Vertical Axis Research Articles

A Graph Neural Network-Based Approach to XANES Data Analysis.

The determination of three-dimensional structures (3D structures) is crucial for understanding the correlation between the structural attributes of materials and their functional performance. X-ray absorption near edge structure (XANES) is an indispensable tool to characterize the atomic-scale local 3D structure of the system. Here, we present an approach to simulate XANES based on a customized 3D graph neural network (3DGNN) model, XAS3Dabs, which takes directly the 3D structure of the system as input, and the inherent relation between the fine structure of spectrum and local geometry is considered during the model construction. It turns out to be faster than the traditional XANES fitting method when the simulation approach and XANES optimization algorithm are combined to fit the 3D structure of the given system. The geometric features of the system are included in the weighted message passing block of XAS3Dabs and their importance is investigated. XAS3Dabs model demonstrates superior accuracy in XANES prediction compared to most machine learning models. By extracting graphs constituted by edges related to the absorbing atom, our model reduces redundant information, thereby not only enhancing the model's performance but also improving its robustness across different hyperparameters. XAS3Dabs model can be generalized to simulate the spectra for the systems with the absorber having the designed absorption edge so as to meet the expectations of online data processing. The method is expected to be the key part of the online 3D structure analysis framework for the XAS-related beamlines of high-energy photon source (HEPS) now under construction.

Paleomagnetic data from volcanic rocks in the southern Central Andes of Argentina and their implications for tectonics and geomagnetic field behavior

Abstract A paleomagnetic study of basaltic lava flows exposed in the northern Neuquén Cordillera, southernmost Central Andes, along the Antiñir-Copahue fault zone (ACFZ), involved 25 sites of the Cola de Zorro Formation (Pliocene–Early Pleistocene) along two different sections. The sites show exclusive normal polarity, corresponding to the Late Pliocene Gauss chron (3.6–2.6 Ma). The angular standard deviation of virtual geomagnetic poles (VGPs; ASD = 14.8°) is consistent with the expected values from recent geomagnetic models, in opposition to anomalously low dispersion found in previous studies in Pleistocene VGPs of reverse polarity from neighboring areas to our study zone. Mean paleomagnetic directions for Bella Vista (Dec = 0.0°, Inc = −50.0°, α₉₅ = 7.6°, K = 36.7, N = 11) and Río Huaraco sections (Dec = 354.9°, Inc = −57.0°, α₉₅ = 7.5°, K = 55.7, N = 8) do not show tectonic rotation around vertical axes. Combining and regrouping our and previous data by area confirmed the absence of tectonic rotations in the Huaraco-Trohunco block and a statistically significant clockwise rotation of 14.4° ± 10.3° of three adjacent tectonic blocks located south of our study locality in Pleistocene times. These results suggest that strike-slip deformation along some sections of the ACFZ was significant in the Pleistocene structural evolution of this region.

Analysis and Applications of Magnetically Coupled Resonant Circuits

Magnetically coupled resonant circuits have been utilized in radio engineering for many years. Recently, these circuits have found new applications, particularly in supplying low- and medium-power devices through medium-range wireless power transmission technology based on magnetic resonance coupling. There is also a growing need to analyze magnetically coupled resonant circuits in the design of metamaterials, which exhibit specific electromagnetic properties within certain frequency bands. This paper provides a coherent and concise overview of the phenomena associated with magnetically coupled resonant circuits. The results encompass numerous established relations as well as new ones. They can be helpful in the design phase of magnetically coupled resonant circuits. The outcomes include equations for determining the coupling resonant frequencies and some parameters of resonance curves. These analytical results are accompanied by 3D and cross-sectional (contour) graphs for better visualization. Moreover, a lumped-element circuit model that includes magnetically coupled resonant circuits is proposed for the resonators used in metamaterials. Formulas for resonant frequencies are derived in the specific case of exciting such resonators. To validate the accuracy of the derived equations, the analytical results are compared with simulations from SPICE (IsSPICE4 ver. 8.1) and COMSOL 5.4 software, which are widely used tools for circuit analysis and electromagnetic simulations. The results of these comparative analyses indicate that the assumptions employed in the analytical solutions introduce only tiny errors.

