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14 pages, 1628 KiB  
Article
Antimicrobial Steroids from Poisonous Mushroom Gymnopilus orientispectabilis and Their Molecular Docking Studies
by Bowon Jung, Eun Jin Heo, Dieu Linh Nguyen, Ui Joung Youn, Ki Hyun Kim, Boram Son and Seulah Lee
Separations 2025, 12(2), 23; https://doi.org/10.3390/separations12020023 - 24 Jan 2025
Abstract
In this study, three fungal steroids (13) were isolated from the fruiting bodies of the poisonous mushroom Gymnopilus orientispectabilis, based on bioactivity-guided isolation methods. The chemical structures of the isolates (13) were determined using [...] Read more.
In this study, three fungal steroids (13) were isolated from the fruiting bodies of the poisonous mushroom Gymnopilus orientispectabilis, based on bioactivity-guided isolation methods. The chemical structures of the isolates (13) were determined using NMR spectroscopic methods. Compounds 13 exhibited inhibition activity against E. coli, and their interactions with several bacterial drug targets were studied via in silico molecular docking, where the lowest binding energies were observed for penicillin binding protein 3 (PBP3) (−62.89, −75.89 and −74.47 kcal/mol, for compounds 1, 2 and 3, respectively). An MD simulation was performed to examine the conformational stability, motion and flexibility of protein–ligand complexes. In conclusion, this study investigates fungal steroids from G. orientaspectabilis as potential sources for new antimicrobial agents, encouraging further research to develop novel therapies. Full article
38 pages, 48465 KiB  
Article
Investigation into the Motion Characteristics and Impact Loads of Buoy Water Entry Under the Influence of Combined Waves and Currents
by Wei Ge, Xiaolong Ying, Hailong Chen, Sheng Wu, Jian Zhang, Lixue Jiang and Hengxu Liu
J. Mar. Sci. Eng. 2025, 13(2), 218; https://doi.org/10.3390/jmse13020218 - 24 Jan 2025
Viewed by 200
Abstract
As a crucial component in marine monitoring, meteorological observation, and navigation systems, studying the motion characteristics and impact loads of buoy water entry is vital for their long-term stability and reliability. When deployed, buoys undergo a complex motion process, including the impact of [...] Read more.
As a crucial component in marine monitoring, meteorological observation, and navigation systems, studying the motion characteristics and impact loads of buoy water entry is vital for their long-term stability and reliability. When deployed, buoys undergo a complex motion process, including the impact of entering the water and a stable floating stage. During the water entry impact phase, the motion characteristics and impact loads involve interactions between the buoy and the water, the trajectory of motion, and dynamic water pressure, among other factors. In this paper, the VOF model is used to calculate the buoy’s water entry motion characteristics, and then the STAR-CCM+&ABAQUS bidirectional fluid–structure interaction (FSI) method is used to calculate the water entry impact load of the buoy under different water surface conditions and different initial throwing conditions, considering the influence of the flow field on the structure and the influence of the structure deformation on the flow field. The study finds that under the influence of wave and current impacts, changes in wave height significantly affect the buoy’s heave motions. Under different parametric conditions, due to the specific direction of wave and current impacts, the buoy’s pitch amplitude is relatively more intense compared to its roll amplitude, yet both pitch and roll motions exhibit periodic patterns. The buoy’s pitch motion is sensitive to changes in the entry angle; even small changes in this angle result in significant differences in pitch motion. Additionally, the entry angle significantly impacts the peak vertical overload on the buoy. Instantaneous stress increases sharply at the moment of water entry, particularly at the joints between the crossplate and the upper and lower panels, and where the mast connects to the upper panel, creating peak stress concentrations. In these concentrated stress areas, as the entry speed and angle increase, the maximum equivalent stress peak at the monitoring points rises significantly. Full article
Show Figures

Figure 1

Figure 1
<p>Schematic of the original physical scenario of a cylindrical buoy entering water at an angle.</p>
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<p>Cylindrical buoy geometric model and its dimensional parameters.</p>
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<p>Computational domain boundary types.</p>
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<p>Computational domain mesh.</p>
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<p>CFD computational domain mesh in co-simulation.</p>
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<p>Refined area mesh division.</p>
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<p>Mesh division of CFD buoy model and CAE buoy model.</p>
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<p>Layout of monitoring points. (<b>a</b>) External monitoring points S1 to S7; (<b>b</b>) internal reinforced crossplate surface monitoring points I1 to I9; (<b>c</b>) monitoring points X1 to X8 at the junction of the upper panel and internal reinforced crossplates.</p>
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<p>Heave data curves at different mesh densities.</p>
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<p>Impact load data curves at different mesh densities.</p>
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<p>Wave height monitoring locations.</p>
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<p>Results of wave height monitoring.</p>
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<p>Comparison of pitch angle changes between this study’s numerical simulation and literature/experimental data.</p>
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<p>The structure of the rotating body model and the setup of monitoring points in the referenced literature.</p>
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<p>The comparison between the effective stress data of S1 and S3 obtained from the numerical simulation in this study and the experimental data from the referenced paper.</p>
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<p>A schematic of the buoy’s inclined water entry at different wave heights (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi>a</mi> <mi>m</mi> <mi>p</mi> <mi>l</mi> <mi>i</mi> <mi>t</mi> <mi>u</mi> <mi>d</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> = h/2, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi>a</mi> <mi>m</mi> <mi>p</mi> <mi>l</mi> <mi>i</mi> <mi>t</mi> <mi>u</mi> <mi>d</mi> <mi>e</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> = 0.25 m, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi>a</mi> <mi>m</mi> <mi>p</mi> <mi>l</mi> <mi>i</mi> <mi>t</mi> <mi>u</mi> <mi>d</mi> <mi>e</mi> <mo>−</mo> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> = 0.5 m, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi>a</mi> <mi>m</mi> <mi>p</mi> <mi>l</mi> <mi>i</mi> <mi>t</mi> <mi>u</mi> <mi>d</mi> <mi>e</mi> <mo>−</mo> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> = 1 m).</p>
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<p>The motion characteristics of the buoy under different wave height conditions.</p>
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<p>The motion attitude of the buoy under different wave height conditions.</p>
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<p>The motion characteristics of the buoy under different entry speed conditions.</p>
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<p>The motion attitude of the buoy under different entry speed conditions.</p>
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<p>The motion characteristics of the buoy under different entry angle conditions.</p>
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<p>The motion attitude of the buoy under different entry angle conditions.</p>
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<p>The motion attitude of the buoy under different entry angle conditions.</p>
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<p>Maximum equivalent stress variation curves at the external monitoring points of the buoy under different wave height conditions.</p>
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<p>Maximum equivalent stress variation curves at the external monitoring points of the buoy under different wave height conditions.</p>
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<p>The maximum equivalent stress clouds and corresponding motion attitudes of external monitoring points on the buoy under different wave height conditions (the clear legend of the maximum equivalent stress clouds can be found in <a href="#app1-jmse-13-00218" class="html-app">Appendix A</a>, as follows).</p>
Full article ">Figure 24 Cont.
