatterer are similar to those of the surrounding medium, the Born approximation provides a computationally efficient way to calculate it. For simple geometrical shapes like spheres and spheroids, the acoustic scattering in the far field evaluated through the Born approximation recipe results in a formula that has been historically employed to predict the response of weakly scattering organisms, such as zooplankton. Further, the Born approximation has been extended to bodies whose geometry can be described as a collection of noncircular rings centered on a smooth curve. In this work, we have developed a numerical approach to calculate the far-field backscattering by arbitrary 3D objects under the Born approximation. The object's geometry is represented by a volumetric mesh composed of tetrahedrons, and the computation is efficiently performed through analytical 3D integration, yielding a solution expressed in terms of elementary functions. On a current desktop PC the model can compute the scattering from meshes with millions of elements in a matter of minutes. This model is able to compute the scattering from a complex shape 200× faster than other methods like the Boundary Element Method, without compromising the numeric quality of the solution. The method's correctness has been successfully validated against benchmark solutions. Additionally, we present acoustic scattering results for species with complex geometries. To enable other researchers to use and validate the method, a computational package named tetrascatt was developed in the R programming language and published in the CRAN (Comprehensive R Archive Network).

Non-invasive in-situ monitoring of deep etching processes using terahertz metasurfaces

This study presents an in-situ and non-invasive process control and monitoring (PCM) method for deep silicon etching, leveraging terahertz metasurfaces. The technique addresses the challenges for monitoring deep and high aspect ratio etching processes, which are prevalent in semiconductor microfabrication. By incorporating metasurfaces with identical geometric shapes and sizes as crucial components of targeted devices, the method enables accurate monitoring of the etching depth in the process. Continuous shifts of terahertz reflection spectra provide information on etching depth, while abrupt change in the curves highlights the etching endpoint, preventing over-etching. For the commonly used comb-finger structure, numerical simulations demonstrate a strong linear relationship between etching depth and terahertz resonant wavelengths (nonlinearity < 1%) and an abrupt resonant frequency change (> 0.6 THz) at the endpoint. Experimental validations confirm the accuracy of the PCM method, with an etching depth estimation error below 2 µm. This approach enhances the precision of PCM in microfabrication, offering the potential for widespread applications in the production of micromechanical sensors, actuators, and other microelectronic devices.

Spatial Sequential Matching Enhanced Underwater Single-Photon Lidar Imaging Algorithm

Traditional LiDAR and air-medium-based single-photon LiDAR struggle to perform effectively in high-scattering environments. The laser beams are subject to severe medium absorption and multiple scattering phenomena in such conditions, greatly limiting the maximum operational range and imaging quality of the system. The high sensitivity and high temporal resolution of single-photon LiDAR enable high-resolution depth information acquisition under limited illumination power, making it highly suitable for operation in environments with extremely poor visibility. In this study, we focus on the data distribution characteristics of active single-photon LiDAR operating underwater, without relying on time-consuming deep learning frameworks. By leveraging the differences in time-domain distribution between noise and echo signals, as well as the hidden spatial information among echo signals from different pixels, we rapidly obtain imaging results across various distances and attenuation coefficients. We have experimentally verified that the proposed spatial sequential matching enhanced (SSME) algorithm can effectively enhance the reconstruction quality of reflection intensity maps and depth maps in strong scattering underwater environments. Through additional experiments, we demonstrated the algorithm’s reconstruction effect on different geometric shapes and the system’s resolution at different distances. This rapidly implementable reconstruction algorithm provides a convenient way for researchers to preview data during underwater single-photon LiDAR studies.