Artificial Intelligence (AI) and Learning Management Systems (LMS): A bibliometric analysis

The advent of Artificial Intelligence (AI) has transformed Learning Management Systems (LMSs), enabled personalized adaptation and facilitated distance education. This study employs a bibliometric analysis based on PRISMA-2020 to examine the integration of AI in LMSs from an educational perspective. Despite the rapid progress observed in this field, the literature reveals gaps in the effectiveness and acceptance of virtual assistants in educational contexts. Therefore, the objective of this study is to examine research trends on the use of AI in LMSs. The results indicate a quadratic polynomial growth of 99.42%, with the years 2021 and 2015 representing the most significant growth. Thematic references include authors such as Li J and Cavus N, the journal Lecture Notes in Computer Science, and countries such as China and India. The thematic evolution can be observed from topics such as regression analysis to LMS and e-learning. The terms e-learning, ontology, and ant colony optimization are highlighted in the thematic clusters. A temporal analysis reveals that suggestions such as a Cartesian plane and a league table offer a detailed view of the evolution of key terms. This analysis reveals that emerging and growing words such as Learning Style and Learning Management Systems are worthy of further investigation. The development of a future research agenda emerges as a key need to address gaps.

Dunkl-Schrödinger equation in higher dimensions

Abstract This paper presents analytical solutions for eigenvalues and eigenfunctions of the Schrödinger equation in higher dimensions, incorporating the Dunkl operator. Two fundamental quantum mechanical problems are examined in their exact forms: the d-dimensional harmonic oscillator and the Coulomb potential. In order to obtain analytical solutions to these problems, both Cartesian and polar coordinate systems were employed. Firstly, the Dunkl-Schrödinger equation is derived in d-dimensional Cartesian coordinates, and then for the isotropic harmonic potential interaction, its solutions are given. Subsequently, using polar coordinates the angular and radial parts of the Dunkl-Schrödinger equation are obtained. It is demonstrated that the system permits the separation of variables in both coordinate systems, with the resulting separated solutions expressed through Laguerre and Jacobi polynomials. Then, the radial Dunkl-Schrödinger equation is solved using the isotropic harmonic, pseudoharmonic, and Coulomb potentials. The eigenstates and eigenvalues are obtained for each case and the behavior of the energy eigenvalue functions are illustrated graphically with the reduced probability densities.

Comparative Aerodynamic Analysis and Parallel Performance of 2D CFD Simulations of a VAWT Using Sliding Mesh Interface Method

ABSTRACTWith rapid advancements in computer hardware and numerical modeling methods, Computational Fluid Dynamics (CFD) has gained prominence in simulating complex flows. As parallel computation becomes an industry standard, the computational efficiency of simulations has become critical. The flow around a Vertical Axis Wind Turbine (VAWT), characterized by complex dynamics and challenging rotating geometry, serves as an intriguing case for CFD studies. This study employs the open‐source CFD solvers SU2 and OpenFOAM to simulate the incompressible, unsteady, and turbulent flow around an H‐type Darrieus VAWT in two dimensions. Spatial and temporal discretization parameters are examined to balance computational cost and accuracy, revealing notable effects on power predictions. Simulations conducted under identical conditions allow for a comparison of the predictions and parallel performances of SU2 and OpenFOAM across three distinct tip speed ratios (TSRs). The findings show that discretization parameters behave differently at various TSRs. While power predictions from SU2 and OpenFOAM generally align with experimental data and with each other, discrepancies arise at lower TSRs, with thrust predictions showing better consistency. Although OpenFOAM provides a faster solution across all parallel configurations, SU2 demonstrates superior parallel scalability, achieving higher speedup and efficiency.

Materials Design and Structural Health Monitoring of Horizontal Axis Offshore Wind Turbines: A State-of-the-Art Review

In recent decades, Offshore Wind Turbines (OWTs) have become crucial to the clean energy transition, yet they face significant safety challenges due to harsh marine conditions. Key issues include blade damage, material corrosion, and structural degradation, necessitating advanced materials and real-time monitoring systems for enhanced reliability. Carbon fiber has emerged as a preferred material for turbine blades due to its strength-to-weight ratio, although its high cost remains a barrier. Structural Health Monitoring Systems (SHMS) play a vital role in detecting potential faults through real-time data on structural responses and environmental conditions. Effective monitoring approaches include vibration analysis and acoustic emission detection, which facilitate early identification of anomalies. Additionally, robust data transmission technologies are essential for SHMS effectiveness. This paper reviews material design strategies, data acquisition methods, and safety assessment techniques for OWTs, addressing current challenges and future directions in the field.

