Dimensional Surface Research Articles

Single Photon Computed Tomography (SPECT) Findings in Cases with Japanese Adult- Attention-Deficit/Hyperactivity Disorder (ADHD): Special Attention to Cerebellar Posterior Lobe Hypoperfusion on Iodoamphetamine (IMP) SPECT using 3 Dimensional Stereotactic Surface Projection (3D-SSP) Analysis

Single Photon Computed Tomography (SPECT) Findings in Cases with Japanese Adult- Attention-Deficit/Hyperactivity Disorder (ADHD): Special Attention to Cerebellar Posterior Lobe Hypoperfusion on Iodoamphetamine (IMP) SPECT using 3 Dimensional Stereotactic Surface Projection (3D-SSP) Analysis

Doubly periodic solutions and non-topological solitons of [formula omitted] dimension Wazwaz Kaur Boussinesq equation employing Jacobi elliptic function method

In this research communication the Boussinesq like equation namely 2+1− dimensional Wazwaz Kaur Boussinesq equation is studied and gotten their Jacobi elliptic function (doubly periodic) solutions employing the simple algebraic Jacobi elliptic function method. The said method makes the two transformations of nonlinear partial differential equations to ordinary differential equations to solvable system of algebraic equations. The gotten doubly periodic solutions are modular transformed to show the degeneration into non-topological solitons, compound non-topological singular solitons and complex singular solitons. The three dimensional surfaces are drawn for few elliptic solutions and solitons.

Enhancing Electric Discharge Machining Performance by Selecting Electrode Design and Geometrical Parameters for Square, Triangular, and Hexagonal Profiled Holes

Manufacturing of dies, molds, and their allied components requires the machining of holes with different profiles. Electric discharge machining (EDM) die-sinking is a crucial process used in the dies and molds manufacturing industry. By nature, EDM die-sinking is a relatively slow process in terms of material removal rate (MRR) and there are high amounts of tool material loss in terms of tool wear rate (TWR) which directly influence dimensional accuracies and surface roughness (SR). Therefore, the process is continuously evolving to address these limitations. The present research is aligned in this direction such as to bring improvements in MRR, TWR, and SR through modifications to the conventional electrode design and its geometrical parameters. Traditional designs of EDM electrodes have a uniform cross-section through the tool’s entire length and have only one geometrical parameter, i.e., the tool’s cross-section. To improve the EDM performance, traditional designs are completely modified by introducing several geometrical parameters such as relief angles, land thickness, cross-sectional area, shank height, circular relief, and non-circular relief, etc. Electrode designs are employed to mill non-circular profiles including triangular, square, and hexagonal shaped holes. The EDM performance measures strongly depend on the tool’s geometrical parameters (design type, relief angle, land thickness), machining profile (circular, square, triangle, hexagon), as well as the height/depth of the machining feature. By selecting proper tool designs and corresponding geometrical parameters, the EDM performance measures can be improved significantly.

Effects of process parameters variations and optimization of biodiesel production from orange seed oil using raw and thermal clay as catalyst

In this study, process parameters for the production of biodiesel from orange seed oil using raw and thermal clay as catalyst were analyzed using randomized optimal design. The design was a response surface method (full fractional factorial) which identified the various design points as being numerical and discrete. The process optimization was performed by varying five factors, each at two different levels. The process parameters: methanol to oil molal ratio (mol/mol), catalyst concentration (weight %), reaction time (minutes), temperature (°C) and agitation speed (revolution per minutes, rpm) were the independent variables (input), while the biodiesel yield (vol/vol) was the dependent variable (response) in the optimization process. The designed matrix, 3D (three dimensional) surface plots, contour plots and analysis of variance (ANOVA), was achieved using the Design Expert Software (version 12.0), the optimum biodiesel yield was predicted and experimented to ascertain the interactive effects of parameters. The correlating regression coefficient indicated the satisfactory performance of the model for the raw and thermal clay. The experimental/actual maximum optimal biodiesel yield for the biodiesel production from the orange seed oil using raw and thermal clay as catalyst was 79.53 and 94.58% v/v while the predicted biodiesel yield was 79.55 and 92.98% v/v. The set of conditions that caused these positive effects were established at Time of 150 minutes, Temperature 65 °C, methanol /sample molal ratio of 12:1, catalyst concentration of 3.0 wt. % and agitation speed at 300 rpm respectively. Thisresults shows agreement between the actual and predicted biodiesel yield. It can be concluded that the best biodiesel yield can be achieved using thermally modified clay as catalyst.

