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Search Results (1,812)

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16 pages, 2870 KiB  
Article
A Construction Method for the Random Factor-Based G Function
by Yongxin Feng, Jiankai Su and Bo Qian
Appl. Sci. 2024, 14(22), 10478; https://doi.org/10.3390/app142210478 - 14 Nov 2024
Viewed by 181
Abstract
In consideration of the prevailing methodology for constructing G functions, there are certain limitations such as fixed change rules and restricted flexibility when producing frequency-hopping sequences. This paper introduces a novel construction method for the Random Factor-based G function (RFGF). This approach incorporates [...] Read more.
In consideration of the prevailing methodology for constructing G functions, there are certain limitations such as fixed change rules and restricted flexibility when producing frequency-hopping sequences. This paper introduces a novel construction method for the Random Factor-based G function (RFGF). This approach incorporates random factors to dynamically divide the frequency set into equal intervals and randomly selects the frequency hopping frequency within each subset. This effectively reduces the correlation between adjacent frequency-hopping frequencies, enhancing the randomness of the sequence and the system’s anti-interference performance. Furthermore, this method utilizes chaotic sequences to scramble data information, further strengthening the security of the information. The experimental results demonstrate that the frequency-hopping sequence generated by this proposed G function construction method outperforms the sequence generated by the time-varying iterative decomposition in terms of randomness, uniformity, and two-dimensional continuity. Specifically, under the same parameter conditions, the two-dimensional continuity is improved by 36.87%. Full article
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<p>Frequency transfer relationship.</p>
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<p>Frequency state transition grid diagram.</p>
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<p>Construction diagram of AES algorithm.</p>
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<p>Schematic diagram of iterative decomposition.</p>
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<p>Structure diagram of the RFGF.</p>
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<p>The inverse G function algorithm.</p>
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<p>Frequency sequence power spectrum.</p>
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<p>Frequency statistics results of the equal distribution test.</p>
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<p>Frequency statistics histogram of two-dimensional continuity test by four methods.</p>
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<p>Single-frequency path statistics.</p>
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13 pages, 5339 KiB  
Article
Three-Dimensional Upper Airway Analysis of Different Craniofacial Skeletal Patterns in Vietnamese Adults
by Trang Thi Thu Vu, Mohamed Bayome, Anh Dinh Viet Vu and Phuong Thi Thu Nguyen
Appl. Sci. 2024, 14(22), 10477; https://doi.org/10.3390/app142210477 - 14 Nov 2024
Viewed by 250
Abstract
Introduction: This study aimed to investigate differences in the three-dimensional (3D) upper airway dimensions in Vietnamese participants. Methods: This study included 341 Vietnamese participants grouped based on the vertical growth pattern (ANB angle) (skeletal Class I, 123; Class II, 124; Class III, 94). [...] Read more.
Introduction: This study aimed to investigate differences in the three-dimensional (3D) upper airway dimensions in Vietnamese participants. Methods: This study included 341 Vietnamese participants grouped based on the vertical growth pattern (ANB angle) (skeletal Class I, 123; Class II, 124; Class III, 94). The patients were categorized into subgroups based on the horizontal growth pattern according to the Frankfort mandibular angle (hypodivergent, 35; normodivergent, 175; hyperdivergent, 131) to compare the frequency distribution of the three growth patterns in each skeletal class. The airway dimensions of the three skeletal classes were divided into four volumes using 3D virtual software (In VivoDental Software 6.0). The height, width, and cross-sectional area (CSA) of each part, as well as the total volume and minimum CSA, were measured and analyzed. Results: The airway space was reduced in hyperdivergent Class II individuals, underscoring an important connection between upper airway dimensions and vertical skeletal patterns, which suggests that vertical growth patterns contribute to pharyngeal narrowing and subsequent upper airway obstruction. Significant differences (p < 0.001) in the minimum CSAs and volumes of the middle and inferior pharyngeal airways were observed based on Angle’s skeletal classification. Conclusions: Our insights are valuable for orthodontics, especially in diverse populations, such as the Vietnamese, due to differences in the influence of genetic and environmental factors on skeletal and airway characteristics. Full article
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<p>The right porion, the left located in the most laterosuperior points of the external auditory meatus, and the right orbitals defined the standard Frankfort horizontal plane. A frontal plane was constructed through origin points.</p>
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<p>Landmarks, anteroposterior measurements, and vertical measurements were used in this study.</p>
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<p>Nine subgroups, combined with anteroposterior and vertical craniofacial groups.</p>
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<p>Four cross-sectional planes of the pharyngeal airway. Pna plane: frontal plane perpendicular to the FH plane passing through the PNS; Uph plane: axial plane parallel to the FH plane passing through the PNS; Mph plane: axial plane parallel to the FH plane passing through the caudal margin of the soft palate; Lph plane: axial plane parallel to the FH plane passing through the superior margin of the epiglottis; abbreviations: PNS, posterior nasal plane; FH, Pna, Uph, Mph, Lph.</p>
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<p>Volume, cross-sectional area, width, and height of the designated airway calculated in mm<sup>3</sup>, cc, and mm, respectively. Abbreviations: AP: Anteroposterior, RL: Right Left.</p>
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13 pages, 539 KiB  
Article
Strength Training Among Male Master Cyclists—Practices, Challenges, and Rationales
by Vidar Vikestad and Terje Dalen
J. Funct. Morphol. Kinesiol. 2024, 9(4), 232; https://doi.org/10.3390/jfmk9040232 - 12 Nov 2024
Viewed by 397
Abstract
Background: Cycling performance declines with age due to reduced aerobic capacity, along with reductions in muscle mass and bone density. Strength training can help counter these effects. This study aims to explore the strength training practices, challenges, and decision-making rationale of male master [...] Read more.