The effect of blade turning angle on rotation improvement of a small horizontal axis wind turbine

Optimization of wind turbine aerodynamic performances implies solving the problem in the domains such as airfoil selection, blade rotation angle, chord optimization, number of blades, appropriate tip speed ratio, etc. The current paper studies the effect of blade rotation angle [Formula: see text] on power coefficient [Formula: see text], torque [Formula: see text] and mechanical power [Formula: see text] in a small horizontal axis wind turbine ( HAWT). The analysis involves two models of blades ( V and) of the airfoil class SD 7003. The first part of the paper employs numerical analysis, that is, Ansys CFX and Ansys Fluent software packages, to define wind turbine characteristics. The obtained results indicate that as the rotation angle [Formula: see text] increases the [Formula: see text] increases within an appropriate range of tip speed ratio [Formula: see text] change. The second part describes experimental investigation conducted on considered HAWT to define the values of [Formula: see text] and [Formula: see text] for studied blade rotation angles and comparison with the values obtained from Ansys. Existing differences in values are a consequence of the design of the HAWT, that is, the influence of the mass moment of inertia of the rotor and generator as well as the placement of the generator. Experimental setup and methodology allow for investigating the effect of different blade models, rotor and generator structure on wind turbine torque and mechanical power output characteristic.

Dynamic reliability estimation of onshore wind turbine tower based on frequency-domain method

With the upscaling of wind turbine, the issue of dynamic reliability of wind turbine towers has become increasingly evident. However, the complexity and nonlinearity of wind turbine dynamics present significant challenges in evaluating their dynamic reliability under stochastic loads, requiring substantial computational resources. In this paper, a computationally cost-effective frequency-domain method is proposed as a means of estimating the dynamic reliability of an onshore wind turbine tower. An analytical approach is employed to obtain the power spectral density (PSD) of the rated wind speed and load on the rotating blades. Secondly, a dynamic model of onshore horizontal axis onshore wind turbine is established using flexible multi-body dynamics (MBD). The linear time-varying dynamic equation is transformed into a linear time-invariant form through the application of statistical linearization method. Subsequently, complex modal analysis is used to employ the displacements and internal forces of the tower. Moreover, the first-passage reliability and fatigue reliability of the wind turbine tower are determined through power spectral analysis. A numerical example of a 5-MW wind turbine is presented to demonstrate that the proposed method yields results in good agreement with Monte Carlo simulations (MCS), thereby demonstrating its effectiveness. The reliability analysis of the 5-MW wind turbine reveals that over a 20-year design life, the first-passage reliability is 0.9997 when adopting fore-after displacement at the tower top as the failure criterion, and the fatigue reliability of the tower base is 0.9445.

SCORBOT arm control using pseudoinverse jacobian control for Bézier path

The kinematics of manipulators represents a fundamental challenge in the field of robotic automatic control. This challenge involves transitioning between Cartesian space and combinatorial space. The kinematic equations governing motion are formulated using the Denavit-Hartenberg (D-H) notation. This paper introduces a method for the inversion of a SCORBOT ER-V plus manipulator, aimed at meticulously analyzing the arm's movement from one spatial point to another. To implement this solution, it is necessary to identify only the starting and ending points in space, along with the geometric constraints of the manipulator. The SCORBOT-ER V plus is a straightforward top-bottom robot featuring five revolute joints, recognized for its reliability and safety, particularly in laboratory and training environments. This experiment provides students with practical experience in robotics, automation, and control systems, highlighting the principles related to the operation and causation of various phenomena. The Python programming language is utilized to develop a mathematical model that complies with a defined set of constraints and guidelines. The analysis of object motion demonstrated that the output from the Python script closely aligned with the actual measurements obtained from the robotic arm. Differential movement is employed as a technique to analyze and characterize motion at different points of the robot, thereby facilitating the examination of robotic movement over short durations. This paper discusses both forward modeling and inverse kinematics within the realm of robotics. The traditional D-H model is applied to create the mathematical framework essential for determining and assessing the position of the end-effector in a 5-Degree-Of-Freedom (5DOF) robot, which will then be used to calculate the necessary joint configurations. Finally, we present the results derived from this experiment.