Modeling and multi-objective optimization of abrasive water jet machining process of composite laminates using a hybrid approach based on neural networks and metaheuristic algorithm

Although the abrasive water jet (AWJ) has proven to be a suitable process for machining composite materials, it has some limitations related to dimensional inaccuracy and surface defects. As the performance of the AWJ process mainly depends on the machining parameters, an optimal selection of them is crucial to achieving an improved quality of cut. In this context, the present study reports an experimental investigation to assess the influence of AWJ machining parameters on kerf taper angle (θ) and surface roughness ( Ra) of E glass/Vinylester 411 resin laminates. The experiments are carried out using a full factorial design by varying the water pressure, traverse speed, abrasive flow rate, and standoff distance. A first-ever attempt is made in this paper to optimize the AWJ process using a hybrid approach combining artificial neural networks (ANNs) with a recently proposed metaheuristic algorithm known as multi-objective bonobo optimizer (MOBO). The results show that standoff distance and abrasive flow rate were the most significant control factors in influencing θ and Ra, respectively. The developed ANN models are capable to predict the output responses with high accuracy and the solutions from the Pareto front provide a sufficient performance with a trade-off between θ and Ra. The corresponding levels of the optimal process parameters are 430 g/min for the abrasive flow rate, the range of 140–180 mm/min for the traverse speed, 280 MPa for the pressure, and 1.5 mm for the standoff distance.

Investigation of Part Quality Achieved by Material Extrusion Printers in Relation to Their Price

Users of material extrusion printers are faced with a wide range of prices. It is unknown which printer price can achieve the required part quality. However, the price and the resulting quality of a printer are decisive factors for the process, especially at small- and medium-sized companies. This study investigated the correlation between the printer price and part quality based on dimensional accuracy, surface quality, strength, and visual appearance. In this paper, 14 printers with different prices were examined. The relationship of printer price and part defects, elongation at break, and the accuracy of roundings could be identified (the regressions achieved a p-value under 0.5 and an R2 over 0.4). A relationship with surface roughness, tensile strength, or other dimensional accuracy characteristics could not be found (the regressions achieved an R2 under 0.4 or anomalies could be detected in the regression analysis). In the performed investigations, more-expensive printers were not necessarily associated with an improvement in these quality characteristics. No relationship between the printer price and the standard deviation, e.g., less variation in part quality, could be identified. This paper provides valuable insights into the relationship of part quality and printer price. The performed research improved upon the existing literature in terms of the number of investigated printers, the observed price range, and the number of tested quality characteristics. The results and approach of this paper will help users select an appropriate printer, and the findings can be used in the sourcing and technology selection phases.

Investigation of bead morphology and mechanical behaviour for metal inert gas welding-based WAAM in pulsed mode metal transfer on 316LSi stainless steel

Bead geometry plays a significant role in the fabrication of artefacts via the wire arc additive manufacturing (WAAM) route especially with respect to the dimensional characteristics, surface properties, and part density. It also substantially affects the material penetration, part accuracy, and thickness of subsequent layers. The present study focuses on the effect of processing parameters on single-bead geometry for fabricating 316LSi stainless steel in pulsed-mode metal transfer WAAM. Models were developed by utilizing multiple regression modelling, machine learning (ML) based on back propagation neural network (BPNN), and finite element modelling (FEM) with four process parameters, wire feed rate (WFR), torch travel speed (TTS), voltage (V), and contact tip-to-substrate distance (CTSD), each considered at three levels. Response surface methodology was utilized for design of experiments to obtain three-bead geometry responses, namely width of weld bead (WWB), height of weld bead (HWB), and depth of penetration (DOP). The results confirm that all three proposed models predict bead geometry reasonably. However, accuracy of ML-based BPNN method is better as compared to the regression and FEM-based approaches. For multi-objective optimization, a desirability function approach is used, which predicts the optimal conditions at WFR of 4.0 m/min, TTS of 284.545 mm/min, voltage of 18.0 V, and the CTSD of 12.0 mm for minimum WWB, maximum HWB, and minimum DOP. A multi-layered wall of SS316LSi is fabricated with optimized process parameters, and it is found that the ultimate tensile strength, yield strength, and percentage elongation are 546.3 MPa, 440.1 MPa, and 43.7%, respectively. WAAM-deposited structure reveals stable deposition, no debonding/delamination, and complete fusion between layers. The results of the present article will go a long way in aiding the researchers/industrial personnel in arriving on the optimal modelling approach and deposition conditions for WAAM-fabricated SS316LSi parts with enhanced mechanical properties and productivity.

