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Appl. Sci., Volume 13, Issue 10 (May-2 2023) – 532 articles

Cover Story (view full-size image): This work explores the use of additive manufacturing (AM) to reprocess recycled glass and carbon fibers in the automotive sector. It aims to foster the exploitation of recycled Glass-Fiber-Reinforced Polymers (rGFRPs) and recycled Carbon-Fiber-Reinforced Polymers (rCFRPs) using two manufacturing workflows: indirect Fused Filament Fabrication (FFF) and UV-assisted Direct Ink Writing (UV-DIW). After tensile tests, some molds were fabricated with an FFF 3D printer for the indirect 3D printing process to cast epoxy-based thermosetting resin with rGFs and rCFs. The second technology consisted of fabricating parts via hardening in situ a photo- and thermal-curable thermosetting acrylic liquid resin with rGFs. These results validate the use of AM and recycled composites for applications in the automotive sector. View this paper
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19 pages, 39778 KiB  
Article
Simulating Atmospheric Characteristics and Daytime Astronomical Seeing Using Weather Research and Forecasting Model
by A. Y. Shikhovtsev, P. G. Kovadlo, A. A. Lezhenin, V. S. Gradov, P. O. Zaiko, M. A. Khitrykau, K. E. Kirichenko, M. B. Driga, A. V. Kiselev, I. V. Russkikh, V. A. Obolkin and M. Yu. Shikhovtsev
Appl. Sci. 2023, 13(10), 6354; https://doi.org/10.3390/app13106354 - 22 May 2023
Cited by 5 | Viewed by 1986
Abstract
The present study is aimed at the development of a novel empirical base for application to ground-based astronomical telescopes. A Weather Research and Forecasting (WRF) model is used for description of atmospheric flow structure with a high spatial resolution within the Baikal Astrophysical [...] Read more.
The present study is aimed at the development of a novel empirical base for application to ground-based astronomical telescopes. A Weather Research and Forecasting (WRF) model is used for description of atmospheric flow structure with a high spatial resolution within the Baikal Astrophysical Observatory (BAO) region. Mesoscale vortex structures are found within the atmospheric boundary layer, which affect the quality of astronomical images. The results of simulations show that upward air motions in the lower atmosphere are suppressed both above the cold surface of Lake Baikal and inside mesoscale eddy structures. A model of the outer scale of turbulence for BAO is developed. In this work, we consider the seeing parameter that represents the full width at half-maximum of the point spread function. Optical turbulence profiles are obtained and daytime variations of seeing are estimated. Vertical profiles of optical turbulence are optimized taking into account data from direct optical observations of solar images. Full article
(This article belongs to the Special Issue Advanced Observation for Geophysics, Climatology and Astronomy)
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Figure 1

Figure 1
<p>Characteristic daytime spatial distributions of wind speed derived from the WRF model at different heights in the atmosphere. The red marker shows the BAO site. The streamlines are indicated with arrows. The yellow dotted line is the coastline of Lake Baikal. (<b>a</b>) <span class="html-italic">z</span> = 30 m. (<b>b</b>) <span class="html-italic">z</span> = 101 m. (<b>c</b>) <span class="html-italic">z</span> = 200 m.</p>
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<p>Continuation of <a href="#applsci-13-06354-f001" class="html-fig">Figure 1</a>. (<b>a</b>) <span class="html-italic">z</span> = 326 m. (<b>b</b>) <span class="html-italic">z</span> = 485 m. (<b>c</b>) <span class="html-italic">z</span> = 687 m. The red marker shows the BAO site.</p>
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<p>Continuation of <a href="#applsci-13-06354-f001" class="html-fig">Figure 1</a>. (<b>a</b>) <span class="html-italic">z</span> = 1264 m. (<b>b</b>) <span class="html-italic">z</span> = 5555 m. (<b>c</b>) <span class="html-italic">z</span> = 12,148 m. The red marker shows the BAO site.</p>