The impact of lower thoracic versus upper lumbar upper instrumented vertebra in minimally invasive correction of adult spinal deformity.

The goal of this study was to compare the impact of using a lower thoracic (LT) versus upper lumbar (UL) level as the upper instrumented vertebra (UIV) on clinical and radiographic outcomes following minimally invasive surgery for adult spinal deformity. A multicenter retrospective study design was used. Inclusion criteria were age ≥ 18 years, and one of the following: coronal Cobb angle > 20°, sagittal vertical axis > 50 mm, pelvic tilt > 20°, pelvic incidence-lumbar lordosis mismatch > 10°. Patients were treated with circumferential or hybrid minimally invasive techniques at ≥ 3 spinal levels and had a 2-year minimum follow-up. They were then divided into 2 groups depending on whether the UIV was in the UL region, defined as a UIV location of L1-2, or the LT region, defined as T10-12. A total of 114 of 223 patients met the inclusion criteria (68 LT and 46 UL). The UL group was older (67.5 vs 62.3 years; p = 0.015). Preoperative spinopelvic parameters were similar, except for sacral slope, which was higher in the UL group (30.5° vs 26.5°; p < 0.001). The percentage of patients with fixation crossing the lumbosacral junction was also similar (70.6% vs 67.4%; p = 0.717). Postoperative lumbar lordosis (42.5° vs 35.5°; p = 0.01) and change in coronal Cobb angle (-23.2° vs -9.6°; p < 0.001) were greater in the LT group, but other changes in postoperative spinopelvic parameters and changes in health-related quality-of-life scores were similar between groups. Reoperation rates were lower in the UL group (17.4% vs 36.8%; p = 0.025), largely associated with fewer radiographic failures (UL = 10.9% vs LT = 26.5%; p = 0.042); however, overall complication rates were not significantly different (UL = 43.5% vs LT = 60.3%; p = 0.077). Selecting a UL vertebra for UIV in minimally invasive surgical correction of adult spinal deformity results in lower reoperation rates compared to extending fixation to the LT region. This choice also correlates with shorter operating room times and reduced estimated blood loss. Although extending fixation to the LT region is associated with slightly greater lumbar lordosis and a greater change in the coronal Cobb angle, clinical outcomes were similar between the LT and UL groups for UIV.

Machine learning with industrial robots: exploring the impact of joint angles on Cartesian coordinates using explainable AI

Machine learning with industrial robots: exploring the impact of joint angles on Cartesian coordinates using explainable AI

An automated construction method of 3D knowledge graph based on multi-agent systems in virtual geographic scene

ABSTRACT Virtual geographic scenes can significantly facilitate the visualization and comprehension of the real world. However, the multitude of object types and complex relationships they contain make it challenging to display embedded geographic knowledge. Knowledge graphs help organize object entities and their relationships within scenes, which makes it easier to store and express geographic knowledge. Current methods for constructing knowledge graphs for virtual geographic scenes suffer from issues such as weak automation, low dimensionality, and poor cognitive efficiency. Thus, this paper presents a method for automatically constructing knowledge graphs by integrating intelligent systems. The method involves designing a multiagent system to extract entities and relationships hidden in virtual geographic scenes. In addition, a three-dimensional visualization approach is proposed for knowledge graphs constrained by spatiotemporal relationships. A virtual scene containing 2,183 objects was selected as the experimental area for constructing and displaying the knowledge graph. The results demonstrate that the method constructs a knowledge graph with 12,958 relationships within 300 s in common network and hardware settings. In addition, a knowledge graph offers better completeness at small scales compared to other geographic knowledge graphs, and it can also enhance the cognitive efficiency of understanding scene relationships.