Abrasive water flow polishing of superalloy microholes drilled by electrical discharge machining

Superalloy microholes resulting from electrical discharge drilling (EDD) can have recast layers and microcracks on the machined surfaces. In this study, the abrasive water flow polishing (AWFP) of superalloy microholes machined by EDD was investigated. The recast layer and surface roughness resulting from the EDD process were evaluated through orthogonal experiments. The machining mechanism of AWFP was analyzed. The effects of AWFP parameters were investigated experimentally. The results show that the material removal rate and surface roughness at the hole exit were better than those at the entrance for one-directional fluid flow during the process. Garnet abrasives achieved better polished roughness and a lower material removal rate than alumina abrasives. The taper angle of the target holes changed slightly from positive to negative after AWFP. The corner edge was slightly rounded on the fluid inlet side. Using a bidirectional fluid resulted in better dimensional uniformity and surface roughness at the entrance and exit. This study verified that AWFP can remove the recast layer and microcracks on the hole wall caused by EDD and reduce the surface roughness from an initial Ra of 5.7 μm to an Ra of 0.6 μm.

Characterizing postural balance on 2-dimensional compliant surfaces with directional virtual time-to-contact

BackgroundThis study aimed to (1) investigate postural balance control on 2-Dimensional (2D) compliant surfaces using directional virtual time-to-contact (d-VTC), a novel method for VTC calculation; and (2) compare d-VTC with conventional balance measures in this context. MethodsA dual-axis robotic platform was used to simulate 2D surfaces/grounds with varying compliance levels. Twenty healthy young adults stood on the platform with either open or closed eyes. Balance was evaluated using d-VTC in multiple aspects, including temporal (VTC mean), spatial (boundary contact - BC), and control aspects (switching rate – SR). Additionally, conventional balance measures, namely center-of-pressure (COP) area and COP root-mean-square (RMS), were employed for further comparisons with d-VTC measures. Normality checks were performed using Shapiro-Wilk tests. Two-way repeated measures ANOVA tests were used to examine the effects of surface compliance and vision on postural balance, followed by post-hoc pairwise comparisons across conditions with Bonferroni correction. ResultsThe results showed that increasing surface compliance and/or absence of vision caused a significant decrease in VTC mean (all p-values <0.001; all ηp2 > 0.816). Interaction effects between surface compliance and vision on 2D and ML VTC mean were also significant (all p-values <0.019; all ηp2 > 0.355). The AP and ML BC values indicated a converging trend to 50%. No vision effect was observed (p = 0.458), but both surface compliance (p = 0.001; ηp2 = 0.522) and interaction (p = 0.002; ηp2 = 0.492) effects were significant. Decreases in SR were significant due to the compliance of the standing surface (p = 0.01; ηp2 = 0.401) but not vision (p = 0.109). COP area increased due to both surface and vision conditions (all p-values <0.001; all ηp2 > 0.872). AP and ML RMS were altered by vision (all p-values <0.001; all ηp2 > 0.741), but not by surface condition (all p-values >0.06). No interaction effect was observed in the conventional measures (all p-values >0.07). ConclusionBalance control is compromised by 2D compliant surfaces, which is exacerbated when vision is absent. Among all balance measures, VTC mean measures demonstrated particularly high sensitivity in identifying decreased balance capabilities, while BC and SR provided new insights into fall risks and balance control mechanisms. These insights may facilitate the development of rehabilitation training or assistive devices for fall prevention.

Application of IBSEF Method to Chaffee–Infante Equation in (1 + 1) and (2 + 1) Dimensions

In this work, Improved Bernoulli Sub-Equation Function (IBSEF) method is proposed to seek solitary solutions of nonlinear differential equations. Chaffee–Infante equations are chosen to illustrate the effectiveness and convenience of the suggested method. Abundant new and more general exact solutions are obtained of these equations. As a result, by selecting the suitable parameters, two and three dimensional surfaces and contour plots of the results are drawn with the help of the software program.