Three-Dimensional Printing of Bioinspired Hierarchical Structures for Enhanced Fog Collection Efficiency in 3D Space via Vat Photopolymerization

Collecting fog water is crucial for dry areas since natural moisture and fog are significant sources of freshwater. Sustainable and energy-efficient water collection systems can take a page out of the cactus’s playbook by mimicking its native fog gathering process. Inspired by the unique geometric structure of the cactus spine, we fabricated a bioinspired artificial fog collector consisting of cactus spines featuring barbs of different sizes and angles on the surfaces for water collection and a series of microcavities within microchannels inspired by Nepenthes Alata on the bottom to facilitate water flowing to the reservoir. However, replicating the actual shape of the cactus spine using conventional manufacturing techniques is challenging, and research in this area has faced a limitation in enhancing water-collecting efficiency. Here, we turned to 3D printing technology (vat photopolymerization) to create bio-mimetic fog collectors with a variety of geometric shapes that would allow for the most effective conveyance and gathering of water. Various barb sizes, angles between each barb in a single array, spine and barb arrangements, and quantity of barbs were tested experimentally and numeric analysis was carried out to measure the volume of water collected and optimize the mass rate. The result shows that optimal fog collection is with a mass flow rate of 0.7433 g/min, with Li = 900 μm, θ = 45°, ϕ = 90°, Nb = 2, and Ns = 5. This study presents a sustainable and ecologically sound method for efficiently collecting humid air, which is expected to be advantageous for the advancement of future-oriented fog-collection, water-transportation, and separation technologies.

Digital light processing printed resin based conductive composites with CF@Ag and HGMs@Ag as fillers for metamaterial absorbers

AbstractAn integrated fabrication process based on multi‐material Digital Light Processing (DLP) technique for metamaterial absorbers (MMAs) is proposed. Considering the requirements of the DLP process for the rheological properties and photocuring properties of the slurry, a photopolymer resin slurry was developed using silver‐plated carbon fiber (CF@Ag) and silver‐plated hollow glass microspheres (HGMs@Ag) as conductive fillers. To meet the conductivity requirements of resistive films in MMAs, the study systematically investigated the influence of material parameters and DLP process parameters on the conductivity and precision of the fabricated patterns. Optimized DLP parameters for isotropic photocurable conductive composites with high conductivity and low percolation thresholds were obtained. Additionally, a MMAs was designed to achieve effective absorption in the wavelength range of 7 to 28 GHz. The absorber utilized a resistive film with a thickness of 30 μm and an average surface resistivity of 23 ohm/sq., prepared using a photocurable resin filled with a total mass fraction of 35 wt% CF@Ag and HGMs@Ag in a 2:1 mass ratio. This work shows that it is feasible to fabricate resistive film patterns with prescribed surface resistivity and geometric shapes through DLP printing directly without sintering, providing a promising process for the integrated manufacturing of high‐performance MMAs.Highlights Developed a conductive slurry using the synergy between CF@Ag and HGMs@Ag. Proposed the preparation process of resistance controllable resistive film. The first demonstration of manufacturing resistive film‐type MMAs via DLP.

Stomatocyte-discocyte-echinocyte transformations of erythrocyte modulated by membrane-cytoskeleton mechanical properties.

Stomatocyte-discocyte-echinocyte (SDE) transformations in human red blood cells (RBCs) have significant influences on blood dynamics and related disorders. The mechanical properties of the RBC membrane, such as shear modulus and bending elasticity, play crucial roles in determining RBC shapes. Recent biophysical findings reveal that building a comprehensive model capable of describing SDE shape transformations is a challenging problem. Based on dissipative particle dynamics, this study develops a two-component RBC model considering the detachment between the lipid bilayer and cytoskeleton, as well as the cytoskeletal reorganization during echinocyte formation. This model is validated by comparing RBCs' geometric shape and the apparent membrane tension with previous experimental measurements. Results indicate that a complete SDE sequence represented by six typical shapes can be obtained by modulating the model's mechanical and geometric parameters. Furthermore, a phase diagram based on reduced variables is obtained using principal-component analysis, demonstrating the phase transformations among SDE shapes. Our result suggests that the transformation from discocyte to stomatocyte is primarily influenced by dimensionless bending rigidity, whereas, during echinocyte formation, three key variables, i.e., dimensionless bending rigidity, targeting cytoskeleton shrinkage ratio, and connecting pattern, have joint impacts on the formation of spicules or bumps and the development of the cytoskeletal framework. The present two-component RBC model and the associated findings provide a perspective for a deeper understanding of the SDE transformation mechanism. This framework offers new insights into biological science and potential applications in the field of biomedical engineering.