Background: Cycling performance declines with age due to reduced aerobic capacity, along with reductions in muscle mass and bone density. Strength training can help counter these effects. This study aims to explore the strength training practices, challenges, and decision-making rationale of male master cyclists to optimize performance and health as they age. Methods: A total of 555 male master cyclists aged 35 and above completed an online questionnaire, distributed via social media platforms, that included Likert-type, single- and multiple-selection, and open-ended questions. Participants were then divided into two age groups: 35–49 years (n = 359) and ≥50 years (n = 196). Analyses involved descriptive statistics, Wilcoxon signed-rank tests, Mann–Whitney U-tests, and chi-square tests, with qualitative data analyzed using content analysis. Results: More cyclists engaged in strength training during the off-/pre-season, with a significant reduction in both frequency and the number of cyclists engaging in strength training during the race season. The strength training practice was focused mainly on core and lower body, employing hypertrophy and maximal strength training methods. Key challenges included fatigue induced by strength training and limited time to perform strength training. The main rationale for the strength training revolved around improving cycling performance, reducing injury risk, and the health benefits of strength training. Both age categories, but the older group in particular, reported bone health as a primary rationale for strength training. Conclusions: While strength training offers performance and health benefits, issues of fatigue and time constraints remain substantial, suggesting the need for tailored training programs to improve adherence and effectiveness. Full article
(This article belongs to the Section Physical Exercise for Health Promotion)
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<p>Responses to the question: “What duration of effort do you consider yourself strongest at?”, shown as a percentage of respondents per age group (35–49 and ≥50). Asterisks (*) indicate significant differences from the other alternatives, as determined by the McNemar test, <span class="html-italic">p</span> &lt; 0.01.</p>
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23 pages, 2957 KiB  
Article
Assessing Appropriation of Space in Urban Green Spaces: Three Case Studies in Downtown Shanghai
by Marcus Vinicius Sant’Anna, Wuzhong Zhou and Yuanyuan Xu
Land 2024, 13(11), 1893; https://doi.org/10.3390/land13111893 - 12 Nov 2024
Viewed by 325
Abstract
This study investigated patterns of activities in urban green spaces (UGSs) in downtown Shanghai. UGSs are essential public infrastructure, contributing to urban sustainability, quality of life, and social cohesion. Although widely studied, there is a gap in the literature regarding Chinese UGSs when [...] Read more.
This study investigated patterns of activities in urban green spaces (UGSs) in downtown Shanghai. UGSs are essential public infrastructure, contributing to urban sustainability, quality of life, and social cohesion. Although widely studied, there is a gap in the literature regarding Chinese UGSs when the object of study is the nature the activities. In this sense, we aimed to investigate the activities from the perspective of appropriation of the space, considered here as different from the use of space. This study addressed this by analyzing user demographics, frequency, and spatial activity patterns to assess how these activities could be classified and scored according to a varying levels of appropriation. Through a mixed-methods design based on non-participant observation and behavior mapping, the study was conducted across three comprehensive parks in Shanghai, divided into nine observation zones. The data were analyzed through descriptive statistics, IBM SPSS, and qualitative coding, revealing, as the main findings, sixty distinct activity types, a soft to moderate level of appropriation, and notable variations in demographic presence and temporal trends. This research underscores the effectiveness of observational methods, validates appropriation as an analytical category, and emphasizes the importance of structured classification systems for improving the understanding of UGSs’ socio-spatial performance and their societal role. Full article
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<p>Observation zones in Xujiahui Park.</p>
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<p>Observation zones in Renmin Park.</p>
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<p>Observation zones in Jing’An Park.</p>
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<p>Distribution of user occurrences characterized by age and gender. <sup>1</sup> Age categories: 1 = infant (0–1 year), 2 = children (2–9 years), 3 = adolescent (10–19 years), 4 = youth (20–24 years), 5 = adult (25–64 years), 6 = elderly (+65 years).</p>
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<p>Activity occurrences by observation rounds in Xujiahui’s observation zones through eight observation days and the average line.</p>
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<p>Activity occurrences by observation rounds in Renmin’s observation zones through eight observation days and the average line.</p>
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<p>Activity occurrences by observation rounds in Jing’An’s observation zones through eight observation days and the average line.</p>
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<p>Average activity occurrences by observation rounds in all the three parks’ observation zones through eight observation days and the grand mean.</p>
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17 pages, 7184 KiB  
Article
Fluid Flow Modeling and Experimental Investigation on a Shear Thickening Fluid Damper
by Shiwei Chen, Xiaojiao Fu, Peiling Meng, Lei Cheng, Lifang Wang and Jing Yuan
Buildings 2024, 14(11), 3548; https://doi.org/10.3390/buildings14113548 - 7 Nov 2024
Viewed by 387
Abstract
Shear Thickening Fluid (STF) is a specialized high-concentration particle suspension capable of rapidly and reversibly altering its viscosity when exposed to sudden impacts. Consequently, STF-based dampers deliver a self-adaptive damping force and demonstrate significant potential for applications in structural vibration control. This study [...] Read more.
Shear Thickening Fluid (STF) is a specialized high-concentration particle suspension capable of rapidly and reversibly altering its viscosity when exposed to sudden impacts. Consequently, STF-based dampers deliver a self-adaptive damping force and demonstrate significant potential for applications in structural vibration control. This study presents both a modeling and experimental investigation of a novel double-rod structured STF damper. Initially, a compound STF is formulated using silica particles as the dispersed phase and polyethylene glycol solution as the dispersing medium. The rheological properties of the STF are then experimentally evaluated. The STF’s constitutive rheological behavior is described using the G-R model. Following this, the flow behavior of the STF within the damper’s annular gap is explored, leading to the development of a two-dimensional axisymmetric fluid simulation model for the damper. Based on this model, the dynamic mechanism of the proposed STF damper is analyzed. Subsequently, the STF damper is optimally designed and subjected to experimental investigation using a dynamic testing platform under different working conditions. The experimental results reveal that the proposed STF damper, whose equivalent stiffness can achieve a nearly threefold change with excitation frequency and amplitude, exhibits good self-adaptive capabilities. By dividing the damper force into two parts: the frictional damping pressure drop, and the osmotic pressure drop generated by the “Jamming effect”. A fitting model is proposed, and it aligns closely with the nonlinear performance of the STF damper. Full article
(This article belongs to the Special Issue Building Foundation Analysis: Soil–Structure Interaction)
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<p>The fabrication of the STF material (<b>a</b>) The preparation process of STF material (<b>b</b>) The SEM picture of SiO<sub>2</sub>.</p>
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<p>The rheological properties of the STF.</p>
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<p>Designing of the STF damper (<b>a</b>) schematic, 1. Damper tube, 2. Piston head, 3. Cylinder chamber, 4. Annular gap, 5. Piston rod, 6. Seal rings (<b>b</b>) 3D model (<b>c</b>) Prototype.</p>
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<p>Flow Field Computation Model STF damper.</p>
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<p>The average shear rate of STF in the annular gap when the damper subjected a A<sub>0</sub> = 3 mm, f = 5 Hz sine displacement excitation.</p>
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<p>Amplitude variation of STF shear rate with piston diameter (30–40 mm) when the damper subjected a A<sub>0</sub> = 3 mm, f = 5 Hz sine displacement excitation.</p>
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<p>Dynamic testing platform of the STF damper (<b>a</b>) experimental picture (<b>b</b>) enlarged schematic diagram of the STF damper.</p>
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<p>Force-displacement hysteresis curves at different frequencies and amplitudes (<b>a</b>) STF damper, A = 3 mm (<b>b</b>) STF damper, A = 2 mm (<b>c</b>) STF damper, A = 1 mm (<b>d</b>) Control group damper, A = 3 mm.</p>
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<p>The dynamic properties of the proposed dampers, the blue arrows represent the trajectory of an infinitesimal fluid element, the black arrow represent the direction of fluid flow (<b>a</b>) Equivalent stiffness (<b>b</b>) Viscous coefficient.</p>
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<p>Fluid state between the gap of the damping spacer bar plate, the blue arrows represent the trajectory of an infinitesimal fluid element (<b>a</b>) Micro-scale particle blockage phenomenon (<b>b</b>) Flow model.</p>
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<p>The dynamic properties of the proposed dampers (<b>a</b>) Equivalent stiffness, e = 2.75, f = 0.014, g = 0.072 (<b>b</b>) Dynamic viscous a = 497.9, b = −46.92, c = 1.75 (<b>c</b>) Friction force, h = 3.824, i = −0.326, j = 0.082.</p>
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<p>Force-displacement curves from both experiment and calculation under different frequencies and amplitudes. (<b>a</b>) STF damper, A = 1 mm (<b>b</b>) STF damper, A = 2 mm (<b>c</b>) STF damper, A = 3 mm.</p>
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14 pages, 592 KiB  
Article
Profile Variation in PSR B0355+54 over a Narrow Frequency Range
by Shibo Jiang, Lin Li, Rai Yuen, Jianping Yuan, Jumei Yao, Xun Shi, Yonghua Xu, Jianling Chen and Zhigang Wen
Universe 2024, 10(11), 416; https://doi.org/10.3390/universe10110416 - 6 Nov 2024
Viewed by 381
Abstract
We investigate changes in the shape of the averaged pulse profile in PSR B0355+54 (PSR J0358+5413) based on data obtained at the center frequency of 1250 MHz using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Our dataset consists of 12 non-consecutive observations, each [...] Read more.