Collimated solar simulator for curved PV modules characterization

The vehicle integrated photovoltaic (VIPV) technology, which consists in integrating PV solar panels in the surfaces of electric vehicles, is a promising technology to increase car autonomy. Free-form curved PV surfaces are demanded to meet the specific design constraints of the automotive. The proper characterization of three dimensional PV surfaces requires specific methods and equipment that must be developed. This paper describes the design principles and requirements of a solar simulator for characterization of curved PV modules. Its effectiveness is demonstrated by means of ray-tracing simulations, the results show the advantages provided by the use of a collimated light source in comparison to the conventional solar simulators used for flat modules. The light beam divergence of a non-collimated light source produces a non-uniformity boost between 2% and 20% depending on the module size and curvature. The module performance will be affected by this non-uniform irradiance, but the performance loss will also depend on specific characteristics of the module such as curvature, number and size of cells, series/parallel electrical connection and number of by-pass diodes. On the contrary, the proposed collimated solar simulator reproduces the solar illumination profile over the curved surface. The Helios 3198, a solar simulator with collimated light developed for concentrator modules, has been adapted accordingly to the design proposed. A module of 1 m of curvature has been tested, the short-circuit current of the cells follows the ideal cosine response of the curvature, differences are lower than 0.5% which proves the quality of the collimated beam.

Design Optimization of DR3AM Vapor Polishing Device for ABS 3D-Printed Parts

3D printing is an additive manufacturing method that turns digital design into an actual product. A 3D-printed part sometimes requires post-processing to enhance its physical and mechanical properties. Acetone vapor polishing is one of those techniques which is highly beneficial in smoothing ABS 3D-printed parts. Previously, an acetone vapor polishing device has been developed which uses a mist maker. However, for a more efficient polishing method, an optimized vapor polishing device using heat has been fabricated in this study. To assess the efficiency of this device, the researchers test the dimensional accuracy, surface roughness, tensile strength, and impact strength of polished and unpolished ABS 3D-printed specimens. The findings showed that the surface smoothness of the polished cube specimens did not significantly alter its physical geometry. The tensile test reveals that the overall elasticity of the polished tensile specimen has increased significantly while the impact test also shows that the polished specimens have the capacity to sustain a resistive impact from a swinging pendulum. Thus, all testing procedures indicated that post-processing using the optimized vapor polishing device has improved the overall physical and mechanical properties of the polished specimens.

Singular Integral Operators Supported in Higher Dimensional Surfaces

Singular Integral Operators Supported in Higher Dimensional Surfaces

Nonlinear electrophoretic velocity of a spherical colloidal particle

Electrophoresis is the motion of a charged colloidal particle in an electrolyte under an applied electric field. The electrophoretic velocity of a spherical particle depends on the dimensionless electric field strength $\beta =a^*e^*E_\infty ^*/k_B^*T^*$ , defined as the ratio of the product of the applied electric field magnitude $E_\infty ^*$ and particle radius $a^*$ , to the thermal voltage $k_B^*T^*/e^*$ , where $k_B^*$ is Boltzmann's constant, $T^*$ is the absolute temperature, and $e^*$ is the charge on a proton. In this paper, we develop a spectral element algorithm to compute the electrophoretic velocity of a spherical, rigid, dielectric particle, of fixed dimensionless surface charge density $\sigma$ over a wide range of $\beta$ . Here, $\sigma =(e^*a^*/\epsilon ^*k_B^*T^*)\sigma ^*$ , where $\sigma ^*$ is the dimensional surface charge density, and $\epsilon ^*$ is the permittivity of the electrolyte. For moderately charged particles ( $\sigma ={O}(1)$ ), the electrophoretic velocity is linear in $\beta$ when $\beta \ll 1$ , and its dependence on the ratio of the Debye length ( $1/\kappa ^*$ ) to particle radius (denoted by $\delta =1/(\kappa ^*a^*)$ ) agrees with Henry's formula. As $\beta$ increases, the nonlinear contribution to the electrophoretic velocity becomes prominent, and the onset of this behaviour is $\delta$ -dependent. For $\beta \gg 1$ , the electrophoretic velocity again becomes linear in field strength, approaching the Hückel limit of electrophoresis in a dielectric medium, for all $\delta$ . For highly charged particles ( $\sigma \gg 1$ ) in the thin-Debye-layer limit ( $\delta \ll 1$ ), our computations are in good agreement with recent experimental and asymptotic results.