<p>The maximum equivalent stress clouds and corresponding motion attitudes of external monitoring points on the buoy under different wave height conditions (the clear legend of the maximum equivalent stress clouds can be found in <a href="#app1-jmse-13-00218" class="html-app">Appendix A</a>, as follows).</p>
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<p>Maximum equivalent stress variation curves at the crossplate monitoring points of the buoy under different wave height conditions.</p>
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<p>Maximum equivalent stress variation curves at the monitoring points of the connection between the crossplate and the upper panel of the buoy under different wave height conditions.</p>
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<p>Maximum equivalent stress variation curves at the external monitoring points of the buoy under different entry speed conditions.</p>
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<p>The maximum equivalent stress clouds and corresponding motion attitudes of external monitoring points on the buoy under different entry speed conditions.</p>
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<p>The maximum equivalent stress clouds and corresponding motion attitudes of external monitoring points on the buoy under different entry speed conditions.</p>
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<p>Maximum equivalent stress variation curves at the crossplate monitoring points of the buoy under different entry speed conditions.</p>
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<p>Maximum equivalent stress variation curves at the monitoring points of the connection between the crossplate and the upper panel of the buoy under different entry speed conditions.</p>
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<p>The maximum equivalent stress variation curves at external monitoring points of the buoy under different entry angle conditions.</p>
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<p>The maximum equivalent stress clouds and corresponding motion attitudes of the external monitoring points on the buoy under different entry angle conditions.</p>
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<p>The maximum equivalent stress variation curves at the crossplate monitoring points of the buoy under different entry angle conditions.</p>
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<p>The maximum equivalent stress variation curves at the monitoring points of the connection between the crossplate and the upper panel of the buoy under different entry angle conditions.</p>
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<p>The impact of wave height, entry speed, and entry angle on the maximum equivalent stress peaks. (<b>a</b>) Maximum equivalent stress peak—wave height(Line X4 and line X5 coincide); (<b>b</b>) Maximum equivalent stress peak—speed; (<b>c</b>) Maximum equivalent stress peak—angle.</p>
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<p>Maximum equivalent stress clouds under different wave height conditions.</p>
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<p>Maximum equivalent stress clouds under different wave height conditions.</p>
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<p>Maximum equivalent stress clouds under different entry speed conditions.</p>
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<p>Maximum equivalent stress clouds under different entry speed conditions.</p>
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<p>Maximum equivalent stress clouds under different entry angle conditions.</p>
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<p>Maximum equivalent stress clouds under different entry angle conditions.</p>
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17 pages, 326 KiB  
Article
Marsilio Ficino and the Soul: Doctrinal and Argumentative Remarks Regarding His Use of the Elements of Physics and the Elements of Theology
by Sokratis-Athanasios Kiosoglou
Philosophies 2025, 10(1), 14; https://doi.org/10.3390/philosophies10010014 - 23 Jan 2025
Viewed by 305
Abstract
The depth and extent of Ficino’s reception and use of Proclus has already attracted much scholarly attention. The present paper builds on and tries to enrich these results, focusing specifically on Ficino’s reception of Proclus’ Elements of Physics and Elements of Theology. [...] Read more.
The depth and extent of Ficino’s reception and use of Proclus has already attracted much scholarly attention. The present paper builds on and tries to enrich these results, focusing specifically on Ficino’s reception of Proclus’ Elements of Physics and Elements of Theology. In the first part I discuss a marginal annotation of Ficino, in which he makes use of arguments about the circular motion of the soul from the Elements of Physics. I provide some clarifications about the annotated text (of Plotinus) and propose one additional possible echo of the Elements of Physics in Ficino’s Platonic Theology and its arguments about the immortality of the soul. The second part of the paper turns to the link between the Elements of Theology and Ficino’s Platonic Theology. Together with some further doctrinal borrowings I suggest that also the structure of the two works bears important affinities. The soul is a central case in point. To ground this claim, I compare specific sections of the two texts. Also, I selectively examine Ficino’s commentary on the Philebus, which is prior to the Platonic Theology and is strongly influenced by the early theorems of the Elements of Theology. Overall, the paper wishes to shed further light on Ficino’s multiform (and not yet fully unveiled) appropriation of Proclus. Full article
(This article belongs to the Special Issue Ancient and Medieval Theories of Soul)
16 pages, 5016 KiB  
Article
Real-Time Observation of Polymer Fluctuations During Phase Transition Using Transmission Electron Microscope
by Takaaki Shiina, Tatsunari Ohkubo, Keegan McGehee, Rena Inamasu, Tatsuya Arai, Daisuke Sasaki, Yuji C. Sasaki and Kazuhiro Mio
Polymers 2025, 17(3), 292; https://doi.org/10.3390/polym17030292 - 23 Jan 2025
Viewed by 265
Abstract
Measuring molecular dynamics improves understanding of the structure–function relationships of materials. In this study, we present a novel technique for observing material dynamics using transmission electron microscopy (TEM), in which the gold nanoparticles are employed as motion probes for tracing the polymer dynamics [...] Read more.
Measuring molecular dynamics improves understanding of the structure–function relationships of materials. In this study, we present a novel technique for observing material dynamics using transmission electron microscopy (TEM), in which the gold nanoparticles are employed as motion probes for tracing the polymer dynamics in real space. A thin layer of polymer materials was generated on the 2 μm diameter holes of Quantifoil grids, and gold nanoparticles were dispersed on the membrane surface. By tracking the movement of gold nanoparticles from a series of TEM images taken under continuous temperature control, we obtained mean squared displacement (MSD) curves. The dynamics of poly{2-(perfluorooctyl)ethyl acrylate} (PC8FA) and poly(stearyl acrylate) (PSA) were analyzed. In the temperature-dependent analysis of the MSD, sharp peaks were observed for both PC8FA and PSA at positions corresponding to their melting and crystallization temperatures. These results demonstrate the capability of TEM to provide valuable insights into the dynamics of polymer materials, highlighting its potential for widespread application in materials sciences. Full article
(This article belongs to the Section Polymer Analysis and Characterization)
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Figure 1

Figure 1
<p>Experimental procedure and analysis: (<b>a</b>) Schematic illustration of TEM-based direct observation of thermally induced polymer fluctuations. (<b>b</b>) Images of gold nanoparticles embedded on the polymer surface. (<b>c</b>) Flowchart of the data analysis process.</p>
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<p>TEM-based motion analysis of PC<sub>8</sub>FA in heating/cooling cycles: (<b>a</b>) Chemical structure of PC<sub>8</sub>FA. (<b>b</b>) TEM image of gold nanoparticles on the PC<sub>8</sub>FA membrane. (<b>c</b>) MSD curves of PC<sub>8</sub>FA in heating process. (<b>d</b>) MSD of (upper) the heating processes from 0 to 98 °C, and (lower) the cooling processes from 98 to 0 °C. Data were obtained from the third cycle of the continuous heating and cooling processes. MSDs at specific time intervals of 8 s (Δt = 8 s) were analyzed. (<b>e</b>) DSC results of PC<sub>8</sub>FA.</p>
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<p>Probability density histograms of PC<sub>8</sub>FA in heating cycles: (<b>a</b>) PD histograms of PC<sub>8</sub>FA under the heating process, taken at 5 °C intervals between 75–90 °C. (<b>b</b>) Overlay of fitting curves for the PC<sub>8</sub>FA heating process. In the figures, ‘n’ represents the total number of displacements.</p>
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<p>Probability density histograms of PC<sub>8</sub>FA in cooling cycles: (<b>a</b>) PD histograms of PC<sub>8</sub>FA under the cooling process, taken at 5 °C intervals between 60–85 °C. (<b>b</b>) Overlay of fitting curves for the PC<sub>8</sub>FA cooling process. In the figures, ‘n’ represents the total number of displacements.</p>
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<p>TEM-based motion analysis of PSA in heating/cooling cycle: (<b>a</b>) Chemical structure of PSA. (<b>b</b>) TEM image of gold nanoparticles on the PSA membrane. (<b>c</b>) MSD curves of PSA in heating process. (<b>d</b>) MSD of (<b>left</b>) heating process from 0 to 98 °C, and (<b>right</b>) cooling process from 98 to 0 °C. MSDs at specific time intervals of 8 s (Δt = 8 s) were analyzed. (<b>e</b>) DSC results of PSA.</p>
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<p>Probability density histograms of PSA in heating cycles: (<b>a</b>) PD histograms of PSA under the heating process, taken at 5 °C intervals between 50–65 °C. (<b>b</b>) Detailed PD histograms of PSA under the heating process, taken at 0.5 °C intervals between 56–58 °C. (<b>c</b>) Overlay of fitting curves for the PSA heating process. In the figures, ‘n’ represents the total number of displacements.</p>
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<p>Probability density histograms of PSA in cooling cycles: (<b>a</b>) PD histograms of PSA under the cooling process, taken at 2.5 °C intervals between 40–50 °C. (<b>b</b>) Overlay of fitting curves for the PSA cooling process. In the figures, ‘n’ represents the total number of displacements.</p>
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<p>Boxplot analysis of PC<sub>8</sub>FA and PSA in heating/cooling cycles. Boxplot analysis of PC<sub>8</sub>FA and PSA in (<b>a</b>) the heating process and (<b>b</b>) the cooling process. Box plots represent the minimum, 25th percentile, 50th percentile, 75th percentile, and maximum.</p>
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15 pages, 17939 KiB  
Article
Evaluating CBCT-Guided Adaptive Radiotherapy for Pancreatic Cancer Using Synthetic CBCT Data
by Sven Olberg, Leah L. Thompson, Hannah J. Roberts, Jennifer Y. Wo, Theodore S. Hong, John Wolfgang, Clemens Grassberger and Jennifer Pursley
Curr. Oncol. 2025, 32(2), 60; https://doi.org/10.3390/curroncol32020060 - 23 Jan 2025
Viewed by 291
Abstract
Ethos adaptive radiotherapy is employed frequently in the pelvis to improve treatment accuracy by adapting to daily anatomical changes. The use of this CBCT-guided platform for abdominal treatments is made challenging by motion-related image artifacts that are detrimental to the Ethos auto-contouring process. [...] Read more.