We investigate changes in the shape of the averaged pulse profile in PSR B0355+54 (PSR J0358+5413) based on data obtained at the center frequency of 1250 MHz using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Our dataset consists of 12 non-consecutive observations, each lasting between 1 and 2 h. Considerable variation is observed in the averaged profiles across the observations even though each is folded from thousands of single pulses. Changes in the profile are measured through the ratio (R) between the peak intensities of the leading and trailing components. We find that the averaged pulse profile exhibits significant variation across observations, but distinctive from typical profile mode-changing. By dividing the frequency bandwidth into eight sub-bands, we demonstrate that the shape of the averaged profile undergoes significant evolution with frequency. In general, the changes in R across the sub-bands are different in different observations, but its value is uniform at low frequencies implying a more consistent emission. We demonstrate that the profile stabilization timescale for this pulsar is much longer than commonly suggested for ordinary pulsars, which is likely due to non-uniform and varying arrangement of the emission sources in the emission region. Full article
(This article belongs to the Section Compact Objects)
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<p>The normalized averaged pulse profiles for the four observations on MJDs 58650, 59363, 59371, and 59373 at the center frequency of 1250 MHz are displayed in the bottom row. Above each are the normalized pulse profiles obtained by averaging the data at the designated frequency sub-bands from the corresponding observation.</p>
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<p>Similar to <a href="#universe-10-00416-f001" class="html-fig">Figure 1</a>, but for the four observations on MJDs 59414, 59415, 59436, and 59508.</p>
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<p>Similar to <a href="#universe-10-00416-f001" class="html-fig">Figure 1</a> but for the four observations on MJDs 59870, 59881, 59883 and 59981.</p>
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<p>The left subplot displays the normalized averaged pulse profiles from the 12 observations in different colors. A zoom in to the two component peaks is shown in the right subplot.</p>
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<p>Plot showing the evolution of the relative intensity ratio (<span class="html-italic">R</span>) over the 12 observation sessions. The red dashed line indicates the mean value of <span class="html-italic">R</span>. For clarity, the separations between observations are plotted with equal spacing along the <span class="html-italic">x</span>-axis by ignoring the actual separation in MJDs.</p>
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<p>The relative intensity ratios of the leading component to the trailing component (<span class="html-italic">R</span>) at different frequency sub-bands for the 12 observations. The curves for the change in <span class="html-italic">R</span> on different MJDs are represented by different colors. The two horizontal lines are drawn with two constant <span class="html-italic">R</span> values across the frequency sub-bands.</p>
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<p>Plot presenting the variation in the <span class="html-italic">R</span> value across the 12 observations for three sub-bands at center frequencies of 1075 MHz, 1275 MHz, and 1425 MHz. Note that the horizontal axis is in observation number, with the actual separation between different MJDs ignored.</p>
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<p>Plot showing the averaged pulse profiles (yellow) and the normalized variance curves (blue). Each subplot corresponds to one observation.</p>
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16 pages, 2933 KiB  
Article
Optimizing Models and Data Denoising Algorithms for Power Load Forecasting
by Yanxia Li, Ilyosbek Numonov Rakhimjon Ugli, Yuldashev Izzatillo Hakimjon Ugli, Taeo Lee and Tae-Kook Kim
Energies 2024, 17(21), 5513; https://doi.org/10.3390/en17215513 - 4 Nov 2024
Viewed by 593
Abstract
To handle the data imbalance and inaccurate prediction in power load forecasting, an integrated data denoising power load forecasting method is designed. This method divides data into administrative regions, industries, and load characteristics using a four-step method, extracts periodic features using Fourier transform, [...] Read more.
To handle the data imbalance and inaccurate prediction in power load forecasting, an integrated data denoising power load forecasting method is designed. This method divides data into administrative regions, industries, and load characteristics using a four-step method, extracts periodic features using Fourier transform, and uses Kmeans++ for clustering processing. On this basis, a Transformer model based on an adversarial adaptive mechanism is designed, which aligns the data distribution of the source domain and target domain through a domain discriminator and feature extractor, thereby reducing the impact of domain offset on prediction accuracy. The mean square error of the Fourier transform clustering method used in this study was 0.154, which was lower than other methods and had a better data denoising effect. In load forecasting, the mean square errors of the model in predicting long-term load, short-term load, and real-time load were 0.026, 0.107, and 0.107, respectively, all lower than the values of other comparative models. Therefore, the load forecasting model designed for research has accuracy and stability, and it can provide a foundation for the precise control of urban power systems. The contributions of this study include improving the accuracy and stability of the load forecasting model, which provides the basis for the precise control of urban power systems. The model tracks periodicity, short-term load stochasticity, and high-frequency fluctuations in long-term loads well, and possesses high accuracy in short-term, long-term, and real-time load forecasting. Full article
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<p>Structural anomalies.</p>
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<p>Business anomalies.</p>
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<p>Analysis of power load-related factors.</p>
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<p>Flow of load characteristic clustering algorithm. (<b>a</b>) Load characteristic clustering algorithm. (<b>b</b>) Clustering algorithm process.</p>
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<p>Noise reduction algorithm process.</p>
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<p>Architecture of power load sequence prediction model.</p>
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<p>Adaptive transformer algorithm process.</p>
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<p>Cluster parameter adjustment results.</p>
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<p>Real-time load, short-term load, and long-term LF effectiveness.</p>
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<p>Model training loss curve.</p>
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<p>LF loss curve.</p>
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11 pages, 990 KiB  
Article
Pregnant Woman in Outcomes with Prosthetic Heart Valves
by Giunai Sefiyeva, Ulyana Shadrina, Tatiana Vavilova, Olga Sirotkina, Andrey Bautin, Aigul Chynybekova, Anna Pozhidaeva, Ekaterina Stepanovykh, Anna Starshinova, Dmitry Kudlay and Olga Irtyuga
J. Cardiovasc. Dev. Dis. 2024, 11(11), 353; https://doi.org/10.3390/jcdd11110353 - 4 Nov 2024
Viewed by 530
Abstract
We here sought to assess thrombotic and hemorrhagic complications and associated risk factors during pregnancy, delivery, and postpartum in women with prosthetic heart valves (PHV). Methods: The retrospective cohort study covered January 2011 to December 2022. The objective of the study was to [...] Read more.