Symbol Detection with Deep Learning

This study shows the feasibility of detecting some symbols from a distance. Therefore, it will be possible to use these symbols as identification of things, e.g. people, vehicles, objects, information labels etc. There is such a necessity especially for construction sites to know the rough location of workers. In order to achieve this goal, firstly some two-dimensional symbols that are distinguishable from each other by both human eye and computer are generated. Afterwards, they are put on a three dimensional surface and annotated. Lastly, the labeled data are trained by the deep learning method YOLO version 5. Results indicate that it is highly efficient to use the chosen symbols for recognizing from a distance. For now, up to 10 meter images are taken into account. Real time tests are taken and the accuracy is about 85%. As the dataset will be combined with real-life images, the accuracy results will be even higher.

Laser directed energy deposition/milling hybrid additive manufacturing of thin-walled GH4169 alloy: Effect of processing strategy on its microstructure and mechanical properties

Laser directed energy deposition (LDED)/milling hybrid manufacturing process was utilized to produce thin-walled GH4169 parts for the first time. The impact of processing strategy, including varied milling rounds and building heights, on the printability, microstructure, and mechanical properties of the GH4169 parts were investigated. Increasing the number of milling rounds within a certain height along the build direction reduced the distance between adjacent hybrid-processed interfaces. Base on a comparative study of the thermal histories of LDED and LDED/milling processes, it was found that the microstructure at the hybrid-processed interfaces experienced less heat accumulation and fewer heating/cooling cycles. Therefore, GH4169 parts after milling exhibited a lower proportion of the Laves phase area with refined sizes and greater interdendritic Nb segregation than those produced by LDED alone. With increased milling rounds, the ultimate tensile strength and yield strength of the thin-walled GH4169 part fabricated using the hybrid process increased from 777.0 MPa to 823.0 MPa and from 506.0 MPa to 538.0 MPa, respectively, while the elongation decreased from 35.1% to 27.2%. These effects can be attributed to an increase in milling round within a constant forming distance, resulting in higher stiffness of the fabricated thin-walled GH4169 part. Finally, a thin-walled GH4169 blade with excellent dimensional accuracy and surface finish was successfully produced using the LDED/milling hybrid process, demonstrating the potential applicability of this process to nickel-based alloys.

A Review of the Residual Stress Generation in Metal Additive Manufacturing: Analysis of Cause, Measurement, Effects, and Prevention.

Metal additive manufacturing (AM) is capable of producing complex parts, using a wide range of functional metals that are otherwise very difficult to make and involve multiple manufacturing processes. However, because of the involvement of thermal energy in the fabrication of metallic AM parts, residual stress remains one of the major concerns in metal AM. This residual stress has negative effects on part quality, dimensional accuracy, and part performance. This study aims to carry out a comprehensive review and analysis of different aspects of residual stress, including the causes and mechanisms behind the generation of residual stress during metal AM, the state-of-the-art measurement techniques for measuring residual stress, various factors influencing residual stress, its effect on part quality and performance, and ways of minimizing or overcoming residual stress in metal AM parts. Residual stress formation mechanisms vary, based on the layer-by-layer deposition mechanism of the 3D printing process. For example, the residual stress formation for wire-arc additive manufacturing is different from that of selective laser sintering, direct energy deposition, and powder bed fusion processes. Residual stress formation mechanisms also vary based on the scale (i.e., macro, micro, etc.) at which the printing is performed. In addition, there are correlations between printing parameters and the formation of residual stress. For example, the printing direction, layer thickness, internal structure, etc., influence both the formation mechanism and quantitative values of residual stress. The major effect residual stress has on the quality of a printed part is in the distortion of the part. In addition, the dimensional accuracy, surface finish, and fatigue performance of printed parts are influenced by residual stress. This review paper provides a qualitative and quantitative analysis of the formation, distribution, and evolution of residual stress for different metal AM processes. This paper also discusses and analyzes both in situ and ex situ measurement techniques for measuring residual stress. Microstructural evolution and its effect on the formation of residual stress are analyzed. Various pre- and post-processing techniques used to countermeasure residual stress are discussed in detail. Finally, this study aims to present both a qualitative and quantitative analysis of the existing data and techniques in the literature related to residual stress, as well as to provide a critical analysis and guidelines for future research directions, to prevent or overcome residual stress formation in metal AM processes.

