Geometric Structure Research Articles

PathNet: Path-Selective Point Cloud Denoising.

Current point cloud denoising (PCD) models optimize single networks, trying to make their parameters adaptive to each point in a large pool of point clouds. Such a denoising network paradigm neglects that different points are often corrupted by different levels of noise and they may convey different geometric structures. Thus, the intricacy of both noise and geometry poses side effects including remnant noise, wrongly-smoothed edges, and distorted shape after denoising. We propose PathNet, a path-selective PCD paradigm based on reinforcement learning (RL). Unlike existing efforts, PathNet enables dynamic selection of the most appropriate denoising path for each point, best moving it onto its underlying surface. We have two more contributions besides the proposed framework of path-selective PCD for the first time. First, to leverage geometry expertise and benefit from training data, we propose a noise- and geometry-aware reward function to train the routing agent in RL. Second, the routing agent and the denoising network are trained jointly to avoid under- and over-smoothing. Extensive experiments show promising improvements of PathNet over its competitors, in terms of the effectiveness for removing different levels of noise and preserving multi-scale surface geometries. Furthermore, PathNet generalizes itself more smoothly to real scans than cutting-edge models.

A review of 2.45 GHz microstrip patch antennas for wireless applications

Recently, microstrip patch antennas have become popular. Due to their ubiquity, these antennas have more uses every day. In this research paper, a 2.45 GHz microstrip patch antenna has been reviewed and analyzed. Different substrate materials have been used to make these antennas, and their thickness is different. Various antennas are designed based on the application, such as rectangular, square, triangle, ring, donut, and dipole. Other types of software were used to design the antenna, including CST, HFSS, MATLAB, ADS, and FEKO. Microstrip patch antenna design is a relatively new field of study for wireless applications. Several devices are linked to send or receive radio waves using a single antenna. Antennas designed for 2.45 GHz are used in various wireless communication systems, including television broadcasts, microwave ovens, mobile phones, wireless local area network (WLAN), Bluetooth, global positioning system (GPS), and two-way radios. This article looks at the geometric structures of antennas, including their many parameters and materials and the many different shapes they can take. In addition, the substrate materials, the loss tangent, the thickness, the return loss, the bandwidth, the voltage standing wave ratio (VSWR), the gain, and the directivity of previous articles will also be discussed.

Structure tensor-based Gaussian kernel edge-adaptive depth map refinement with triangular point view in images

<span lang="EN-US">Image reconstruction is the process of restoring the image resolution. In 3D image reconstruction, the objects in different viewpoints are processed with the triangular point view (TPV) method to estimate object geometry structure for 3D model. This work proposes a depth refinement methodology in preserving the geometric structure of objects using the structure tensor method with a Gaussian filter by transforming a series of 2D input images into a 3D model. The computation of depth map errors can be found by comparing the masked area/patch with the distribution of the original image's greyscale levels using the error pixel-based patch extraction algorithm. The presence of errors in the depth estimation could seriously deteriorate the quality of the 3D effect. The depth maps were iteratively refined based on histogram bins number to improve the accuracy of initial depth maps reconstructed from rigid objects. The existing datasets such as the dataset tanks and unit (DTU) and Middlebury datasets, were used to build the model out of the object scene structure. The results of this work have demonstrated that the proposed patch analysis outperformed the existing state of the art models depth refinement methods in terms of accuracy.</span>

Multi-granularity tooth analysis via YOLO-based object detection models for effective tooth detection and classification

Accurate detection and classification of teeth is the first step in dental disease diagnosis. However, the same class of tooth exhibits significant variations in surface appearance. Moreover, the complex geometrical structure poses challenges in learning discriminative features among the different tooth classes. Due to these complex features, tooth classification is one of the challenging research domains in deep learning. To address the aforementioned issues, the presented study proposes discriminative local feature extraction at different granular levels using YOLO models. However, this necessitates a granular intra-oral image dataset. To facilitate this requirement, a dataset at three granular levels (two, four, and seven teeth classes) is developed. YOLOv5, YOLOv6, and YOLOv7 models were trained using 2,790 images. The results indicate superior performance of YOLOv6 for two-class classification problems. The model generated a mean average precision (mAP) value of 94%. However, as the granularity level is increased, the performance of YOLO models decreases. For, four and seven-class classification problems, the highest mAP value of 87% and 79% was achieved by YOLOv5 respectively. The results indicate that different levels of granularity play an important role in tooth detection and classification. The YOLO’s performance gradually decreased as the granularity decreased especially at the finest granular level.