We here sought to assess thrombotic and hemorrhagic complications and associated risk factors during pregnancy, delivery, and postpartum in women with prosthetic heart valves (PHV). Methods: The retrospective cohort study covered January 2011 to December 2022. The objective of the study was to assess the risk factors and frequency of thrombotic and hemorrhagic complications during pregnancy, delivery, and the postpartum period in women with PHV based on the experience of one perinatal center. We included 88 pregnancies with 77 prosthetic heart valves (PHV), which were divided into two groups, mechanical valve prostheses (MVP) (n = 64) and biological valve prosthesis (BVP) (n = 24). In the study we analyzed pregnancy outcomes, as well as thrombotic and hemorrhagic complication frequencies. Results: Of 88 pregnancies, 79 resulted in live births. In the MVP group, there were six miscarriages (9.4%) and two medical abortions (3.1%), including one due to Warfarin’s teratogenic effects. No miscarriages were reported in the BVP group, but one fetal mortality case (4.2%) occurred. During pregnancy, 11 MVP cases (17.2%) experienced thrombotic complications. In the BVP group, one patient (4.2%) had transient ischemic attack (TIA). Two MVP cases required surgical valve repair during pregnancy, and one in the post-delivery stage was caused by thrombotic complications. Postpartum, two MVP cases had strokes, and in one MVP patient, pulmonary embolism was registered, while no thrombotic complications occurred in the BVP group. Hemorrhagic complications affected 15 MVP cases (17.9%) in the postpartum period. There were no registered cases of maternal mortality. Conclusions: The effective control of anti-factor Xa activity reduced thrombotic events. However, the persistently high incidence of postpartum hemorrhagic complications suggests a need to reassess anticoagulant therapy regimens, lower target levels of anti-Xa, and reduce INR levels for discontinuing heparin bridge therapy. Despite the heightened mortality risk in MVP patients, our study cohort did not have any mortality cases, which contrasts with findings from other registries. Full article
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<p>Characterization of the studied patient cohort. (<b>A</b>) Patient recruitment process. (<b>B</b>) The etiological reasons for valve replacement surgery. Total number of observations n = 77. (<b>C</b>) Localization of prosthesis. Total number of observations n = 77. AV—aortic valve, MV—mitral valve, TV—tricuspid valve (Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>).</p>
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<p>Complications in pregnancies with mechanical valve prosthesis and biological valve prosthesis (Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>).</p>
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13 pages, 337 KiB  
Article
Impact of the COVID-19 Pandemic on Diabetic Ketoacidosis Patients Treated in a Pediatric Intensive Care Unit: A Single-Center Cross-Sectional Study
by Eva Perak, Dina Mrcela and Josko Markic
Medicina 2024, 60(11), 1775; https://doi.org/10.3390/medicina60111775 - 30 Oct 2024
Viewed by 419
Abstract
Background and Objectives: Diabetic ketoacidosis (DKA) is a common complication of type 1 diabetes mellitus (T1DM) in children. Here, we explored the impact of the coronavirus disease 2019 (COVID-19) pandemic on the occurrence and severity of DKA in children in southern Croatia. Materials [...] Read more.
Background and Objectives: Diabetic ketoacidosis (DKA) is a common complication of type 1 diabetes mellitus (T1DM) in children. Here, we explored the impact of the coronavirus disease 2019 (COVID-19) pandemic on the occurrence and severity of DKA in children in southern Croatia. Materials and Methods: The demographics and clinical and laboratory findings of all children and adolescents aged 0–18 years diagnosed with DKA and admitted to the pediatric intensive care unit (PICU) of the University Hospital of Split, Croatia from January 2013 to May 2023 were retrospectively collected. The participants were divided into two groups: (1) the pre-pandemic group (presenting before mid-March 2020) and (2) the pandemic group (presenting afterwards). Results: A total of 91 patients were included, 68 in the pre-pandemic and 23 in the pandemic group. The admission rate was similar (<1 patient per month) in both groups. In comparison to pre-pandemic patients, which mostly presented during the summer (52.9%) and winter seasons (23.5%), most pandemic cases occurred in spring (34.8%) and fall (30.4%, p = 0.002). No significant differences between the groups were identified in the severity of DKA, as reflected either by mean pH and median bicarbonate levels or by the proportion of patients with severe DKA. Nevertheless, HbA1c and triglycerides were significantly higher in the pandemic group (12.56% vs. 11.02%, p = 0.002 and 4.95 mmol/L vs. 2.8 mmol/L, p = 0.022, respectively) indicating poorer long-term glycemia. DKA complications were, overall, rare and without significant differences between the groups. Conclusions: The COVID-19 pandemic did not impact overall frequency or severity of DKA in children in southern Croatia. While the seasonal changes in DKA occurrence and a poorer long-term glycemia in pandemic patients may have been influenced by COVID-19 outbreaks and the imposed anti-pandemic measures, further studies are needed to determine if this was a temporary pandemic-related phenomenon or if this trend would persist in the future. Full article
(This article belongs to the Section Epidemiology & Public Health)
10 pages, 627 KiB  
Article
Impact of Hormones and Lifestyle on Oral Health During Pregnancy: A Prospective Observational Regression-Based Study
by Liliana Sachelarie, Ait el haj Iman, Murvai Violeta Romina, Anca Huniadi and Loredana Liliana Hurjui
Medicina 2024, 60(11), 1773; https://doi.org/10.3390/medicina60111773 - 30 Oct 2024
Viewed by 386
Abstract
Background and Objectives: This study explores the impact of hormonal fluctuations during pregnancy and lifestyle factors on stomatognathic system (SS) health. The aim is to determine how pregnancy-related hormonal changes and oral hygiene behaviors affect the onset of stomatognathic issues, such as [...] Read more.