Ethos adaptive radiotherapy is employed frequently in the pelvis to improve treatment accuracy by adapting to daily anatomical changes. The use of this CBCT-guided platform for abdominal treatments is made challenging by motion-related image artifacts that are detrimental to the Ethos auto-contouring process. We present a preliminary in silico study enabled by synthetic CBCT data of Ethos adaptive radiotherapy for pancreatic cancer. Simulation CT and daily CBCT images were collected from nonadaptive patients treated on Ethos. Contoured CBCTs drove structure-guided deformable registration from the CT to daily CBCTs, providing an approximate daily CT used to produce synthetic CBCT data. Two adaptive workflows were simulated using an Ethos emulator. Over 70 fractions across 10 patients in a solely deformation-based workflow, PTV prescription coverage increased by 23.3±9.4% through plan adaptation. Point doses to the stomach were reduced by 10.2±9.3%. Ultimately, un-adapted plans satisfied target coverage and OAR constraints in 0% and 6% of fractions while adapted plans did so in 80% of fractions. Anatomical variation led to poor performance in rigidly aligned un-adapted plans, illustrating the promise of Ethos adaptive radiotherapy in this region. This promise is balanced by the need for artifact reduction and questions regarding auto-contouring performance in the abdomen. Full article
(This article belongs to the Section Gastrointestinal Oncology)
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Figure 1

Figure 1
<p>Synthetic CBCT generation process. Contours of the stomach (blue), liver (green), and both kidneys (cyan, yellow) manually drawn on daily CBCTs are used to drive a structure-guided deformable image registration from the planning CT with clinical contours to the daily CBCT. Analytical projections are then acquired of the deformed CT and reconstructed to produce the synthetic CBCT data.</p>
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<p>Target and OAR dose metrics achieved in the initial IMRT treatment plans (<math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>) in the Ethos treatment planning system (blue) compared to the clinically delivered, nonadaptive VMAT plans (orange) for reference. Dose objectives are included in <a href="#curroncol-32-00060-t001" class="html-table">Table 1</a>.</p>
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<p>Comparison for the auto-contouring workflow between dose metrics in scheduled and adapted plans over 70 simulated fractions for the low-dose targets (<b>a</b>), high-dose targets (<b>b</b>), stomach (<b>c</b>), bowel (<b>d</b>), duodenum (<b>e</b>), and kidneys (<b>f</b>). Coverage goals and dose limits from <a href="#curroncol-32-00060-t001" class="html-table">Table 1</a> are illustrated in each case as dotted lines of the corresponding color along with the line of equality between scheduled and adapted plans.</p>
Full article ">Figure 3 Cont.
<p>Comparison for the auto-contouring workflow between dose metrics in scheduled and adapted plans over 70 simulated fractions for the low-dose targets (<b>a</b>), high-dose targets (<b>b</b>), stomach (<b>c</b>), bowel (<b>d</b>), duodenum (<b>e</b>), and kidneys (<b>f</b>). Coverage goals and dose limits from <a href="#curroncol-32-00060-t001" class="html-table">Table 1</a> are illustrated in each case as dotted lines of the corresponding color along with the line of equality between scheduled and adapted plans.</p>
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<p>Comparison for the deformation-based workflow between dose metrics in scheduled and adapted plans over 70 simulated fractions for the low-dose targets (<b>a</b>), high-dose targets (<b>b</b>), stomach (<b>c</b>), bowel (<b>d</b>), duodenum (<b>e</b>), and kidneys (<b>f</b>). Coverage goals and dose limits from <a href="#curroncol-32-00060-t001" class="html-table">Table 1</a> are illustrated in each case as dotted lines of the corresponding color along with the line of equality between scheduled and adapted plans.</p>
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<p>Daily CBCT images from a single patient (not included in the 10 patient cohort of this study) acquired using the pre-upgrade imaging panel with a 17 s acquisition period (left) and using the upgraded HyperSight system with a 6 s acquisition period (right). Ethos-produced stomach contours at a representative slice are shown. The anatomy observed in fractions (<b>a</b>,<b>b</b>) is similar but the full extent of the stomach is more accurately contoured in the post-upgrade image (<b>b</b>). Motion artifacts that lead to poor contouring performance as illustrated in pre-upgrade image (<b>c</b>) also cause failures post-upgrade (<b>d</b>).</p>
Full article ">
25 pages, 14926 KiB  
Article
Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
by Huazhe Zhang, Nian Liu, Juan Xia, Lejun Chen and Shengde Chen
Agriculture 2025, 15(3), 236; https://doi.org/10.3390/agriculture15030236 - 22 Jan 2025
Viewed by 367
Abstract
Plant genomics have progressed significantly due to advances in information technology, but phenotypic measurement technology has not kept pace, hindering plant breeding. As maize is one of China’s three main grain crops, accurately measuring plant height is crucial for assessing crop growth and [...] Read more.
Plant genomics have progressed significantly due to advances in information technology, but phenotypic measurement technology has not kept pace, hindering plant breeding. As maize is one of China’s three main grain crops, accurately measuring plant height is crucial for assessing crop growth and productivity. This study addresses the challenges of plant segmentation and inaccurate plant height extraction in maize populations under field conditions. A three-dimensional dense point cloud was reconstructed using the structure from motion–multi-view stereo (SFM-MVS) method, based on multi-view image sequences captured by an unmanned aerial vehicle (UAV). To improve plant segmentation, we propose a column space approximate segmentation algorithm, which combines the column space method with the enclosing box technique. The proposed method achieved a segmentation accuracy exceeding 90% in dense canopy conditions, significantly outperforming traditional algorithms, such as region growing (80%) and Euclidean clustering (75%). Furthermore, the extracted plant heights demonstrated a high correlation with manual measurements, with R2 values ranging from 0.8884 to 0.9989 and RMSE values as low as 0.0148 m. However, the scalability of the method for larger agricultural operations may face challenges due to computational demands when processing large-scale datasets and potential performance variability under different environmental conditions. Addressing these issues through algorithm optimization, parallel processing, and the integration of additional data sources such as multispectral or LiDAR data could enhance its scalability and robustness. The results demonstrate that the method can accurately reflect the heights of maize plants, providing a reliable solution for large-scale, field-based maize phenotyping. The method has potential applications in high-throughput monitoring of crop phenotypes and precision agriculture. Full article
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Figure 1

Figure 1
<p>Aerial views of the experimental maize test fields. (<b>a</b>) Test Plot 1: covers an area of approximately 0.3 hectares, and (<b>b</b>) Test Plot 2: covers an area of approximately 0.2 hectares. Images were captured on day 30 after maize emergence using UAV-based multi-view imaging.</p>
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<p>UAV planning diagram.</p>
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<p>The flowchart of corn single-plant height estimation based on column space segmentation.</p>
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<p>Point cloud preprocessing.</p>
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<p>Point cloud preprocessing.</p>
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<p>Flowchart of the extraction height.</p>
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<p>Algorithm segmentation effect.</p>
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<p>Clustering of different experimental monocultures.</p>
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<p>Histogram of estimated maize plant height using the point cloud extraction method for different sizes of wraparound boxes.</p>
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<p>Histogram of direction angle.</p>
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<p>Normal vector point cloud map.</p>
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<p>Example diagram of the column space algorithm.</p>
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<p>Box plot of estimated height data for each test.</p>
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<p>Histogram of estimated maize plant height data for five sets of tests using the point cloud extraction method. The heights for each test (<b>a</b>–<b>e</b>) were derived from the processed point cloud data generated by the multi-view reconstruction method.</p>
Full article ">Figure 13 Cont.