Microscopic pathways of transition from low-density to high-density amorphous phase of water.

In recent years, much attention has been devoted to understanding the pathways of phase transition between two equilibrium condensed phases (such as liquids and solids). However, the microscopic pathways of transition involving non-equilibrium, non-diffusive amorphous (glassy) phases still remain poorly understood. In this work, we have employed computer simulations, persistence homology (a tool rooted in topological data analysis), and machine learning to probe the microscopic pathway of pressure-induced non-equilibrium transition between the low- and high-density amorphous (LDA and HDA, respectively) ice phases of the TIP4P/2005 and ST2 water models. Using persistence homology and machine learning, we introduced a new order parameter that unambiguously identifies the LDA- and HDA-like local environments. The LDA phase transitions continuously and collectively into the corresponding HDA phase via a pre-ordered intermediate phase during the isothermal compression. The local order parameter susceptibilities show a maximum near the transition pressure (P*)-suggesting maximum structural heterogeneities near P*. The HDA-like clusters are structurally ramified and spatially delocalized inside the LDA phase near the transition pressure. We also found manifestations of the first-order low-density to high-density liquid transition in the sharpness of the order parameter change during the LDA to HDA transition. We further investigated the (geometrical) structures and topologies of the LDA and HDA ices formed via different protocols and also studied the dependence of the (microscopic) pathway of phase transition on the protocol followed to prepare the initial LDA phase. Finally, the method adopted here to study the phase transition pathways is not restricted to the system under consideration and provides a robust way of probing phase transition pathways involving any two condensed phases at both equilibrium and out-of-equilibrium conditions.

GraM: Geometric Structure Embedding into Attention Mechanisms for 3D Point Cloud Registration

3D point cloud registration is a crucial technology for 3D scene reconstruction and has been successfully applied in various domains, such as smart healthcare and intelligent transportation. With theoretical analysis, we find that geometric structural relationships are essential for 3D point cloud registration. The 3D point cloud registration method achieves excellent performance only when fusing local and global features with geometric structure information. Based on these discoveries, we propose a 3D point cloud registration method based on geometric structure embedding into the attention mechanism (GraM), which can extract the local features of the non-critical point and global features of the corresponding point containing geometric structure information. According to the local and global features, the simple regression operation can obtain the transformation matrix of point cloud pairs, thereby eliminating the semantics that ignores the geometric structure relationship. GraM surpasses the state-of-the-art results by 0.548° and 0.915° regarding the relative rotation error on ModelNet40 and LowModelNet40, respectively.

Development of a hybrid material with auxetic phase

AbstractHybrid multiphase materials exhibit a wide range of desirable properties, which may be tailored to the needs of their user or application. Modern solutions often use advanced smart materials with specific properties, which in some cases allow the development of devices previously impossible to manufacture due to restrictions of conventional materials. There is ongoing research on multiphase materials composed of phases with differing Poisson’s ratios, which have increased elastic modulus compared to their respective monophase components. Precise analysis of multiphase materials composed of periodic microstructures is possible with the use of multiscale modeling methods and numerical homogenization of individual phases’ geometric structures into homogenous materials retaining the properties of their representative volume elements. Auxetic materials behavior under loading differs from conventional materials. Their Poisson’s ratio value is negative, which means that when they are uniaxially stretched they both elongate and expand laterally, and while uniaxially compressed they both shorten and shrink laterally. While seemingly changing volume, their density remains constant in microscale. Deformation causes the gaps in auxetics patterned structure to change shape and size, but the actual material of the structure remains unchanged. This paper presents the results of development of a multiphase hybrid material with auxetic phase, in two variants. First, with the goal of maximization of the material’s elastic modulus. Second, to obtain a zero-value effective Poisson’s ratio. Different patterns of phases distribution in the material were analyzed. A few different auxetic structures were taken into consideration. Optimization utilized numerical simulation based on finite element method.