Background and Objectives: This study explores the impact of hormonal fluctuations during pregnancy and lifestyle factors on stomatognathic system (SS) health. The aim is to determine how pregnancy-related hormonal changes and oral hygiene behaviors affect the onset of stomatognathic issues, such as gingival inflammation (GI) and dental erosion (DE). Materials and Methods: A prospective, observational study was conducted with 100 pregnant women, divided into two groups: Group A (60 women with significant stomatognathic alterations) and Group B (40 women without such alterations). Multiple regression analysis was used to evaluate the influence of hormonal levels, oral hygiene habits, and vomiting episodes on stomatognathic health. Results: Age and socioeconomic status showed no significant association with stomatognathic health (p > 0.05). In contrast, elevated levels of estrogen (p = 0.001) and progesterone (p = 0.003) were significantly linked to the severity of stomatognathic changes. Oral hygiene habits also had a statistically significant impact (p = 0.02), while vomiting frequency was not an important factor (p > 0.05). Conclusions: Hormonal changes during pregnancy, particularly increased estrogen and progesterone levels, are key predictors of stomatognathic health. These findings suggest that while oral hygiene is important, hormonal fluctuations play a dominant role in influencing stomatognathic system (SS) health during pregnancy. Full article
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<p>Workflow.</p>
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11 pages, 1868 KiB  
Article
The Effect of Bone Mechanical Stress Caused by Electrical Stimulation-Induced Muscle Contraction on Osteocalcin Secretion
by Yi-Chen Chen, Ryoya Oga, Takahiro Furumi, Koki Nakagawa, Yoshihiro Nita and Hiroyuki Tamaki
Biology 2024, 13(11), 882; https://doi.org/10.3390/biology13110882 - 30 Oct 2024
Viewed by 537
Abstract
Electrical stimulation-induced muscle contraction (ESMC) has demonstrated various physiological benefits, but its effects on the secretion of undercarboxylated osteocalcin (ucOC), a bone-derived cytokine, remain unclear. This study explored the relationship between ESMC, bone strain, and ucOC secretion through two experiments. In the first, [...] Read more.
Electrical stimulation-induced muscle contraction (ESMC) has demonstrated various physiological benefits, but its effects on the secretion of undercarboxylated osteocalcin (ucOC), a bone-derived cytokine, remain unclear. This study explored the relationship between ESMC, bone strain, and ucOC secretion through two experiments. In the first, young male Fischer 344 rats were divided into three groups: low-frequency ES (LF, 10 Hz), high-frequency ES (HF, 100 Hz), and control (CON). Acute 30-min transcutaneous ES was applied, and both bone strain and ucOC levels were measured. In the second experiment, rats underwent LF or HF long-term ES (two sessions per week for 4 weeks), with ucOC and insulin levels monitored. Results revealed a significant peak in ucOC at 6 h post-acute LF-ESMC. Despite HF-ESMC generating greater bone strain, LF-ESMC, with smaller but repetitive bone strain, proved more effective in stimulating ucOC secretion. In the long-term study, both ESMC groups exhibited early increases in ucOC, with a positive correlation to insulin levels. In conclusion, bone strain induced by ES-mediated muscle contraction promotes ucOC secretion, with both the magnitude and frequency of strain playing critical roles. Full article
(This article belongs to the Section Physiology)
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<p>Bone strain under (<b>A</b>) 10 Hz, (<b>B</b>) 100 Hz electrical stimulation-induced muscle contraction (ESMC), and (<b>C</b>) the temporal changes of ucOC after a single bout of ESMC intervention. * <span class="html-italic">p</span> &lt; 0.05. (CON, control [n = 6]; LF, low-frequency electrical stimulation [n = 8]; HF, high-frequency electrical stimulation [n = 8]). Values are presented as the mean ± SD.</p>
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<p>The changes in (<b>A</b>) ucOC, (<b>B</b>) insulin levels during the 4-week ESMC intervention, and (<b>C</b>) the correlation between ucOC and insulin levels at all time points. # <span class="html-italic">p</span> &lt; 0.05 vs. pre. (CON, control [n = 6]; LF, low-frequency electrical stimulation [n = 8]; HF, high-frequency electrical stimulation [n = 7]). Values are presented as the mean ± SD.</p>
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<p>Immunohistochemical staining photomicrographs of tibial anterior (TA) muscle for dystrophin in (<b>A</b>) CON, (<b>B</b>) LF, and (<b>C</b>) HF groups, and quantification of mean myofiber cross-sectional area (FCSA) for (<b>D</b>) all sections and (<b>E</b>) deep sections. The scale bar represents 100 μm. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01. (CON, control [n = 6]; LF, low-frequency electrical stimulation [n = 8]; HF, high-frequency electrical stimulation [n = 8]). Values are presented as the mean ± SD.</p>
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<p>The schematic summary of the methodology and research findings. (LF, low-frequency electrical stimulation; HF, high-frequency electrical stimulation; ESMC, electrical stimulation-induced muscle contraction; ucOC, undercarboxylated osteocalcin).</p>
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12 pages, 2029 KiB  
Communication
Neural Network Adaptive Inverse Control of Flexible Joint Space Manipulator Considering the Influence of Gravity
by Shaoqing Li, Lingcong Meng, Kai Fang and Fucai Liu
Sensors 2024, 24(21), 6942; https://doi.org/10.3390/s24216942 - 29 Oct 2024
Viewed by 357
Abstract
With the aim of correcting the problem of trajectory tracking control of a flexible joint space manipulator in environments with different gravity, a neural network adaptive inverse control algorithm based on singular perturbation theory is proposed to resist the disturbance caused by system [...] Read more.