<p>Histogram of estimated maize plant height data for five sets of tests using the point cloud extraction method. The heights for each test (<b>a</b>–<b>e</b>) were derived from the processed point cloud data generated by the multi-view reconstruction method.</p>
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<p>Histogram of manually measured maize plant height data for five sets of tests. The heights for each test (<b>a</b>–<b>e</b>) were obtained through manual measurement, serving as a ground-truth comparison to the point cloud estimation.</p>
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<p>Comparative validation plot of plant height for the five experimental groups.</p>
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<p>Validation plot of plant height comparison for composite test groups.</p>
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20 pages, 4780 KiB  
Article
Large-Space Laser Tracking Attitude Combination Measurement Using Backpropagation Algorithm Based on Neighborhood Search
by Ziyue Zhao, Zhi Xiong, Zhengnan Guo, Hao Zhang, Xiangyu Li, Zhongsheng Zhai and Weihu Zhou
Appl. Sci. 2025, 15(3), 1083; https://doi.org/10.3390/app15031083 - 22 Jan 2025
Viewed by 338
Abstract
Large-space high-precision attitude dynamic measurement technology has urgent application needs in large equipment manufacturing fields, such as aerospace, rail transportation, automobiles, and ships. In this paper, taking laser tracking equipment as the base station, a backpropagation algorithm based on neighborhood search is proposed, [...] Read more.
Large-space high-precision attitude dynamic measurement technology has urgent application needs in large equipment manufacturing fields, such as aerospace, rail transportation, automobiles, and ships. In this paper, taking laser tracking equipment as the base station, a backpropagation algorithm based on neighborhood search is proposed, which is applied to the fusion of multi-source information for solving the dynamic attitude angle. This paper firstly established a mathematical model of laser tracking attitude dynamic measurement based on IMU and CCD multi-sensor, designed a 6-11-3 back propagation network structure and algorithm flow, and realized the prediction of attitude angle through model training. Secondly, the method based on neighborhood search realizes the determination of the optimal training target value of the model, of which the MSE has a 34% reduction compared to the IMU determination method. Finally, the experimental platform is set up with the precision rotary table as the motion carrier to verify the effectiveness of the research method in this paper. The experimental results show that with the neighborhood-based backpropagation algorithm, the measurement results have a higher data update rate and a certain inhibition effect on the error accumulation of IMU. The absolute value of the system angle error can be less than 0.4° within 8 m and 0–50°, with an angle update rate of 100 Hz. The research method in this paper can be applied to the dynamic measurement of laser tracking attitude angles, which provides a new reference for the angle measurement method based on the fusion of multi-source information. Full article
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<p>Composition of the laser tracking attitude dynamic measurement system.</p>
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<p>Fusion method of visual and inertial combined measurements.</p>
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<p>Training effect of different numbers of neurons.</p>
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<p>Backpropagation algorithm training structure model.</p>
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<p>Backpropagation-based network model training process flowchart.</p>
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<p>Regression function for the learning process.</p>
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<p>The smallest MSE for the learning process.</p>
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<p>Diagram error histogram for the learning process.</p>
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<p>Neighborhood search flowchart.</p>
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<p>Comparison of neighborhood search effects.</p>
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<p>Experimental verification platform of attitude dynamic measurement system based on rotary table.</p>
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<p>Comparison of fusion effect and single measurement unit results at 4 m.</p>
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<p>The comparative analysis between the results of IMU integrated solution and data fusion solution.</p>
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36 pages, 17912 KiB  
Review
Effects of Hypergravity on Phase Evolution, Synthesis, Structures, and Properties of Materials: A Review
by Yisheng Zheng, Lilin Xie, Yanhui Chen and Xiaodong Han
Materials 2025, 18(3), 496; https://doi.org/10.3390/ma18030496 - 22 Jan 2025
Viewed by 265
Abstract
In a hypergravity environment, the complex stress conditions and the change in gravity field intensity will significantly affect the interaction force inside solid- and liquid-phase materials. In particular, the driving force for the relative motion of the phase material, the interphase contact interaction, [...] Read more.
In a hypergravity environment, the complex stress conditions and the change in gravity field intensity will significantly affect the interaction force inside solid- and liquid-phase materials. In particular, the driving force for the relative motion of the phase material, the interphase contact interaction, and the stress gradient are enhanced, which creates a nonlinear effect on the movement mode of the phase material, resulting in a change in the material’s behavior. These changes include increased stress and contact interactions; accelerated phase separation; changes in stress distribution; shear force and phase interface renewal; enhanced interphase mass transfer and molecular mixing; and increased volume mass transfer and heat transfer coefficients. These phenomena have significant effects on the synthesis, structural evolution, and properties of materials in different phases. In this paper, the basic concepts of hypergravity and the general rules of the effects of hypergravity on the synthesis, microstructure evolution, and properties of materials are reviewed. Based on the development of hypergravity equipment and characterization methods, this review is expected to broaden the theoretical framework of material synthesis and mechanical property control under hypergravity. It provides theoretical reference for the development of high-performance materials under extreme conditions, as well as new insights and methods for research and application in related fields. Full article
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<p>Effects of hypergravity on the interaction forces of matters [<a href="#B1-materials-18-00496" class="html-bibr">1</a>]. (<b>a</b>) Increasing the force and gradient; (<b>b</b>) strengthening the relative motion between phases.</p>
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<p>Centrifugal hypergravity equipment. (<b>a</b>) Diagram of the rotating packed bed [<a href="#B38-materials-18-00496" class="html-bibr">38</a>]; (<b>b</b>) the beam centrifuge [<a href="#B1-materials-18-00496" class="html-bibr">1</a>]; (<b>c</b>) the centrifugal hypergravity apparatus [<a href="#B39-materials-18-00496" class="html-bibr">39</a>].</p>
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<p>Fluid morphology change in a rotating packed bed [<a href="#B55-materials-18-00496" class="html-bibr">55</a>]. (<b>a</b>) The three typical liquid forms; (<b>b</b>) morphological differences at different rotational speeds.</p>
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<p>The liquid flow in the MLI-RPB reactor with 3D-printed wire mesh packing was compared with CFD simulation and high-speed camera data (N = 600 rpm) [<a href="#B56-materials-18-00496" class="html-bibr">56</a>].</p>
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<p>Research on liquid flow behavior in the cavity region of a rotating packed bed [<a href="#B57-materials-18-00496" class="html-bibr">57</a>]. (<b>a</b>) Droplet morphological changes in the cavity region at different rotational speeds; (<b>b</b>) liquid distribution; (<b>c</b>) images of liquid morphological changes. From left to right: The first and the second, the liquid membrane is gradually decomposed into droplets, the third; the droplets are separated from the ligament, and the fourth: the mother droplets are separated to form consecutive droplets.</p>