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<p>Characteristic daytime spatial distributions of positive vertical component of the wind speed at different heights in the atmosphere. The red marker shows the BAO site. The streamlines are shown with arrows. The yellow dotted line is the coastline of Lake Baikal. (<b>a</b>) <span class="html-italic">z</span> = 30 m. (<b>b</b>) <span class="html-italic">z</span> = 101 m. (<b>c</b>) <span class="html-italic">z</span> = 200 m.</p>
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<p>Continuation of <a href="#applsci-13-06354-f004" class="html-fig">Figure 4</a>. (<b>a</b>) <span class="html-italic">z</span> = 326 m. (<b>b</b>) <span class="html-italic">z</span> = 485 m. (<b>c</b>) <span class="html-italic">z</span> = 687 m.</p>
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<p>Continuation of <a href="#applsci-13-06354-f004" class="html-fig">Figure 4</a>. (<b>a</b>) <span class="html-italic">z</span> = 1264 m. (<b>b</b>) <span class="html-italic">z</span> = 5555 m. (<b>c</b>) <span class="html-italic">z</span> = 12,148 m. The red marker shows the BAO site.</p>
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<p>Large Solar Vacuum Telescope.</p>
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<p>Adaptive optics system at the Large Solar Vacuum Telescope.</p>
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<p>The focal solar spot pattern detected via the Shack–Hartmann sensor.</p>
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<p>Seeing variations at the BAO site (8 August 2022). The blue line corresponds to changes in averaged seeing values. The fill shows the standard deviations of seeing.</p>
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<p>The meteorological ultrasonic complex mounted on the LSVT.</p>
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<p>Temporary changes of calculated and measured values of <math display="inline"><semantics> <msubsup> <mi>C</mi> <mi>n</mi> <mn>2</mn> </msubsup> </semantics></math>. The oranges markers correspond to the measured values of <math display="inline"><semantics> <msubsup> <mi>C</mi> <mi>n</mi> <mn>2</mn> </msubsup> </semantics></math>. The blue markers correspond to the calculated values of <math display="inline"><semantics> <msubsup> <mi>C</mi> <mi>n</mi> <mn>2</mn> </msubsup> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <msub> <mi>a</mi> <mrow> <mi>L</mi> <mi>t</mi> <mi>s</mi> <mi>p</mi> </mrow> </msub> </semantics></math> = 1.64, <math display="inline"><semantics> <msub> <mi>b</mi> <mrow> <mi>L</mi> <mi>t</mi> <mi>s</mi> <mi>p</mi> </mrow> </msub> </semantics></math> = 42 s. (<b>b</b>) <math display="inline"><semantics> <msub> <mi>a</mi> <mrow> <mi>L</mi> <mi>t</mi> <mi>s</mi> <mi>p</mi> </mrow> </msub> </semantics></math> = 2.5, <math display="inline"><semantics> <msub> <mi>b</mi> <mrow> <mi>L</mi> <mi>t</mi> <mi>s</mi> <mi>p</mi> </mrow> </msub> </semantics></math> = 8 s.</p>
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<p>Average daytime profiles of optical turbulence at the BAO site. Line 1 corresponds to the profile calculated from WRF simulations. Line 2 corresponds to the Hufnagel–Valley model adapted for BAO.</p>
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<p>Temporary changes in the seeing parameter during the day. The blue line corresponds to the astronomical observations. The red line corresponds to the classic Dewan model. The orange line corresponds to the proposed model of vertical changes of the outer scale of turbulence.</p>
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43 pages, 3373 KiB  
Review
Review of Anomaly Detection Algorithms for Data Streams
by Tianyuan Lu, Lei Wang and Xiaoyong Zhao
Appl. Sci. 2023, 13(10), 6353; https://doi.org/10.3390/app13106353 - 22 May 2023
Cited by 10 | Viewed by 8223
Abstract
With the rapid development of emerging technologies such as self-media, the Internet of Things, and cloud computing, massive data applications are crossing the threshold of the era of real-time analysis and value realization, which makes data streams ubiquitous in all kinds of industries. [...] Read more.