With the aim of correcting the problem of trajectory tracking control of a flexible joint space manipulator in environments with different gravity, a neural network adaptive inverse control algorithm based on singular perturbation theory is proposed to resist the disturbance caused by system uncertainty. Firstly, the dynamic model of a flexible joint space manipulator with the influence of gravity is established, and then the system is divided into a fast subsystem and a slow subsystem using singular perturbation theory. The velocity feedback control rate is designed for the fast subsystem to suppress the elastic vibration caused by the joint flexibility. For the slow subsystem, the uncertain term and known term are separated by the inverse control algorithm, where the uncertain term is approximated online by the RBF neural network, and the robust control rate is designed to compensate for the approximation error. The simulation results show that the control method can not only effectively reduce the high-frequency vibration caused by the flexible joint but also resist the system disturbance so that a good track control effect is achieved. Full article
(This article belongs to the Section Sensors and Robotics)
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<p>Two-link flexible joint space manipulator.</p>
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<p>Simplified model of flexible joint.</p>
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<p>Control block diagram of flexible joint space manipulator system.</p>
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<p>PD control end tracking based on singular perturbation.</p>
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<p>PD control joint driving torque based on singular perturbation.</p>
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<p>Trajectory tracking of adaptive backstepping control based on singularly perturbed neural network.</p>
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<p>Singular perturbation neural network adaptive inverse control position error.</p>
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<p>Adaptive inverse control of motor control torque based on singularly perturbed neural network.</p>
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<p>f(x) and its estimation of adaptive backstepping control of singularly perturbed neural networks.</p>
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16 pages, 2292 KiB  
Article
Frequencies or Absolute Numbers? Cluster Analysis of Frequencies and Absolute Numbers of B-Cell Subsets in Dialysis Patients Who Are Candidates for Kidney Transplantation Reveals Different Profiles
by Ariadni Fouza, Asimina Fylaktou, Anneta Tagkouta, Maria Daoudaki, Lampros Vagiotas, Efstratios Kasimatis, Aliki Xochelli, Vasilki Nikolaidou, Georgios Katsanos, Georgios Tsoulfas, Lemonia Skoura, Aikaterini Papagianni and Nikolaos Antoniadis
J. Clin. Med. 2024, 13(21), 6454; https://doi.org/10.3390/jcm13216454 - 28 Oct 2024
Viewed by 404
Abstract
Background: Detailed characterization of B cells in dialysis patients who are candidates for kidney transplant is still lacking, with little information on how dialysis duration and modality impact B cell subsets. Methods: Cluster analysis of flow cytometry determined the frequencies and absolute numbers [...] Read more.
Background: Detailed characterization of B cells in dialysis patients who are candidates for kidney transplant is still lacking, with little information on how dialysis duration and modality impact B cell subsets. Methods: Cluster analysis of flow cytometry determined the frequencies and absolute numbers of B-cell subsets and divided the cohort of 78 candidates into two distinct clusters, one with shorter and one with longer dialysis duration. Results: The immune profiles of the clusters differed depending on whether frequencies or absolute counts were considered. In long-term dialysis patients, the frequency of total memory, double negative and marginal zone B cells increased, while the frequency of naive and regulatory B cells decreased. This pattern was reversed in short-term dialysis patients, with a decrease in memory and an increase in naive and regulatory populations. The B subset number decreased significantly in long-term dialysis patients, while it increased significantly in short-term dialysis patients. The dialysis modality affected the frequency-based subset immune profiles. Conclusions: It is important to determine whether the evaluation is based on frequencies or absolute numbers. The different distribution of B cell subsets in the clusters, in terms of frequencies and absolute numbers, was influenced by dialysis duration. Modality and age only influenced the frequencies. Full article
(This article belongs to the Section Immunology)
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<p>Workflow of the study.</p>
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<p>The two models constructed by cluster analysis.</p>
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<p>Cluster dendrogram of Model 1 (percentage model). The two clusters are shown as rectangles, separating 22 patients (cluster 1) from 56 patients (cluster 2). The vertical axis of the dendrogram (height) represents the dissimilarity between the clusters, as expressed by the distance metric. The greater the vertical distance, the greater the dissimilarity. The horizontal axis represents the individual data points (patients) that are grouped into clusters.</p>
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<p>The cell immune profile of candidates in cluster 1 and 2 (Model 1).</p>
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<p>Cluster dendrogram of Model 2 (absolute model). The two clusters are shown in rectangles separating 46 patients from 32 patients (left; cluster C, right; cluster A). The vertical axis of the dendrogram (height) represents the dissimilarity between the clusters as expressed by the distance metric. The greater the vertical distance, the greater the dissimilarity. The horizontal axis represents the individual data points (patients), which were combined to form clusters.</p>
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<p>The cell immune profile of candidates in cluster A and C.</p>
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18 pages, 5084 KiB  
Article
Activation of Ms 6.9 Milin Earthquake on Sedongpu Disaster Chain, China with Multi-Temporal Optical Images
by Yubin Xin, Chaoying Zhao, Bin Li, Xiaojie Liu, Yang Gao and Jianqi Lou
Remote Sens. 2024, 16(21), 4003; https://doi.org/10.3390/rs16214003 - 28 Oct 2024
Viewed by 478
Abstract
In recent years, disaster chains caused by glacier movements have occurred frequently in the lower Yarlung Tsangpo River in southwest China. However, it is still unclear whether earthquakes significantly contribute to glacier movements and disaster chains. In addition, it is difficult to measure [...] Read more.
In recent years, disaster chains caused by glacier movements have occurred frequently in the lower Yarlung Tsangpo River in southwest China. However, it is still unclear whether earthquakes significantly contribute to glacier movements and disaster chains. In addition, it is difficult to measure the high-frequency and large gradient displacement time series with optical remote sensing images due to cloud coverage. To this end, we take the Sedongpu disaster chain as an example, where the Milin earthquake, with an epicenter 11 km away, occurred on 18 November 2017. Firstly, to deal with the cloud coverage problem for single optical remote sensing analysis, we employed multiple platform optical images and conducted a cross-platform correlation technique to invert the two-dimensional displacement rate and the cumulative displacement time series of the Sedongpu glacier. To reveal the correlation between earthquakes and disaster chains, we divided the optical images into three classes according to the Milin earthquake event. Lastly, to increase the accuracy and reliability, we propose two strategies for displacement monitoring, that is, a four-quadrant block registration strategy and a multi-window fusion strategy. Results show that the RMSE reduction percentage of the proposed registration method reaches 80%, and the fusion method can retrieve the large magnitude displacements and complete displacement field. Secondly, the Milin earthquake accelerated the Sedongpu glacier movement, where the pre-seismic velocities were less than 0.5 m/day, the co-seismic velocities increased to 1 to 6 m/day, and the post-seismic velocities decreased to 0.5 to 3 m/day. Lastly, the earthquake had a triggering effect around 33 days on the Sedongpu disaster chain event on 21 December 2017. The failure pattern can be summarized as ice and rock collapse in the source area, large magnitude glacier displacement in the moraine area, and a large volume of sediment in the deposition area, causing a river blockage. Full article
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<p>Overview of the study area. (<b>a</b>) Image footprints of the Sentinel-2 (S2), Beijing-2 (BJ-2), and SuperView-1 (SV-1) used in this study with a shaded 30 m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) as the background. Fault data and seismicity activities with a magnitude above four were downloaded from the Chinese Earthquake Network Center (<a href="http://www.ceic.ac.cn/" target="_blank">http://www.ceic.ac.cn/</a>, accessed on 20 December 2023). (<b>b</b>) Enlarged topography of the Sedongpu Basin. The black line defines the extent of the Sedongpu basin, the yellow and green rectangles represent two representative stable areas, and the red line represents the profile A-B-C-D along the glacier flow direction.</p>