Effects of Different Blade Sweep Models and Blade Geometries on the Performance of Horizontal Axis Wind Turbines

Effects of Different Blade Sweep Models and Blade Geometries on the Performance of Horizontal Axis Wind Turbines

The Effect of Pacemakers and Defibrillators on Distortion in 2 Magnetic Resonance Imaging (MRI) Sequences Commonly Used in Radiation Oncology Practice—3D True Fast Imaging with Steady State Precession (TrueFISP) at 0.35T MR-Linear Accelerator (LINAC) and 3D T1 at 3T MR Simulator

Background: We aimed to measure the pacemaker- and defibrillator-induced distortion at 0.35T and 3.0T magnetic fields. Methods: The pacemaker/defibrillator was placed at the top center of a water-filled/MagPhan phantom, followed by a T1 scan at 3T and a TrueFISP scan at 0.35T. The extent of distortion (i.e., the distance from the device to the furthest signal loss/void/rings) in the water-filled phantom was measured in MIM. For geometrical distortion (i.e., dislocation of geometrical structures), the spheres in the MagPhan phantom were contoured and their distortion was calculated based on their manufacturing coordinate positions. Results: The maximum extent of distortion caused by the defibrillator was 18.8 cm at 0.35T and 5.8 cm at 3.0T. Similarly, the maximum extent of distortion caused by the pacemaker was 9.28 cm at 0.35T and 2.8 cm at 3.0T. Geometrical distortion measurements using the MagPhan phantom showed that the maximum distortion caused by the defibrillator was 12.8 mm at 0.35T and 13.2 mm at 3.0T. Likewise, the maximum distortion caused by the pacemaker was 8.7 mm at 0.35T and 6.0 mm at 3.0T. Conclusions: Defibrillators cause larger distortions/signal voids than pacemakers, and require careful consideration when performing MRI-based treatment planning. To minimize distortion, sequences with lower sensitivity to magnetic field inhomogeneity should be used.

The Impact of Virtual Reality Content Characteristics on Cybersickness and Head Movement Patterns.

Virtual reality (VR) technology has gained popularity across various fields; however, its use often induces cybersickness, characterized by symptoms such as dizziness, nausea, and eye strain. This study investigated the differences in cybersickness levels and head movement patterns under three distinct VR viewing conditions: dynamic VR (DVR), static VR (SVR), and a control condition (CON) using a simulator. Thirty healthy adults participated, and their head movements were recorded using the Meta Quest 2 VR headset and analyzed using Python. The Virtual Reality Sickness Questionnaire (VRSQ) assessed subjective cybersickness levels. The results revealed that the SVR condition induced the highest VRSQ scores (M = 58.057), indicating the most severe cybersickness symptoms, while the DVR condition elicited significantly higher values in head movement variables, particularly in the coefficient of variation (CV) and integral values of head position along the vertical axis, and mean velocity (p < 0.05). These findings suggest that VR content characteristics directly influence users' head movement patterns, closely related to cybersickness occurrence and severity. This study highlights the importance of analyzing head movement patterns in cybersickness research and provides insights for VR content design.

Impact of Vertical Scaling on Normal Probability Density Function Plots.

Probability density function (PDF) curves are among the few charts on a Cartesian coordinate system that are commonly presented without y-axes. This design decision may be due to the lack of relevance of vertical scaling in normal PDFs. In fact, as long as two normal PDFs have the same means and standard deviations (SDs), they can be scaled to occupy different amounts of vertical space while still remaining statistically identical. Because unfxed PDF height increases as SD decreases, visualization designers may fnd themselves tempted to vertically shrink low-SD PDFs to avoid occlusion or save white space in their fgures. Although irregular vertical scaling has been explored in bar and line charts, the visualization community has yet to investigate how this visual manipulation may affect reader comparisons of PDFs. In this paper, we present two preregistered experiments (n = 600, n = 401) that systematically demonstrate that vertical scaling can lead to misinterpretations of PDFs. We also test visual interventions to mitigate misinterpretation. In some contexts, we fnd including a y-axis can help reduce this effect. Overall, we fnd that keeping vertical scaling consistent, and therefore maintaining equal pixel areas under PDF curves, results in the highest likelihood of accurate comparisons. Our fndings provide insights into the impact of vertical scaling on PDFs, and reveal the complicated nature of proportional area comparisons.

Rotational Stability and Outcomes of V4c Toric Implantable Collamer Lenses Placed at Different Lens Orientations.