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<p>Synthesis of alumina zeolite by rotating packed bed in hypergravity. The red arrow shows the direction of sodium-induced nucleation [<a href="#B63-materials-18-00496" class="html-bibr">63</a>].</p>
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<p>TEM image and particle size distribution of BaSO<sub>4</sub> nanoparticles prepared at different rotational speeds. (<b>a</b>) The stirred tank reactor uses a rotational speed of 500 r/min (<b>b</b>) RPB uses a rotational speed of 500 r/min (<b>c</b>) RPBuses a rotational speed of 1500 r/min (<b>d</b>) RPB uses a rotational speed of 2500 r/min (<b>e</b>) Particle size distribution [<a href="#B64-materials-18-00496" class="html-bibr">64</a>].</p>
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<p>The growth of SiO<sub>2</sub> inclusions in 304 stainless steel after treatment for 1 min under different gravity conditions [<a href="#B70-materials-18-00496" class="html-bibr">70</a>]. (<b>a</b>) Volume fraction; (<b>b</b>) density distribution.</p>
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<p>Hypergravity-enhanced separation for the removal of inclusions from Inconel 718 superalloys [<a href="#B73-materials-18-00496" class="html-bibr">73</a>]. (<b>a</b>) Macroscopic and microscopic imaging of samples before and after treatment under hypergravity; (<b>b</b>) density and particle size distribution of inclusions after treatment of Inconel 718 superalloys under different hypergravity conditions (A: The central area of the ingot under normal gravity; B–F: Different areas of sample cross section obtained by hypergravity facilitation separation).</p>
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<p>Hypergravity separation of valuable components in mixed metals [<a href="#B39-materials-18-00496" class="html-bibr">39</a>,<a href="#B74-materials-18-00496" class="html-bibr">74</a>]. (<b>a</b>) Schematic diagram of filtration mechanism of hypergravity technology (<b>a1</b>): The initial phase of random distribution of antimony phase,(<b>a2</b>): Hypergravity is applied for separation, (<b>a3</b>): The phase with small particle size is separated to the bottom, black arrow: direction of phase migration); (<b>b</b>) Hypergravity technology used for fractional separation and recovery of Pb, Sn, Zn, and Cu.</p>
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<p>WPCB metal recovery process combining the pyrolysis process and hypergravity technology separation [<a href="#B75-materials-18-00496" class="html-bibr">75</a>].</p>
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<p>Distribution and SEM of the precipitated phase in GH4169 alloy under diverse gravity conditions and temperature treatments [<a href="#B112-materials-18-00496" class="html-bibr">112</a>]. (<b>a</b>) Statistics of the δ phase area fraction; (<b>b</b>) SEM images of the rod-shaped δ at 720 °C, 24 h, 1 G; (<b>c</b>) SEM images showing no δ at 720 °C, 24 h, 50,000 G; (<b>d</b>) SEM images of the needle-like δ at 800 °C, 24 h, 1 G; (<b>e</b>) SEM images of the needle-like δ at 800 °C, 24 h, 50,000 G; (<b>f</b>) SEM images of the needle-like δ at 850 °C, 24 h, 1 G; (<b>g</b>) SEM images of the needle-like δ at 850 °C, 24 h, 50,000 G.</p>
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<p>Effect of hypergravity coefficient on solidified grains of different metals [<a href="#B113-materials-18-00496" class="html-bibr">113</a>,<a href="#B114-materials-18-00496" class="html-bibr">114</a>]. (<b>a</b>) Pure aluminum; (<b>b</b>) Cu-11%Sn alloy.</p>
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<p>Microstructure of Al-8 wt% Cu alloy after hypergravity treatment with G = 300 at different solidification stages [<a href="#B116-materials-18-00496" class="html-bibr">116</a>]. (<b>a</b>) Starting from high-temperature liquid and ending with a solid phase fraction of 20%; (<b>b</b>) Starting with a solid phase fraction of 20% and ending with a solid phase fraction of 50%; (<b>c</b>) Starting with a solid phase fraction of 50% and ending with a solid phase fraction of 80%; (<b>d</b>) Starting with a solid phase fraction of 80% and ending with a solid phase fraction of 100%; (<b>e</b>) The complete solidification stage, where the red wireframe represents the fine crystal zone (Red dotted line: the fine grain zone of each sample).</p>
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<p>Evolution of dendrite structure at solid–liquid interface under different gravity conditions. Red dashed circle: broken dendrites [<a href="#B121-materials-18-00496" class="html-bibr">121</a>].</p>
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<p>Mechanism of grain refinement of 7050 aluminum alloy under hypergravity (<b>a</b>) Dendrite fragmentation. (<b>b</b>) Dense primary crystals settle to the bottom (blue arrow: hypergravity causes the dendrite root to break; dashed arrow: broken dendrites begin to form primary crystals; green arrow: direction of settling of primary crystal) [<a href="#B122-materials-18-00496" class="html-bibr">122</a>].</p>
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<p>The growth process of columnar crystals under different gravity conditions. A indicates the primary dendrites; the direction of gravity vector is the same as that of main dendrite growth under positive gravity and the opposite direction under negative gravity; blue is the primary dendritic phase, and green or red is the copper-rich liquid; the scale is 100 μm [<a href="#B126-materials-18-00496" class="html-bibr">126</a>].</p>
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<p>Effect of hypergravity on dendrite growth [<a href="#B122-materials-18-00496" class="html-bibr">122</a>]. (<b>a</b>) Dendrite structure at different locations; (<b>b</b>) the typical element distribution and microstructure of the second phase of Al7050 alloy at 1023 K (<b>b1</b>): 1 g—region I, (<b>b2</b>): 3000 g—region II, (<b>b3</b>): 3000 g—region IV. I–IV represent different regions). (<b>c</b>) area fractions of the second phases of different structures, B and T represent the bottom and top regions, respectively.</p>
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<p>The gradient structure was prepared under hypergravity [<a href="#B134-materials-18-00496" class="html-bibr">134</a>]. (<b>a</b>) Samples after hypergravity treatment; (<b>b</b>) low-magnification and (<b>c</b>) high-magnification SEM and EDS analysis for the AlZn<sub>0.4</sub>Li<sub>0.2</sub>Mg<sub>0.2</sub>Cu<sub>0.2</sub>.</p>
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<p>Gradient structure in Se-Te alloy after hypergravity treatment. a–b solid red line indicates the direction of component detection [<a href="#B144-materials-18-00496" class="html-bibr">144</a>].</p>
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<p>Effect of high-temperature hypergravity on copper–brass diffusion. (<b>a</b>) Centrifuge-modified high-temperature hypergravity environment equipment [<a href="#B145-materials-18-00496" class="html-bibr">145</a>]; (<b>b</b>) copper–brass diffusion couple treated with high-temperature hypergravity (400 °C, 400,000× <span class="html-italic">g</span>); (<b>c</b>) approximate vacancy concentration distribution in copper–brass diffusion couple after high-temperature and hypergravity treatment. Points A and B represent the top and bottom of the sample [<a href="#B146-materials-18-00496" class="html-bibr">146</a>].</p>
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<p>Backscattering SEM images of the sample interface at a hypergravity level of 870, 2260, 3500, and 4700× <span class="html-italic">g</span>, with diffusion at 350 °C for 6 h [<a href="#B147-materials-18-00496" class="html-bibr">147</a>]. (<b>a</b>–<b>d</b>) G+ sample, the hypergravity direction is Cu to Zn; (<b>e</b>–<b>h</b>) G- sample, the hypergravity direction is Zn to Cu. The yellow arrows indicates the defects (voids), the red arrows indicate the ε phase diffusion layer, and the black arrows indicate the β phase (not obvious).</p>
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<p>SEM and EDS maps and IMC layer thicknesses of CuSn diffusion couples treated at 210 °C for 100 h under normal/hypergravity. G represents the direction of hypergravity [<a href="#B149-materials-18-00496" class="html-bibr">149</a>].</p>
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<p>Atomic structure of β phase (CuZn) under different gravity conditions, HRTEM images, and corresponding superimposed images after filtering. (<b>a</b>,<b>b</b>) HRTEM filtered images and corresponding overlapping images of the corresponding variable distribution of β under 870 <span class="html-italic">g</span> hypergravity. (<b>c</b>,<b>d</b>) HRTEM filtered images and corresponding overlapping images of the corresponding variable distribution of β under 3500 <span class="html-italic">g</span> hypergravity. (<b>e</b>,<b>f</b>) HRTEM filtered images and corresponding overlapping images of the corresponding variable distribution of β under 4700 <span class="html-italic">g</span> hypergravity. The yellow marks indicate the presence of dislocations, and the black arrows indicate the presence of strain but no dislocation. The atomic strain distribution was obtained by applying the geometric phase analysis method for TEM images [<a href="#B147-materials-18-00496" class="html-bibr">147</a>].</p>