A general analogy mass-spring system analytical model for sound reduction of side-branch resonators.

This study introduces a general analytical model designed to predict the sound reduction frequency of side-branch resonators, regardless of their geometric shapes. Grounded in a continuous analogy mass-spring system, we conceptualize the air within the branch cavity of the resonator as a series of infinitesimal continuous air layers. Each air layer is represented as a mass-spring unit, influenced by the pressure distribution inside the branch cavity. These mass-spring units, following mass conservation principles, are systematically transformed into planar configurations and stacked in a singular direction. These stacked mass-spring units are then converted into an effective one-dimensional mass-spring. Standing wave and the conservation of energy are employed to determine the natural frequency of the effective one-dimensional mass-spring, which corresponds to the sound reduction frequency of the side-branch resonator. This analytical model offers precise predictions for the sound reduction frequencies of side-branch resonators, regardless of their geometric variations. The analytical model can help designers design side-branch resonators in various shapes that accurately target specific sound reduction frequencies in real-world applications. Our analytical model's predictions for sound reduction frequencies were benchmarked against simulations from COMSOL 5.4. The comparative analysis demonstrates the adaptability of the proposed analogy mass-spring system model to side-branch resonators of varied geometric designs. Furthermore, the model exhibits both high predictive accuracy and robustness. Further details and examples will be elaborated upon subsequently.

Digital learning using ChatGPT in elementary school mathematics learning: a systematic literature review

Digital learning with ChatGPT in elementary school mathematics learning is urgently implemented. Several studies have explored digital learning with ChatGPT in elementary school mathematics learning, but studies using SLR are minimal. This article presents the 2022-2024 SLR study on digital learning with ChatGPT in elementary school mathematics learning. This SLR and PRISMA method is supported by Publish or Perish 8, VOSviewer version 1.6.20, Mendeley version 1.19.8, and ATLAS.ti version 7.5.16. The search results obtained 1,259 Scopus articles, which were filtered to 40 and analyzed using ATLAS.ti, then the results were described according to the research question. Digital learning with ChatGPT is a learning approach using the synchronous-asynchronous mode, virtual classrooms, distance, use of interactive digital tools, digital methods and media, innovation, digital modeling, use of robotics and AI ChatGPT for children with the principle of collaboration digital, and problem-solving with the support of digital resources. ChatGPT features multilingual, natural language, advanced AI, 24/7 availability, answering math questions, recurring training, and helping students with various math tasks. Implementation of digital learning with ChatGPT in elementary school mathematics learning for problem-solving, geometry, function limits in algebra, the material on flat shapes, geometric shapes, integrated PjBL, online, mixed and flipped classes.

3D point cloud regularization method for uniform mesh generation of mining excavations

Mine excavation systems are usually dozens of kilometers long with varying geometry on a small scale (roughness and shape of the walls) and on a large scale (varying widths of the tunnels, turns, and crossings). In this article, the authors address the problem of analyzing laser scanning data from large mining structures that can be used for various purposes, with focus on ventilation simulations. Together with the quality of the measurement data (diverse point-cloud density, missing samples, holes induced by obstructions in the field of view, measurement noise), this creates problems that require multi-stage processing of the obtained data. The authors propose a robust methodology to process a single segmented section of the mining system. The presented approach focuses on obtaining a point cloud ready for application in the computational fluid dynamics (CFD) analysis of airflow with minimal need for additional manual corrections on the generated mesh model. This requires the point cloud to have evenly distributed points and reduced noise (together with removal of objects inside) while keeping the unique geometrical properties and shape of the scanned tunnels. Proposed methodology uses trajectory of the excavation either obtained during the measurements or by skeletonization process explained in the article. Cross-sections obtained on planes perpendicular to the trajectory are processed towards the equalization of point distribution, removing measurement noise, holes in the point cloud and objects inside the excavation. The effects of the proposed algorithm are validated by comparing the processed cloud with the original cloud and testing within the CFD environment. The algorithm proved high effectiveness in improving skewness rate of the obtained mesh and geometry mapping accuracy (standard deviation below 5 centimeters in cloud-to-mesh comparison).