To evaluate the 12-month rotational stability and outcomes of V4c toric Implantable Collamer Lenses (TICLs) (STAAR Surgical) located at two different lens orientations (horizontal or oblique). This retrospective study included a total of 403 eyes with TICL implantation, enrolled between January 2021 and December 2022, that were divided into two groups based on the minimum intended angle off the horizontal axis: horizontal with 263 eyes at an angle ±22.5 degrees or less and oblique with 140 eyes at a minimum intended angle of greater than ±22.5 degrees. A comparison was made of the preoperative characteristics, surgical outcomes, secondary surgical interventions, footplate positions, and adverse events at 1, 6, and 12 months postoperatively. The rotation-related factors were analyzed. At 12 months, there were 243 eyes (92%) in the horizontal group and 130 eyes (93%) in the oblique group that were within ±0.50 diopters (D) of emmetropia. Their rotation proportion was 5.3 % and 5.7%, respectively, and their rotation angle was 9.11 ± 2.84 and 9.96 ± 2.42 degrees, respectively (P = .086). TICL footplates were mostly positioned on the ciliary body. No correlation was observed of lens placement axis, vault, or manifest refraction with rotation in both groups (P > .05). During the follow-up, no vision-threatening complications were observed in both groups. Rotation cases were successfully addressed via alignment or lens exchange. TICLs, when implanted across a range of lens orientations, demonstrated favorable rotational stability and postoperative outcomes. The postoperative rotational stability is independent of the placement angle of the TICL. [J Refract Surg. 2025;41(1):e29-e38.].

Deep learning-driven prediction of chemical addition patterns for carboncones and fullerenes.

Carboncones and fullerenes are exemplary π-conjugated carbon nanomaterials with unsaturated, positively curved surfaces, enabling the attachment of atoms or functional groups to enhance their physicochemical properties. However, predicting and understanding the addition patterns in functionalized carboncones and fullerenes are extremely challenging due to the formidable complexity of the regioselectivity exhibited in the adducts. Existing predictive models fall short in systems where the carbon molecular framework undergoes severe distortion upon high degrees of addition. Here, we propose an incremental deep learning approach to predict regioselectivity in the hydrogenation of carboncones and chlorination of fullerenes. Utilizing exclusively graph-based features, our deep neural network (DNN) models rely solely on atomic connectivity, without requiring 3D molecular coordinates as input or their iterative optimization. This advantage inherently avoids the risk of obtaining chemically unreasonable optimized structures, enabling the handling of highly distorted adducts. The DNN models allow us to study regioselectivity in hydrogenated carboncones of C70H20 and C62H16, accommodating up to at least 40 and 30 additional H atoms, respectively. Our approach also correctly predicts experimental addition patterns in C50Cl10 and C76Cln (n = 18, 24, and 28), whereas in the latter cases all other known methods have been proven unsuccessful. Compared to our previously developed topology-based models, the DNN's superior predictive power and generalization ability make it a promising tool for investigating complex addition patterns in similar chemical systems.

Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines.

Data-driven reaction discovery and development is a growing field that relies on the use of molecular descriptors to capture key information about substrates, ligands, and targets. Broad adaptation of this strategy is hindered by the associated computational cost of descriptor calculation, especially when considering conformational flexibility. Descriptor libraries can be precomputed agnostic of application to reduce the computational burden of data-driven reaction development. However, as one often applies these models to evaluate novel hypothetical structures, it would be ideal to predict the descriptors of compounds on-the-fly. Herein, we report DFT-level descriptor libraries for conformational ensembles of 8528 carboxylic acids and 8172 alkyl amines towards this goal. Employing 2D and 3D graph neural network architectures trained on these libraries culminated in the development of predictive models for molecule-level descriptors, as well as the bond- and atom-level descriptors for the conserved reactive site (carboxylic acid or amine). The predictions were confirmed to be robust for an external validation set of medicinally-relevant carboxylic acids and alkyl amines. Additionally, a retrospective study correlating the rate of amide coupling reactions demonstrated the suitability of the predicted DFT-level descriptors for downstream applications. Ultimately, these models enable high-fidelity predictions for a vast number of potential substrates, greatly increasing accessibility to the field of data-driven reaction development.