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<p>Structural evolution of turbine blades under high temperature and a hypergravity field [<a href="#B153-materials-18-00496" class="html-bibr">153</a>]. (<b>a</b>) Schematic; (<b>b</b>) different position points. 1–13 represent a severely degraded portion from tip to stalk near the trailing edge of the blade.</p>
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<p>Fracture failure behavior of 1060 aluminum alloy under different hypergravity conditions [<a href="#B154-materials-18-00496" class="html-bibr">154</a>]. (<b>a</b>) The stress of the specimen; (<b>b</b>) difference in crack limit tension under constant stress and hypergravity gradient stress; (<b>c</b>) SEM fracture morphology under different hypergravity magnitude and temperature conditions.</p>
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<p>Fracture failure behavior of 7075 aluminum alloy under different hypergravity conditions [<a href="#B155-materials-18-00496" class="html-bibr">155</a>]. (<b>a</b>) Comparison of crack tips at different gravity levels; (<b>b</b>) EBSD analysis of different crack propagation locations (IPF diagram, GND diagram and dislocation frequency distribution of the location in the dotted box, red dotted box is the upper edge of the sample, blue dotted box is the lower edge of the sample); (<b>c</b>) Schematic diagram of crack patterns under hypergravity/conventional uniaxial stress (the dotted lines are in the direction of torsion).</p>
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34 pages, 8852 KiB  
Article
A Biologically Inspired Model for Detecting Object Motion Direction in Stereoscopic Vision
by Yuxiao Hua, Sichen Tao, Yuki Todo, Tianqi Chen, Zhiyu Qiu and Zheng Tang
Symmetry 2025, 17(2), 162; https://doi.org/10.3390/sym17020162 - 22 Jan 2025
Viewed by 289
Abstract
This paper presents a biologically inspired model, the Stereoscopic Direction Detection Mechanism (SDDM), designed to detect motion direction in three-dimensional space. The model addresses two key challenges: the lack of biological interpretability in current deep learning models and the limited exploration of binocular [...] Read more.
This paper presents a biologically inspired model, the Stereoscopic Direction Detection Mechanism (SDDM), designed to detect motion direction in three-dimensional space. The model addresses two key challenges: the lack of biological interpretability in current deep learning models and the limited exploration of binocular functionality in existing biologically inspired models. Rooted in the fundamental concept of ’disparity’, the SDDM is structurally divided into components representing the left and right eyes. Each component mimics the layered architecture of the human visual system, from the retinal layer to the primary visual cortex. By replicating the functions of various cells involved in stereoscopic motion direction detection, the SDDM offers enhanced biological plausibility and interpretability. Extensive experiments were conducted to evaluate the model’s detection accuracy for various objects and its robustness against different types of noise. Additionally, to ascertain whether the SDDM matches the performance of established deep learning models in the field of three-dimensional motion direction detection, its performance was benchmarked against EfficientNet and ResNet under identical conditions. The results demonstrate that the SDDM not only exhibits strong performance and robust biological interpretability but also requires significantly lower hardware and time costs compared to advanced deep learning models. Full article
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<p>Overall model structure.</p>
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<p>Photoreception mechanism.</p>
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<p>Photoreceptor cells.</p>
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<p>Horizontal cells.</p>
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<p>Bipolar cells.</p>
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<p>Direction-selective ganglion cells.</p>
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<p>Left receptive field of direction-selective ganglion cells.</p>
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<p>Right receptive field of direction-selective ganglion cells.</p>
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<p>Voxels of 3D objects.</p>
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<p>Direction detection and disparity detection.</p>
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<p>Dataset B.</p>
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<p>Dataset B with static background noise.</p>
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<p>Dataset B with dynamic background noise.</p>
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<p>Dataset B with static global noise.</p>
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<p>Dataset B with dynamic global noise.</p>
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<p>The structure of EfficientNetB0.</p>
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<p>The structure of ResNet34.</p>
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<p>Final results of static background noise.</p>
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<p>Final results of dynamic background noise.</p>
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<p>Final results of static global noise.</p>
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<p>Final results of dynamic global noise.</p>
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8 pages, 1934 KiB  
Proceeding Paper
A Simulation Method for Fluid–Solid Coupling in the Flexible Wings of MAVs Based on the LBM
by Liansong Peng and Chen Wang
Eng. Proc. 2024, 80(1), 26; https://doi.org/10.3390/engproc2024080026 - 22 Jan 2025
Viewed by 212
Abstract
In this paper, a fast and accurate simulation method for the large deformation motion of anisotropic complex models is proposed. By establishing a fluid–structure interaction (FSI) coupling model based on the Lattice Boltzmann Method (LBM) and the Central Difference Method, the effect of [...] Read more.
In this paper, a fast and accurate simulation method for the large deformation motion of anisotropic complex models is proposed. By establishing a fluid–structure interaction (FSI) coupling model based on the Lattice Boltzmann Method (LBM) and the Central Difference Method, the effect of flexible deformation on the aerodynamic performance of anisotropic wings during flapping is analyzed. The method can provide theoretical guidance and data support for the fluid–solid coupling study and the aerodynamic optimization of Micro Aerial Vehicles (MAVs). Full article
(This article belongs to the Proceedings of 2nd International Conference on Green Aviation (ICGA 2024))
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<p>Co-simulation process flow.</p>
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<p>Verification against Wang’s dataset for a 2D dragonfly in hover mode: (<b>a</b>) force coefficient; (<b>b</b>) vortex contour.</p>
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<p>The building process of the finite element model.</p>
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<p>Analysis of the temporal variations in lift coefficients for both flexible and rigid wings.</p>
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<p>The geometric comparison of FW and RW. (<b>a</b>) The geometrical differences between the FW and RW during downstroke. (<b>b</b>) The spanwise two-dimensional flow fields at T = 0.25.</p>
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18 pages, 32492 KiB  
Article
Fabrication and Optimization of Additively Manufactured Hybrid Nanogenerators for Wearable Devices
by Khaled A. Eltoukhy, Mohamed Fawzy Aly, Marc Sarquella, Concepción Langreo and Mohamed Serry
Nanomaterials 2025, 15(3), 159; https://doi.org/10.3390/nano15030159 - 21 Jan 2025
Viewed by 414
Abstract
This paper aims to fabricate a hybrid piezoelectric/triboelectric nanogenerator via fusion deposition modeling as a proof of concept in the wearable device industry. The nanogenerator structure consists of a TPU/ZnO nanocomposite and an Ecoflex layer. The nanocomposite layer is fabricated using two different [...] Read more.