Optimal sand transport roadbed geometry structure of Taklamakan Desert highway

In order to obtain the optimal geometric structure for sand transport in desert roads, this study fully utilizes the natural sand transport capacity of the desert roads, aiming to reduce occurrences of road damage due to sand burial. The research integrates Computational Fluid Dynamics (CFD) with optimization analysis theory. Various typical geometric structures of desert highway roadbed were modeled using the Design Exploration module in CFD. Optimization analysis methods were employed to model and compute the sediment transport optimization design on both the roadbed and road surface. Then, the initial sample points are obtained by using the Design of Experiments (DOE), and the response surface is established by using the Kriging model to obtain the change trend of the input parameters and the objective function. Finally, the cross-section parameters of the sediment transport subgrade corresponding to different inflow conditions are obtained. The results show that the sediment transport performance of embankment, cutting and semi-filled uphill subgrade is negatively correlated with the height of subgrade. The relationship between slope gradient and sediment transport performance of subgrade depends on the height of subgrade and the type of subgrade section. For embankment and cutting, when the subgrade height is less than 0.5 m, the sediment transport performance of the subgrade is positively correlated with the subgrade slope; however, when the subgrade height exceeds 0.5 m, the sediment transport performance is negatively correlated with the subgrade slope. For the semi-filled uphill flow subgrade, the sediment transport performance of the subgrade is negatively correlated with the subgrade slope. Comprehensive analysis shows that semi-filled and excavated downhill subgrade has the best sand transport performance, embankment subgrade has the second highest sand transport capacity, then cutting subgrades, while semi-filled and excavated uphill subgrade has the worst sand transport performance. The research conclusions provide valuable scientific guidance for the design of sand control embankment structures tailored to local conditions for desert highways. This is of significant importance for enhancing the sediment transport capacity of desert highways and prolonging their service life.

Wasserstein Local Slow Feature Analysis and Its Application for Process Monitoring

Abstract Complex industrial processes are commonly characterized by dynamics, which results from the fact that there exists compensation of the closed-loop control and complex reflux during process operations. In this work, we propose a new method termed Wasserstein local slow feature analysis approach (WLSFA) used for monitoring the dynamic process, which can learn slowly varying information to capture the trend of process variations and characterize dynamics. Specifically, Wasserstein graph embedding based on optimal transport is proposed to preserve the local geometrical structure of raw data so that the reduced-dimensional representations can more faithfully reveal the underlying information of data, enhancing monitoring performance. Moreover, the l_2 -norm orthogonal constraint is incorporated into the objective function to improve generalization ability and alleviate the false alarm rate. On the basis of WLSFA model, a contribution plot in the sense of reconstruction is developed to locate the fault variables, which is beneficial to regulate abnormal conditions towards normal levels. Finally, the efficiency of the proposed approach is illustrated by a benchmark process and the application to a real-world fractionation process.

Adsorption of Multiple NO Molecules on Anionic Gold Clusters: Electron Transfer and Disproportionation Enhanced by Molecular Aggregation.

The reactions of Aun- clusters with multiple nitric oxide (NO) molecules are explored at 150 K by utilizing a mini-flow-tube reactor and a time-of-flight mass spectrometer. Adsorption of multiple NO molecules is observed on most Aun-, while disproportionation reactions only occur on even-sized Aun- with n = 4, 6, 8, 20 and odd-sized ones with n = 5 and 7. Theoretical calculations reveal the geometric structures and electronic states of the products containing bimolecular and trimolecular NO units, where two NO molecules typically form dimers. Different from NO monomers that weakly interact with odd-sized Aun- and form electron-sharing covalent bonds with Au10-(D3h) and Au16-, NO dimers can extract significant charge from parent Aun-. Regarding the three NO molecules, a predilection toward condensation into trimers on even-sized Aun- is observed, while the tendency is more toward an adsorption pattern of a dimer plus a monomer on odd-sized Aun-. The NO trimers register even higher charge gain from Aun- as compared with the NO dimers, which leads to an elevated degree of activation and induces the progression of disproportionation reactions. Therefore, when considering the reaction between NO and Aun-, it appears that NO has a propensity to form dimers or trimers on Aun-. This behavior of aggregate formation substantially enhances the ability of NO to absorb negative charges from Aun- although the occurrence of disproportionate dissociation reactions is initiated only for specific sizes.