Oblique Rifting in the Southern Tibetan Plateau Revealed From 3‐D High‐Resolution Seismic Travel‐Time Tomography Around the India–Eurasia Continental Collision Zone

AbstractThe India–Eurasia continental collision zone (IECCZ) is an ideal setting for studying plate collision processes, plateau uplift mechanisms, and orogenic activities. Several models have attempted to explain the north–south (N‒S) collision and east–west (E‒W) extension based on geological and geophysical observations. Among them, the subduction of the subducted Indian lower crust (SILC) and the deep geometric shape of the southern Tibetan rifts are still controversial. To address these issues, we selected P‐ and S‐wave arrival times from 35,193 earthquakes recorded by 575 permanent and temporary stations and applied an improved double‐difference tomography method to obtain high‐resolution 3‐D P‐ and S‐wave velocity structures of the crust and uppermost mantle and relocated earthquake event locations around the IECCZ. The N‒S velocity profiles display a northward‐subducting high‐velocity layer stretching from the 20–40 km depth range in the Himalayan belt to the 50–60 km depth range beneath the northern Lhasa terrane (NL), suggesting that the eclogitized SILC extended beyond the Indus–Yarlung suture, reaching the NL. Additionally, the E‒W velocity profile at the SILC front reveals a discrete high‐velocity layer at depths of 40–60 km beneath the Longgar, Tingri–Nyima, Xianza‐Dinggye, and Yadong–Gulu rifts, implying that the SILC experienced tearing. Based on a comprehensive analysis of the seismicity, large‐earthquake source mechanisms near the Moho, and tomographic images of the study area, we proposed a new dynamic model of the India–Eurasia collision and N–S‐trending rifts. The significant characteristic of this model is that the rifts cut through the crust obliquely, not vertically.

A Bayesian Approach Toward Robust Multidimensional Ellipsoid-Specific Fitting.

This work presents a novel and effective method for fitting multidimensional ellipsoids (i.e., ellipsoids embedded in [Formula: see text]) to scattered data in the contamination of noise and outliers. Unlike conventional algebraic or geometric fitting paradigms that assume each measurement point is a noisy version of its nearest point on the ellipsoid, we approach the problem as a Bayesian parameter estimate process and maximize the posterior probability of a certain ellipsoidal solution given the data. We establish a more robust correlation between these points based on the predictive distribution within the Bayesian framework, i.e., considering each model point as a potential source for generating each measurement. Concretely, we incorporate a uniform prior distribution to constrain the search for primitive parameters within an ellipsoidal domain, ensuring ellipsoid-specific results regardless of inputs. We then establish the connection between measurement point and model data via Bayes' rule to enhance the method's robustness against noise. Due to independent of spatial dimensions, the proposed method not only delivers high-quality fittings to challenging elongated ellipsoids but also generalizes well to multidimensional spaces. To address outlier disturbances, often overlooked by previous approaches, we further introduce a uniform distribution on top of the predictive distribution to significantly enhance the algorithm's robustness against outliers. Thanks to the uniform prior, our maximum a posterior probability coincides with a more tractable maximum likelihood estimation problem, which is subsequently solved by a numerically stable Expectation Maximization (EM) framework. Moreover, we introduce an ε-accelerated technique to expedite the convergence of EM considerably. We also investigate the relationship between our algorithm and conventional least-squares-based ones, during which we theoretically prove our method's superior robustness. To the best of our knowledge, this is the first comprehensive method capable of performing multidimensional ellipsoid-specific fitting within the Bayesian optimization paradigm under diverse disturbances. We evaluate it across lower and higher dimensional spaces in the presence of heavy noise, outliers, and substantial variations in axis ratios. Also, we apply it to a wide range of practical applications such as microscopy cell counting, 3D reconstruction, geometric shape approximation, and magnetometer calibration tasks. In all these test contexts, our method consistently delivers flexible, robust, ellipsoid-specific performance, and achieves the state-of-the-art results.