This paper aims to fabricate a hybrid piezoelectric/triboelectric nanogenerator via fusion deposition modeling as a proof of concept in the wearable device industry. The nanogenerator structure consists of a TPU/ZnO nanocomposite and an Ecoflex layer. The nanocomposite layer is fabricated using two different weight percentages (15 wt% and 20 wt%) and poled piezoelectric sheets, generating 2.63 V to 3.46 V. Variations regarding the nanogenerator’s physical parameters were implemented to examine the effect on nanogenerator performance under different frequencies. The hybrid nanogenerator enabled energy harvesting for wearable devices. It was strapped on the side of the wrist to generate a potential difference with the motion of the wrist, creating a contact separation piezoelectric/triboelectric nanogenerator. Furthermore, a piezoelectric sheet was placed at the bottom of the wrist to harvest energy. The hybrid nanogenerator provided a maximum triboelectric response of 5.75 V and a maximum piezoelectric response of 2.85 V during wrist motion. The piezoelectric nanogenerator placed at the bottom of the wrist generated up to 4.78 V per wrist motion. Full article
(This article belongs to the Special Issue Application of Nanogenerators in Nanoelectronics)
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<p>Graphical abstract of the fabrication of wearable device.</p>
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<p>Schematic of the ZnO/TPU fabrication process.</p>
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<p>Schematic of the nanogenerator fabrication process and characterization.</p>
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<p>FT-IR spectra of pure TPU, 15% ZnO/TPU, and 20% ZnO/TPU.</p>
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<p>SEM image of pure TPU.</p>
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<p>SEM image of 15 wt% Zno/TPU nanocomposite.</p>
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<p>SEM image of 20 wt% Zno/TPU nanocompsite.</p>
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<p>XRD patterns pre-corona and post-corona treatment.</p>
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<p>Force voltage characterization of 3D-printed piezoelectric test sheets.</p>
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<p>3D-printed sheets used for PENG characterization: (<b>a</b>) 15 wt% Zno/TPU; (<b>b</b>) 20 wt% ZnO/TPU sheet.</p>
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<p>Schematic of the three-electrode system.</p>
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<p>Testing of the PTENG performance.</p>
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<p>PTENG performances of the 20 cm<sup>2</sup> generator under (<b>a</b>) 2 Hz and (<b>b</b>) 4 Hz loading, the 25 cm<sup>2</sup> generator under 2 Hz (<b>a</b>) and 4 Hz (<b>b</b>) loading, and the 25 cm<sup>2</sup> generator under (<b>c</b>) 2 Hz and (<b>d</b>) 4 Hz loading.</p>
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<p>Schematic of PENG and PTENG placement during testing.</p>
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<p>Testing of (<b>a</b>) PENG and (<b>b</b>) PTENG responses.</p>
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<p>Open-circuit voltage recorded during testing.</p>
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18 pages, 7358 KiB  
Article
Multi-Point Optical Flow Cable Force Measurement Method Based on Euler Motion Magnification
by Jinzhi Wu, Bingyi Yan, Yu Xue, Jie Qin, Deqing You and Guojun Sun
Buildings 2025, 15(3), 311; https://doi.org/10.3390/buildings15030311 - 21 Jan 2025
Viewed by 293
Abstract
This study introduces a multi-point optical flow cable force measurement method based on Euler motion amplification to address challenges in accurately measuring cable displacement under small displacement conditions and mitigating background interference in complex environments. The proposed method combines phase-based magnification with an [...] Read more.
This study introduces a multi-point optical flow cable force measurement method based on Euler motion amplification to address challenges in accurately measuring cable displacement under small displacement conditions and mitigating background interference in complex environments. The proposed method combines phase-based magnification with an optical flow method to enhance small displacement features and improve SNR (signal-to-noise ratio) in cable displacement tracking. By leveraging magnified motion data and integrating auxiliary feature points, the approach compensates for equipment-induced vibrations and background noise, allowing for precise cable displacement measurement and the identification of vibration modes. The methodology was validated using a scaled model of a cable net structure. The results demonstrate the method’s effectiveness, achieving a significantly higher SNR (e.g., from 7.5 dB to 22.24 dB) compared to traditional optical flow techniques. Vibration frequency errors were reduced from 6.2% to 1.5%, and cable force errors decreased from 11.38% to 3.13%. The multi-point optical flow cable force measurement method based on Euler motion magnification provides a practical and reliable solution for non-contact cable force measurement, offering potential applications in structural health monitoring and the maintenance of bridges and high-altitude structures. Full article
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<p>Flowchart of multi−point optical flow cable force measurement method based on Euler motion magnification.</p>
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<p>Schematic diagram of the scale model.</p>
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<p>Structural component diagram.</p>
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<p>Schematic diagram of the inclined cable position.</p>
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<p>Schematic diagram of the acceleration sensor position.</p>
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<p>Selection of feature points of the traditional optical flow method.</p>
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<p>Characteristic point selection of the multi-point optical flow method.</p>
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<p>Comparison of SNR at different magnifications.</p>
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<p>Comparison of the oblique cable displacement response curve. (<b>a</b>) XS-1-P<sub>1</sub> comparison of the node time shift curve; (<b>b</b>) XS-2-P<sub>1</sub> comparison of the node time shift curve; (<b>c</b>) XS-3-P<sub>1</sub> comparison of the node time shift curve; (<b>d</b>) XS-4-P<sub>1</sub> comparison of the node time shift curve.</p>
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<p>The cable spectrum data are collected by high-speed camera. (<b>a</b>) Comparison of the Spectrum Diagram for Node XS1-P<sub>1</sub>; (<b>b</b>) comparison of the Spectrum Diagram for Node XS2-P<sub>1</sub>; (<b>c</b>) comparison of the Spectrum Diagram for Node XS3-P<sub>1</sub>; (<b>d</b>) comparison of the Spectrum Diagram for Node XS4-P<sub>1</sub>.</p>
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<p>Frequency error comparison diagram.</p>
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<p>Cable vibration mode shape. (<b>a</b>) XS-1 vibration mode shape; (<b>b</b>) XS-2 vibration mode shape; (<b>c</b>) XS-3 vibration mode shape; (<b>d</b>) XS-4 vibration mode shape.</p>
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<p>Cable force comparison diagram.</p>
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<p>Cable force error comparison diagram.</p>
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19 pages, 8391 KiB  
Article
NeuroFlex: Feasibility of EEG-Based Motor Imagery Control of a Soft Glove for Hand Rehabilitation
by Soroush Zare, Sameh I. Beaber and Ye Sun
Sensors 2025, 25(3), 610; https://doi.org/10.3390/s25030610 - 21 Jan 2025
Viewed by 914
Abstract
Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person’s ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to [...] Read more.
Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person’s ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to functionally and structurally reorganize itself in response to stimulation, which underpins rehabilitation from brain injuries or strokes. Linking voluntary cortical activity with corresponding motor execution has been identified as effective in promoting adaptive plasticity. This study introduces NeuroFlex, a motion-intent-controlled soft robotic glove for hand rehabilitation. NeuroFlex utilizes a transformer-based deep learning (DL) architecture to decode motion intent from motor imagery (MI) EEG data and translate it into control inputs for the assistive glove. The glove’s soft, lightweight, and flexible design enables users to perform rehabilitation exercises involving fist formation and grasping movements, aligning with natural hand functions for fine motor practices. The results show that the accuracy of decoding the intent of fingers making a fist from MI EEG can reach up to 85.3%, with an average AUC of 0.88. NeuroFlex demonstrates the feasibility of detecting and assisting the patient’s attempted movements using pure thinking through a non-intrusive brain–computer interface (BCI). This EEG-based soft glove aims to enhance the effectiveness and user experience of rehabilitation protocols, providing the possibility of extending therapeutic opportunities outside clinical settings. Full article
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<p>The experimental protocol for EEG data collection consisted of three phases: motor execution, motor imagery, and rest, each lasting 16 s.</p>
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<p>Raw EEG data during glove MI after bandpass filtering (0.5–45 Hz) and notch filtering (60 Hz), segmented into overlapping 1-second epochs with a 0.75-second overlap.</p>
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<p>Overview of EEG signal processing using a transformer architecture.</p>
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<p>Design and conceptual function of the soft fingers in this study.</p>
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<p>Assembly of the full glove from the CAD software, including all the parts used for the actuation process.</p>
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<p>Comparison of the top plane of the full glove for the (<b>a</b>) glove assembly CAD model and (<b>b</b>) the actual glove design.</p>
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<p>Different views and comparison of the full glove: (<b>a</b>) Isometric view of the CAD model. (<b>b</b>) Isometric view of the actual design. (<b>c</b>) Back view of the actual side.</p>
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<p>Schematic diagram and optimized control process for the rehabilitation loop and the rest conditions.</p>
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<p>EEG band power for Subject ID 1 across frequency bands.</p>
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<p>Confusion matrices for participants.</p>
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<p>ROC curves for participants.</p>
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19 pages, 5395 KiB  
Article
Optimizing 3D Point Cloud Reconstruction Through Integrating Deep Learning and Clustering Models
by Seyyedbehrad Emadi and Marco Limongiello
Electronics 2025, 14(2), 399; https://doi.org/10.3390/electronics14020399 - 20 Jan 2025
Viewed by 361
Abstract
Noise in 3D photogrammetric point clouds—both close-range and UAV-generated—poses a significant challenge to the accuracy and usability of digital models. This study presents a novel deep learning-based approach to improve the quality of point clouds by addressing this issue. We propose a two-step [...] Read more.
Noise in 3D photogrammetric point clouds—both close-range and UAV-generated—poses a significant challenge to the accuracy and usability of digital models. This study presents a novel deep learning-based approach to improve the quality of point clouds by addressing this issue. We propose a two-step methodology: first, a variational autoencoder reduces features, followed by clustering models to assess and mitigate noise in the point clouds. This study evaluates four clustering methods—k-means, agglomerative clustering, Spectral clustering, and Gaussian mixture model—based on photogrammetric parameters, reprojection error, projection accuracy, angles of intersection, distance, and the number of cameras used in tie point calculations. The approach is validated using point cloud data from the Temple of Neptune in Paestum, Italy. The results show that the proposed method significantly improves 3D reconstruction quality, with k-means outperforming other clustering techniques based on three evaluation metrics. This method offers superior versatility and performance compared to traditional and machine learning techniques, demonstrating its potential to enhance UAV-based surveying and inspection practices. Full article
(This article belongs to the Special Issue Point Cloud Data Processing and Applications)
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<p>Illustration of the reprojection error.</p>
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<p>Illustration of the angle of intersection.</p>
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<p>Conceptual illustration of the proposed methodology, highlighting the main steps involved in optimizing point cloud data using deep learning clustering models.</p>
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<p>Application example: Temple of Neptune in Paestum (Italy).</p>
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<p>A selected section of the Temple of Neptune.</p>
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<p>Visualizations of point cloud data under different single-parameter noise reduction analyses: (<b>a</b>) reprojection errors, (<b>b</b>) average intersection angles, (<b>c</b>) number of images, and (<b>d</b>) projection accuracy.</p>
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<p>Distribution of clusters generated by: (<b>a</b>) GMM clustering algorithms, (<b>b</b>) k-means clustering algorithms, (<b>c</b>) agglomerative clustering algorithms, and (<b>d</b>) Spectral clustering algorithms.</p>
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20 pages, 4883 KiB  
Article
Study on the Bubble Collapse Characteristics and Heat Transfer Mechanism of the Microchannel Reactor
by Gaoan Zheng, Pu Xu, Tong Wang and Qing Yan
Processes 2025, 13(1), 281; https://doi.org/10.3390/pr13010281 - 20 Jan 2025
Viewed by 391
Abstract
Microreactors have the advantages of high heat and mass transfer efficiency, strict control of reaction parameters, easy amplification, and good safety performance, and have been widely used in various fields such as chip manufacturing, fine chemicals, and biomanufacturing. However, narrow microchannels in microreactors [...] Read more.
Microreactors have the advantages of high heat and mass transfer efficiency, strict control of reaction parameters, easy amplification, and good safety performance, and have been widely used in various fields such as chip manufacturing, fine chemicals, and biomanufacturing. However, narrow microchannels in microreactors often become filled with catalyst particles, leading to blockages. To address this challenge, this study proposes a multiphase flow heat transfer model based on the lattice Boltzmann method (LBM) to investigate the dynamic changes during the bubble collapse process and temperature distribution regularities. Based on the developed three-phase flow dynamics model, this study delves into the shock dynamic evolution process of bubble collapse and analyzes the temperature distribution regularities. Then, the flow patterns under different particle density conditions are explored. The study found that under the action of shock wave, the stable structure of the liquid film of the bubble is destroyed, and the bubble deforms and collapses. At the moment of bubble collapse, energy is rapidly transferred from the potential energy of the bubble to the kinetic energy of the flow field. Subsequently, the kinetic energy is converted into pressure waves. This results in the rapid generation of extremely high pressure in the flow field, creating high-velocity jets and intense turbulent vortices, which can enhance the mass transfer effects of the multiphase flows. At the moment of bubble collapse, a certain high temperature phenomenon will be formed at the collapse, and the high temperature phenomenon in this region is relatively chaotic and random. The pressure waves generated during bubble collapse have a significant impact on the motion trajectories of particles, while the influence on high-density particles is relatively small. The results offer a theoretical basis for understanding mass transfer mechanisms and particle flow patterns in three-phase flow. Moreover, these findings have significant practical implications for advancing technologies in industrial applications, including chip manufacturing and chemical process transport. Full article
(This article belongs to the Section Chemical Processes and Systems)
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<p>Three-dimensional physical model of the microchannel.</p>
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<p>Pressure contour and pressure curve during bubble collapse. (<b>a</b>) Pressure contour distribution during bubble collapse. (<b>b</b>) Time-varying pressure curve at the bubble collapse point.</p>
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<p>Velocity contour and velocity curve during bubble collapse. (<b>a</b>) Velocity contour distribution during bubble collapse. (<b>b</b>) Time-varying velocity curve at the bubble collapse point.</p>
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<p>Turbulence intensity analysis during bubble collapse. (<b>a</b>) Turbulence intensity distribution. (<b>b</b>) Turbulence intensity curve at the collapse point.</p>
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<p>Vorticity distribution and vorticity curve during bubble collapse in the flow field. (<b>a</b>) Vorticity distribution. (<b>b</b>) Time-varying vorticity curve.</p>
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<p>Temperature distribution at observation points in the flow field. (<b>a</b>) Observation point 1. (<b>b</b>) Observation point 2. (<b>c</b>) Observation point 3. (<b>d</b>) Observation point 4.</p>
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<p>Temperature evolution contour of the flow field before and after bubble collapse. (<b>a</b>) Before bubble collapse. (<b>b</b>) After bubble collapse.</p>
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<p>Dynamic process of bubble collapse in the flow field. (<b>a</b>) <span class="html-italic">t</span> = 0 ms. (<b>b</b>) <span class="html-italic">t</span> = 2.7 ms. (<b>c</b>) <span class="html-italic">t</span> = 3.1 ms. (<b>d</b>) <span class="html-italic">t</span> = 3.38 ms.</p>
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<p>Motion trajectories of particles affected by bubble collapse.</p>
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<p>Variations in particle velocity in the flow field. (<b>a</b>) Changes in particle velocity in the <span class="html-italic">X</span> direction. (<b>b</b>) Changes in particle velocity in the <span class="html-italic">Y</span> direction.</p>
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