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<p>Flowchart of pre-, co-, and post-seismic glacier displacement monitoring and accuracy evaluation with multi-temporal optical image correlation.</p>
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<p>Technical flowchart of the four-quadrant block image registration method.</p>
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<p>Optical image correlation results for image pair acquired on 7 November 2017 and 21 December 2017 with different registration strategies. The first row (<b>a</b>–<b>d</b>) is block diagrams, the second row (<b>e</b>–<b>h</b>) is the north–south results, and the third row (<b>i</b>–<b>l</b>) is the east–west results. (<b>e</b>,<b>i</b>) are the results without registration, (<b>f</b>,<b>j</b>) are the results after overall registration, (<b>g</b>,<b>k</b>) are the results after top and bottom block registration, and (<b>h</b>,<b>l</b>) are the results after four-quadrant block registration.</p>
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<p>The accuracy evaluation of four registration methods was conducted over two stable areas indicated in <a href="#remotesensing-16-04003-f001" class="html-fig">Figure 1</a>. (<b>a</b>,<b>b</b>) are the Mean Value (MEV) and Standard Deviation (STD) of the N–S and E–W displacement in the green and yellow regions, respectively. (<b>c</b>,<b>d</b>) are the reduction percentage of Root Mean Square Error (RMSE) in the green and yellow areas, respectively.</p>
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<p>Optical image correlation results with different window sizes. (<b>a</b>–<b>d</b>) are north–south results, (<b>e</b>–<b>h</b>) are east–west results, (<b>i</b>–<b>l</b>) are fusion results from different window sizes shown in 2D and 3D DEM.</p>
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<p>Accuracy evaluation for different window sizes was conducted over two stable areas indicated in <a href="#remotesensing-16-04003-f001" class="html-fig">Figure 1</a>. (<b>a</b>,<b>b</b>) the MEV and STD of the N–S and E–W displacement; (<b>c</b>,<b>d</b>) the RMSE reduction percentage in the green and yellow areas, respectively.</p>
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<p>Displacement magnitudes and directions for three stages during the pre-, co-, and post-seismic periods. (<b>a</b>) Show the gully distribution with I–V and the direction of the glacial displacement. (<b>b</b>–<b>d</b>) are the pre-seismic displacement field from Sentinel-2 images, (<b>e</b>) the co-seismic high-resolution displacement field, (<b>f</b>) the post-seismic displacement field from Sentinel-2 images.</p>
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<p>Displacement rate maps and the profile of Sedongpu Glacier at three stages. (<b>a</b>–<b>c</b>) the daily displacement during the pre-, co-, and post-seismic periods, respectively. (<b>d</b>) The profile of daily displacement rate during three stages. The three sections are separated by dashed lines, with section I corresponding to AB, section II to BC, and section III to CD.</p>
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<p>(<b>a</b>,<b>b</b>) are Planet satellite images from 21 December and 26 December 2017, respectively, used to observe changes before and after the disaster chain. (<b>c</b>) The schematic diagram of longitudinal section in Sedongpu. (<b>d</b>) The failure pattern of the Sedongpu disaster chain.</p>
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32 pages, 7112 KiB  
Article
Stochastic Green’s Function Method Considering Non-Uniform Rise Time Distribution to Simulate 3D Broadband Ground Motion
by Longfei Ji, Xu Xie and Xiaoyu Pan
Appl. Sci. 2024, 14(21), 9796; https://doi.org/10.3390/app14219796 - 26 Oct 2024
Viewed by 508
Abstract
The stochastic Green’s function method has been widely used in the field of ground motion simulation in recent years. It is generally assumed that the rise time of each subfault is the same in this method. Since the rise time significantly influences the [...] Read more.
The stochastic Green’s function method has been widely used in the field of ground motion simulation in recent years. It is generally assumed that the rise time of each subfault is the same in this method. Since the rise time significantly influences the amplitude of simulation results in the intermediate frequency band, to improve the accuracy of stochastic Green’s function method for near-fault broadband ground motion simulation, referring to the numerical simulation results of Day, the rise time is assumed to be non-uniformly distributed on the fault, and an improved approximate expression of rise time on a rectangular fault considering that the rupture starting point may be at any position and the aspect ratio may be arbitrary is proposed. Additionally, the contributions of P, SV and SH wave are considered, respectively, and an improved stochastic Green’s function method is proposed for 3D broadband ground motion simulation. Taking the 1994 Northridge earthquake in America and 2013 Lushan earthquake in China as examples, under different subfault division numbers, the synthesized source spectra are compared with the omega-squared theoretical source spectra of the large earthquake, and the simulated ground motions at observation points are compared with observed records to verify the effectiveness of the improved method. The results show that when the Northridge earthquake fault and Lushan earthquake fault are divided into 9 × 10 subfaults and 11 × 7 subfaults, respectively, the simulation results obtained using the improved method are close to the observed records in the broadband frequency range. Therefore, the improved method can effectively simulate the 3D ground motion in near-fault regions. Full article
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<p>Stochastic Green’s function method for 3D ground motion simulation. (<span class="html-italic">OXYZ</span> is the overall coordinate system, <span class="html-italic">O</span> is the projection of the upper left corner of the fault plane on the surface, <span class="html-italic">X</span>, <span class="html-italic">Y</span>, and <span class="html-italic">Z</span> are the north, east, and vertical downward direction, respectively, <span class="html-italic">oxy</span> is the local coordinate system of the fault plane, <span class="html-italic">o</span> is the upper left corner of the fault plane, <span class="html-italic">x</span> and <span class="html-italic">y</span> are the direction of fault length and width, respectively, <span class="html-italic">Q</span> is the rupture starting point on the fault plane, and <span class="html-italic">d</span><sub>t</sub> is the top depth of the fault plane, red arrows indicate the vibration direction of waves).</p>
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<p>Fault location and station location of 1994 Northridge earthquake. (<span class="html-italic">OXYZ</span> is the overall coordinate system, <span class="html-italic">O</span> is the projection of the upper left corner of the fault plane on the surface, <span class="html-italic">X</span>, <span class="html-italic">Y</span>, and <span class="html-italic">Z</span> are the north, east, and vertical downward direction, respectively, <span class="html-italic">oxy</span> is the local coordinate system of the fault plane, <span class="html-italic">o</span> is the upper left corner of the fault plane, <span class="html-italic">x</span> and <span class="html-italic">y</span> are the direction of fault length and width, respectively, <span class="html-italic">Q</span> is the rupture starting point on the fault plane, and <span class="html-italic">d</span><sub>t</sub> is the top depth of the fault plane. The black star marks the hypocenter, and the black triangles mark the locations of the stations.).</p>
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<p>The source spectra of the 1994 Northridge earthquake. (The red solid line represents the source spectra of the large earthquake synthesized using the constant rise time, the blue solid line represents the source spectra of the large earthquake synthesized using the modified rise time expression, and the black dashed line represents the theoretical ω<sup>−2</sup> source spectrum.).</p>
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<p>Simulation results of the L4B station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the red line represents the GMPE, the yellow line represents the simulation result with the constant rise time when the subfault division number is 9 × 10, the green line represents the simulation result with the modified rise time expression when the subfault division number is 9 × 10, the brown line represents the simulation result with the constant rise time when the subfault division number is 36 × 40, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 36 × 40).</p>
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<p>Simulation results of the L09 station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the red line represents the GMPE, the yellow line represents the simulation result with the constant rise time when the subfault division number is 9 × 10, the green line represents the simulation result with the modified rise time expression when the subfault division number is 9 × 10, the brown line represents the simulation result with the constant rise time when the subfault division number is 36 × 40, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 36 × 40).</p>