3D-PSSIM: Projective Structural Similarity for 3D Mesh Quality Assessment Robust to Topological Irregularities.

Despite acceleration in the use of 3D meshes, it is difficult to find effective mesh quality assessment algorithms that can produce predictions highly correlated with human subjective opinions. Defining mesh quality features is challenging due to the irregular topology of meshes, which are defined on vertices and triangles. To address this, we propose a novel 3D projective structural similarity index ( 3D- PSSIM) for meshes that is robust to differences in mesh topology. We address topological differences between meshes by introducing multi-view and multi-layer projections that can densely represent the mesh textures and geometrical shapes irrespective of mesh topology. It also addresses occlusion problems that occur during projection. We propose visual sensitivity weights that capture the perceptual sensitivity to the degree of mesh surface curvature. 3D- PSSIM computes perceptual quality predictions by aggregating quality-aware features that are computed in multiple projective spaces onto the mesh domain, rather than on 2D spaces. This allows 3D- PSSIM to determine which parts of a mesh surface are distorted by geometric or color impairments. Experimental results show that 3D- PSSIM can predict mesh quality with high correlation against human subjective judgments, across the presence of noise, even when there are large topological differences, outperforming existing mesh quality assessment models.

Pengaruh Setting kawasan Tamansari terhadap Temperature Humidity Index

Title: The Effect of Environmental Setting of Tamansari on Temperature Humidity Index Geometric shape, land cover type, and density impact increasing global temperature and microclimate comfort. Tamansari was originally a park, garden, and bathhouse for the King and his family. Today, Tamansari is a heritage tourism area surrounded by dense residential areas in the middle of Yogyakarta City. This study aims to determine how the setting of the Tamansari area affects changes in temperature and relative humidity, which impact changes in the Temperature Humidity Index (THI) on the micro-scale of the Tamansari area. The method used is descriptive quantitative analysis, and calculation analysis was performed using the (THI) equation. The study results showed that the THI value in the Tamansari area was uncomfortable, with a THI value of 27.77-32.52°C. Building coverage ratio, population, and human activities affect the THI value. The THI value can also change due to weather factors that affect the physical condition of the area, especially in open areas.

Dynamic 3D Point Cloud Sequences as 2D Videos.

Dynamic 3D point cloud sequences serve as one of the most common and practical representation modalities of dynamic real-world environments. However, their unstructured nature in both spatial and temporal domains poses significant challenges to effective and efficient processing. Existing deep point cloud sequence modeling approaches imitate the mature 2D video learning mechanisms by developing complex spatio-temporal point neighbor grouping and feature aggregation schemes, often resulting in methods lacking effectiveness, efficiency, and expressive power. In this paper, we propose a novel generic representation called Structured Point Cloud Videos (SPCVs). Intuitively, by leveraging the fact that 3D geometric shapes are essentially 2D manifolds, SPCV re-organizes a point cloud sequence as a 2D video with spatial smoothness and temporal consistency, where the pixel values correspond to the 3D coordinates of points. The structured nature of our SPCV representation allows for the seamless adaptation of well-established 2D image/video techniques, enabling efficient and effective processing and analysis of 3D point cloud sequences. To achieve such re-organization, we design a self-supervised learning pipeline that is geometrically regularized and driven by self-reconstructive and deformation field learning objectives. Additionally, we construct SPCV-based frameworks for both low-level and high-level 3D point cloud sequence processing and analysis tasks, including action recognition, temporal interpolation, and compression. Extensive experiments demonstrate the versatility and superiority of the proposed SPCV, which has the potential to offer new possibilities for deep learning on unstructured 3D point cloud sequences.