Structural evolution and electronic properties of the La-doped germanium clusters

The geometric structure, relative stability, and electronic properties of LaGe n − (n = 3–14) are systematically studied by density functional theory, and the results are compared with the experimental literature data. It is found that the structures of 10A-I, 11A-I, and 14A-I are clearly two-parted and have the potential to design molecular chains. Combined with the average binding energy and second-order differential energy analysis, it is speculated that LaGe 9 − is a magic number cluster. The density of states and natural population analysis indicate that electrons consistently transfer from the La atom to Ge atoms. Doped La atom increases the interaction with Ge atoms and improve the stability of pure germanium clusters. Interestingly, LaGe 9 − has a high transfer charge number, and its high stability can be further explained by the spherical jellium model. The investigation into LaGe 9 − magic number clusters offer valuable insights for the exploration of novel materials with superior stability and performance.

Exploiting finite geometries for better quantum advantages in Mermin-like games

Abstract Quantum games embody non-intuitive consequences of quantum phenomena, such as entanglement and contextuality. The Mermin-Peres game is a simple example, demonstrating how two players can utilise shared quantum information to win a no - communication game with certainty, where classical players cannot. In this paper we look at the geometric structure behind such classical strategies, and borrow ideas from the geometry of symplectic polar spaces to maximise this quantum advantage. We introduce a new game called the Eloily game with a quantum-classical success gap of 0.267, larger than that of the Mermin-Peres and doily games. We simulate this game in the IBM Quantum Experience and obtain a success rate of 1, beating the classical bound of 0.733 demonstrating the efficiency of the quantum strategy.

Bridged Pt-OH-Mn Mediator in N-coordinated Mn Single Atoms and Pt Nanoparticles for Electrochemical Biomolecule Oxidation and Discrimination.

The rational design of efficient catalysts for uric acid (UA) electrooxidation, as well as the establishment of structure-activity relationships, remains a critical bottleneck in the field of electrochemical sensing. To address these challenges, herein, a hybrid catalyst that integrates carbon-supported Pt nanoparticles and nitrogen-coordinated Mn single atoms (PtNPs/MnNC) is developed. The metal-metal interaction during annealing affords the construction of metallic-bonded Pt-Mn pairs between PtNPs and Mn single atoms, facilitating the electron transfer from PtNPs to the support and thereby optimizing the electronic structure of catalysts. More importantly, experiments and theoretical calculations provide visual proof for the 'incipient hydrous oxide adatom mediator' mechanism for UA oxidation. The Pt-Mn pairs first adsorb OH* to construct the bridged Pt-OH-Mn mediators to serve as a highly active intermediate for N-H bond dissociation and proton transfer. Benefiting from the unique electronic and geometric structure of the catalytic center and reactive intermediates, PtNPs/MnNC exhibits superior electrooxidation performance. The electrochemical sensor based on PtNPs/MnNC enables sensitive detection and discrimination of UA and dopamine in serum samples. This work offers new insights into the construction of novel electrocatalysis for sensitive sensing platforms.

A search algorithm for the compact uncertainty region of system elements with interval and affine constraint properties

This study proposes a search algorithm for the compact uncertainty region of system elements with affine constraints and interval information. While existing studies have considered only the interval information of uncertainty, the proposed algorithm reflects both the interval and affine constraints of uncertain elements to reduce conservatism due to uncertainties. First, the affine constraints of uncertainty are plotted in an N-dimensional space, where N is the number of elements that should be considered. Due to the properties of the affine constraints, the affine constraints are expressed as an (N-1) dimensional structure. The interval information is then expressed as an N-dimensional structure. The algorithm iteratively checks the edges of the N-dimensional structure between two geometrical structures to determine the intersection region. The algorithm then collects the vertices of the convex polytope that overlap the two structures. To show the effectiveness of proposed algorithm, this study proposes a consensus criterion for a multi-agent time-delayed system with uncertain switching topologies as an application of a system with uncertainties in system elements.