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<p>Simulation results of the ATB station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the red line represents the GMPE, the yellow line represents the simulation result with the constant rise time when the subfault division number is 9 × 10, the green line represents the simulation result with the modified rise time expression when the subfault division number is 9 × 10, the brown line represents the simulation result with the constant rise time when the subfault division number is 36 × 40, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 36 × 40).</p>
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<p>Simulation results of the HOW station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the red line represents the GMPE, the yellow line represents the simulation result with the constant rise time when the subfault division number is 9 × 10, the green line represents the simulation result with the modified rise time expression when the subfault division number is 9 × 10, the brown line represents the simulation result with the constant rise time when the subfault division number is 36 × 40, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 36 × 40).</p>
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<p>Simulation results of the CHL station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the red line represents the GMPE, the yellow line represents the simulation result with the constant rise time when the subfault division number is 9 × 10, the green line represents the simulation result with the modified rise time expression when the subfault division number is 9 × 10, the brown line represents the simulation result with the constant rise time when the subfault division number is 36 × 40, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 36 × 40).</p>
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<p>Simulation results of the LV1 station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the red line represents the GMPE, the yellow line represents the simulation result with the constant rise time when the subfault division number is 9 × 10, the green line represents the simulation result with the modified rise time expression when the subfault division number is 9 × 10, the brown line represents the simulation result with the constant rise time when the subfault division number is 36 × 40, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 36 × 40).</p>
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<p>The simulation misfit of the ground motion of the Northridge earthquake. (The yellow line represents the simulation misfit with the constant rise time when the subfault division number is 9 × 10, the green line represents the simulation misfit with the modified rise time expression when the subfault division number is 9 × 10, the brown line represents the simulation misfit with the constant rise time when the subfault division number is 36 × 40, and the blue line represents the simulation misfit with the modified rise time expression when the subfault division number is 36 × 40).</p>
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<p>Fault location and station location of 2013 Lushan earthquake. (<span class="html-italic">OXYZ</span> is the overall coordinate system, <span class="html-italic">O</span> is the projection of the upper left corner of the fault plane on the surface, <span class="html-italic">X</span>, <span class="html-italic">Y</span>, and <span class="html-italic">Z</span> are the north, east, and vertical downward direction, respectively, <span class="html-italic">oxy</span> is the local coordinate system of the fault plane, <span class="html-italic">o</span> is the upper left corner of the fault plane, <span class="html-italic">x</span> and <span class="html-italic">y</span> are the direction of fault length and width, respectively, <span class="html-italic">Q</span> is the rupture starting point on the fault plane, and <span class="html-italic">d</span><sub>t</sub> is the top depth of the fault plane. The black star marks the hypocenter, and the black triangles mark the locations of the stations.).</p>
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<p>The source spectra of the 2013 Lushan earthquake. (The red solid line represents the source spectra of the large earthquake synthesized using the constant rise time, the blue solid line represents the source spectra of the large earthquake synthesized using the modified rise time expression, the black dashed line represents the theoretical ω<sup>−2</sup> source spectrum.).</p>
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<p>Simulation results of the 51BXZ station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the yellow line represents the simulation result with the constant rise time when the subfault division number is 11 × 7, the green line represents the simulation result with the modified rise time expression when the subfault division number is 11 × 7, the brown line represents the simulation result with the constant rise time when the subfault division number is 44 × 28, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 44 × 28).</p>
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<p>Simulation results of the 51YAM station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the yellow line represents the simulation result with the constant rise time when the subfault division number is 11 × 7, the green line represents the simulation result with the modified rise time expression when the subfault division number is 11 × 7, the brown line represents the simulation result with the constant rise time when the subfault division number is 44 × 28, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 44 × 28).</p>
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<p>Simulation results of the 51LSF station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the yellow line represents the simulation result with the constant rise time when the subfault division number is 11 × 7, the green line represents the simulation result with the modified rise time expression when the subfault division number is 11 × 7, the brown line represents the simulation result with the constant rise time when the subfault division number is 44 × 28, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 44 × 28).</p>
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<p>Simulation results of the 51BXM station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the yellow line represents the simulation result with the constant rise time when the subfault division number is 11 × 7, the green line represents the simulation result with the modified rise time expression when the subfault division number is 11 × 7, the brown line represents the simulation result with the constant rise time when the subfault division number is 44 × 28, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 44 × 28).</p>
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<p>Simulation results of the 51YAL station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the yellow line represents the simulation result with the constant rise time when the subfault division number is 11 × 7, the green line represents the simulation result with the modified rise time expression when the subfault division number is 11 × 7, the brown line represents the simulation result with the constant rise time when the subfault division number is 44 × 28, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 44 × 28).</p>
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<p>Simulation results of the 51HYT station. (The comparisons of acceleration time histories are shown in the top row, the comparisons of acceleration response spectra are shown in the middle row, and the comparisons of displacement amplitude spectra are shown in the bottom row. The simulation results in the NS, EW, and UD directions are shown from left to right. The black line represents the observed record, the yellow line represents the simulation result with the constant rise time when the subfault division number is 11 × 7, the green line represents the simulation result with the modified rise time expression when the subfault division number is 11 × 7, the brown line represents the simulation result with the constant rise time when the subfault division number is 44 × 28, and the blue line represents the simulation result with the modified rise time expression when the subfault division number is 44 × 28).</p>
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<p>The simulation misfit of the ground motion of the Lushan earthquake. (The yellow line represents the simulation misfit with the constant rise time when the subfault division number is 11 × 7, the green line represents the simulation misfit with the modified rise time expression when the subfault division number is 11 × 7, the brown line represents the simulation misfit with the constant rise time when the subfault division number is 44 × 28, and the blue line represents the simulation misfit with the modified rise time expression when the subfault division number is 44 × 28).</p>
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