3D Koch-type crystals

We consider the construction of a family \{K_N\} of 3 -dimensional Koch-type surfaces, with a corresponding family of 3 -dimensional Koch-type “snowflake analogues” \{\mathcal{C}_N\} , where N&gt;1 are integers with N \not\equiv 0 \mathrm{mod}\,\,3 . We first establish that the Koch surfaces K_N are s_N -sets with respect to the s_N -dimensional Hausdorff measure, for s_N=\log(N^2+2)/\log(N) the Hausdorff dimension of each Koch-type surface K_N . Using self-similarity, one deduces that the same result holds for each Koch-type crystal \mathcal{C}_N . We then develop lower and upper approximation monotonic sequences converging to the s_N -dimensional Hausdorff measure on each Koch-type surface K_N , and consequently, one obtains upper and lower bounds for the Hausdorff measure for each set \mathcal{C}_N . As an application, we consider the realization of Robin boundary value problems over the Koch-type crystals \mathcal{C}_N , for N&gt;2 .

Railway Bridge Geometry Assessment Supported by Cutting-Edge Reality Capture Technologies and 3D As-Designed Models

Documentation of structural visual inspections is necessary for its monitoring, maintenance, and decision about its rehabilitation, and structural strengthening. In recent times, close-range photogrammetry (CRP) based on unmanned aerial vehicles (UAVs) and terrestrial laser scanners (TLS) have greatly improved the survey phase. These technologies can be used independently or in combination to provide a 3D as-is image-based model of the railway bridge. In this study, TLS captured the side and bottom sections of the deck, while the CRP-based UAV captured the side and top sections of the deck, and the track. The combination of post-processing techniques enabled the merging of TLS and CRP models, resulting in the creation of an accurate 3D representation of the complete railway bridge deck. Additionally, a 3D as-designed model was developed based on the design plans of the bridge. The as-designed model is compared to the as-is model through a 3D digital registration. The comparison allows the detection of dimensional deviation and surface alignments. The results reveal slight deviations in the structural dimension with a global average value of 9 mm.

Manifold learning for fMRI time-varying functional connectivity.

Whole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolves over time in meaningful ways at temporal scales going from years (e.g., development) to seconds [e.g., within-scan time-varying FC (tvFC)]. Yet, our ability to explore tvFC is severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers often seek to generate low dimensional representations (e.g., 2D and 3D scatter plots) hoping those will retain important aspects of the data (e.g., relationships to behavior and disease progression). Limited prior empirical work suggests that manifold learning techniques (MLTs)-namely those seeking to infer a low dimensional non-linear surface (i.e., the manifold) where most of the data lies-are good candidates for accomplishing this task. Here we explore this possibility in detail. First, we discuss why one should expect tvFC data to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (ID; i.e., minimum number of latent dimensions) of tvFC data manifolds. Third, we describe the inner workings of three state-of-the-art MLTs: Laplacian Eigenmaps (LEs), T-distributed Stochastic Neighbor Embedding (T-SNE), and Uniform Manifold Approximation and Projection (UMAP). For each method, we empirically evaluate its ability to generate neuro-biologically meaningful representations of tvFC data, as well as their robustness against hyper-parameter selection. Our results show that tvFC data has an ID that ranges between 4 and 26, and that ID varies significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed: UMAP and T-SNE can capture these two levels of detail concurrently, but LE could only capture one at a time. We observed substantial variability in embedding quality across MLTs, and within-MLT as a function of hyper-parameter selection. To help alleviate this issue, we provide heuristics that can inform future studies. Finally, we also demonstrate the importance of feature normalization when combining data across subjects and the role that temporal autocorrelation plays in the application of MLTs to tvFC data. Overall, we conclude that while MLTs can be useful to generate summary views of labeled tvFC data, their application to unlabeled data such as resting-state remains challenging.