Bridged Pt–OH–Mn Mediator in N‐coordinated Mn Single Atoms and Pt Nanoparticles for Electrochemical Biomolecule Oxidation and Discrimination

The rational design of efficient catalysts for uric acid (UA) electrooxidation, as well as the establishment of structure‐activity relationships, remains a critical bottleneck in the field of electrochemical sensing. To address these challenges, herein, a hybrid catalyst that integrates carbon‐supported Pt nanoparticles and nitrogen‐coordinated Mn single atoms (PtNPs/MnNC) is developed. The metal‐metal interaction during annealing affords the construction of metallic‐bonded Pt−Mn pairs between PtNPs and Mn single atoms, facilitating the electron transfer from PtNPs to the support and thereby optimizing the electronic structure of catalysts. More importantly, experiments and theoretical calculations provide visual proof for the ‘incipient hydrous oxide adatom mediator’ mechanism for UA oxidation. The Pt−Mn pairs first adsorb OH* to construct the bridged Pt–OH–Mn mediators to serve as a highly active intermediate for N–H bond dissociation and proton transfer. Benefiting from the unique electronic and geometric structure of the catalytic center and reactive intermediates, PtNPs/MnNC exhibits superior electrooxidation performance. The electrochemical sensor based on PtNPs/MnNC enables sensitive detection and discrimination of UA and dopamine in serum samples. This work offers new insights into the construction of novel electrocatalysis for sensitive sensing platforms.

A Thorough Examination of the Variables Affecting the Quantum Efficiency of Radiative Decay of Trichlorotriphenylmethyl Radicals.

Fluorescence quantum efficiency is determined by the competition between radiation and nonradiation processes of the excited states. Understanding the factors affecting the radiation and nonradiative decay rates is of great significance for the design of luminescent materials. The excitation state deactivation mechanisms of singlet and triplet states have been extensively studied, providing a comprehensive understanding of the processes involved in the relaxation of these states. However, research on free radical systems involving doublet states is relatively scarce. Therefore, in this study, radiation and nonradiative decay rates and the mechanism of a series of trichlorotriphenylmethyl-based radicals were investigated theoretically. The results indicate that the relative rotations of electron donor and acceptor, as well as the internal rotations of trichlorotriphenylmethyl moiety, play important roles in energy dissipation through nonradiative channels. The effect of a solid-state environment on the radiation and nonradiative decay rates of radicals was investigated using a combination of quantum mechanics and molecular mechanics methods. The results indicate that the solid-state environment restricts the expansion of the conjugated system in the excited state of radicals, leading to a slight decrease in radiative decay rate. In addition, the solid-state environment reduces the reorganization energy and also affects the adiabatic excitation energy of radicals. The reduction in reorganization energy results in a decrease in nonradiative rate, while the opposite effect is observed for adiabatic excitation energy. The nonradiative rate of radicals in a solid-state environment is thus inflected by a combination of molecular geometric structure relaxation and ground-excited state energy gap.

CorTAF-LBE: A full scale subchannel-level thermal-hydraulic characteristics analysis code for LBR core based on OpenFOAM

The lead-bismuth cooled fast reactor core adopts box-type design, and the fuel rod bundle is fixed by wrapping wires. The complex geometric structure of the fuel assemblies significantly affects the flow and heat transfer status of the liquid metal coolant. Building upon the open-source CFD platform, OpenFOAM, this work established conservation equations for liquid metal coolant, fuel rod thermal characteristic models, inter-wrapper coolant analysis models, and coupled heat transfer models, and developed the subchannel level thermal-hydraulic analysis code CorTAF-LBE for whole-core analysis of LBR core using the finite volume method. The wire-wrapped rod bundles internal flow and heat transfer experiments of lead-bismuth, carried out based on the THEADES and KYLIN-II circulation loops, were selected to perform verification and validation. The results match well with the experimental data and CFD fine-modeling prediction. The steady-state numerical simulation of three wire-wrapped rod bundle assemblies with inter-wrapper flow was carried out under normal operation condition. The temperature and pressure distributions of the coolant in each subchannel and inter-wrapper flow region, the temperature of cladding and other key thermal-hydraulic parameters were obtained. The role of the inter-wrapper coolant in heat removal from the fuel assemblies was revealed. The work in this paper has referential value for the optimization of lead-bismuth cooled fast reactor design and safety assessment during operation.

Nontrivial evolution and geometric phase for an orbital angular momentum qutrit

Photonic orbital angular momentum (OAM) offers a promising platform for high-dimensional quantum information processing. While geometric phase (GP) is the crucial tool in enabling intrinsically fault-tolerant quantum computation, the measurement of GP using linear optics remains relatively unexplored in the OAM state space. Here, we propose an experimental scheme to detect GP shifts resulting from the cyclic evolution of OAM qutrit states. Distinguished with the conventional evolution along cyclic path on the Poincaré sphere (PS), the nontrivial evolution in our theoretical scheme is along a cyclic path residing within the SU(3)/U(2) parameter space. By employing a combination of X-gates, dove prisms, and double cylindrical lenses, we achieve the cyclic evolution and analyse the resultant GP through our designed Sagnac interferometer. Our theoretical study may find potential in high-dimensional quantum computation using twisted photons and in exploring the geometric structure of such optical systems.

Theoretical Insights into the Effect of Different Numbers of Thiophene Groups on Hydrogen Bond Interaction and Excited-State Intramolecular Proton-Transfer Process for Flavonoid Derivatives.

In this study, we systematically explored the impact of varying the number of thiophene groups on the hydrogen bond interaction and excited-state intramolecular proton-transfer (ESIPT) processes in flavonoid derivatives (STF, DTF, and TTF) using the density functional theory and time-dependent density functional theory methods. Initially, a thorough analysis of the optimized geometric structures revealed that the intramolecular hydrogen bond in the S1 state is enhanced and gradually weakened as the number of thiophene groups increases. To gain a deeper understanding of the hydrogen bond interaction, topological analysis, interaction region indicator scatter plots, and isosurface plots were employed. These images provide further insights that align with the structural analysis. Additionally, we observed a red-shift in the electronic spectra (absorption and fluorescence spectra), which is primarily attributed to the narrowing of the energy gap between the highest occupied molecular orbital and the lowest unoccupied molecular orbital, as elucidated by the frontier molecular orbitals. Furthermore, a combined analysis between the hole-electron distribution and the transition density matrix heat map shows that electron excitation involves the unidirectional charge-transfer mechanism. In the end, by conducting relaxed potential energy curve scans, we found that an increase in the number of thiophene groups elevates the energy barrier for ESIPT, making it more challenging for the reaction. In summary, our study underscores the vital effect of thiophene-substituted numbers in modulating the ESIPT process, which is able to provide valuable insights for the design and synthesis of desired organic fluorescent probes in the future.

DFTB Simulation of Charged Clusters Using Machine Learning Charge Inference.

We present a modification to self-consistent charge density functional-based tight binding (SCC-DFTB), which allows computation based on approximate atomic charges. We obtain these charges by means of a machine learning (ML) process that combines a Coulomb model with a neural network. This allows us to avoid the SCC cycles in the SCC-DFTB calculation while maintaining its accuracy. The main input of the model is the atomic positions characterized by a set of atom-centered symmetry functions. The charge inference from our ML algorithm is as close as 10-2 units of charge from the exact SCC solution. Our ML-DFTB approach provides a good approximation of the density matrix and of the energy and forces with only a single diagonalization. This is a significant computational saving with respect to the complete SCC algorithm, which allows us to investigate a bigger ensemble of atoms. We show the quality of our approach in the case of charged silicon carbide (SiC) clusters. The ML-DFTB potential energy surface (PES) mimics the SCC-DFTB PES rather well, despite its simplicity. This allows us to obtain the same geometric structure ordering with respect to energy for small clusters. The dissociation barriers for ion emission are well-reproduced, which opens the way to investigating ion field emission and charged cluster stability. The ML-DFTB approach is obviously not limited to charged clusters or SiC materials. It opens a new route to investigate larger clusters than those investigated by standard SCC-DFTB, as well as surface and solid-state chemistry at the atomic level.