With the rapid development of emerging technologies such as self-media, the Internet of Things, and cloud computing, massive data applications are crossing the threshold of the era of real-time analysis and value realization, which makes data streams ubiquitous in all kinds of industries. Therefore, detecting anomalies in such data streams could be very important and full of challenges. For example, in industries such as electricity and finance, data stream anomalies often contain information that can help avoiding risks and support decision making. However, most traditional anomaly detection algorithms rely on acquiring global information about the data, which is hard to apply to stream data scenarios. Currently, the reviews of the algorithm in the field of anomaly detection, both domestically and internationally, tend to focus on the exposition of anomaly detection algorithms in static data environments, while lacking in the induction and analysis of anomaly detection algorithms in the context of streaming data. As a result, unlike the existing literature reviews, this review provides the current mainstream anomaly detection algorithms in data streaming scenarios and categorizes them into three types on the basis of their fundamental principles: (1) based on offline learning; (2) based on semi-online learning; (3) based on online learning. This review discusses the current state of research on data stream anomaly detection and studies the key issues in various algorithms for detecting anomalies in data streams on the basis of concise summarization. Moreover, the review conducts a detailed comparison of the pros and cons of the algorithms. Finally, the future challenges in the field are analyzed, and future research directions are proposed. Full article
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Figure 1
<p>Classification of common data stream anomaly detection algorithms.</p>
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<p>Abnormal data in two-dimensional dataset.</p>
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<p>IForestASD algorithm framework.</p>
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<p>Re-adjust a CH to cover the new data that always falls on its edge.</p>
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<p>Convex hull and distance to supporting point (red line).</p>
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<p>The left picture shows CH before segmentation, and the right picture shows the center point C of segmentation.</p>
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<p>Split convex hull.</p>
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<p>ADA algorithm framework.</p>
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<p>ADA event model.</p>
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26 pages, 5244 KiB  
Article
Closed-Form Method for Atmospheric Correction (CMAC) of Smallsat Data Using Scene Statistics
by David P. Groeneveld, Timothy A. Ruggles and Bo-Cai Gao
Appl. Sci. 2023, 13(10), 6352; https://doi.org/10.3390/app13106352 - 22 May 2023
Cited by 3 | Viewed by 1564
Abstract
High-cadence Earth observation smallsat images offer potential for near real-time global reconnaissance of all sunlit cloud-free locations. However, these data must be corrected to remove light-transmission effects from variable atmospheric aerosol that degrade image interpretability. Although existing methods may work, they require ancillary [...] Read more.
High-cadence Earth observation smallsat images offer potential for near real-time global reconnaissance of all sunlit cloud-free locations. However, these data must be corrected to remove light-transmission effects from variable atmospheric aerosol that degrade image interpretability. Although existing methods may work, they require ancillary data that delays image output, impacting their most valuable applications: intelligence, surveillance, and reconnaissance. Closed-form Method for Atmospheric Correction (CMAC) is based on observed atmospheric effects that brighten dark reflectance while darkening bright reflectance. Using only scene statistics in near real-time, CMAC first maps atmospheric effects across each image, then uses the resulting grayscale to reverse the effects to deliver spatially correct surface reflectance for each pixel. CMAC was developed using the European Space Agency’s Sentinel-2 imagery. After a rapid calibration that customizes the method for each imaging optical smallsat, CMAC can be applied to atmospherically correct visible through near-infrared bands. To assess CMAC functionality against user-applied state-of-the-art software, Sen2Cor, extensive tests were made of atmospheric correction performance across dark to bright reflectance under a wide range of atmospheric aerosol on multiple images in seven locations. CMAC corrected images faster, with greater accuracy and precision over a range of atmospheric effects more than twice that of Sen2Cor. Full article
(This article belongs to the Special Issue Small Satellites Missions and Applications)
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Figure 1
<p>Inexact synchronicity is a source of error for ancillary image application as shown in the following example: (<b>a</b>) 14 August 2021 Sentinel 2 TOAR RGB of southern Minnesota, (<b>b</b>) the cirrus band (B10) of the same scene, and (<b>c</b>) the cirrus band (B09) of Landsat 8 taken about 18 min before. Cirrus affects visible bands as in (<b>a</b>).</p>
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<p>An example 100 m resolution (10 × 10 pixel grid cell) Atm-I grayscale for the 8-22-21 S2 tile over Lake Tahoe, CA, USA. At least some ground signal must remain for correction (exceeded in portions of this image).</p>
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<p>CMAC conceptual model illustrated as a dashed line expressing the effect upon any pixel, dark to bright, from a single level of atmospheric aerosol. SR is surface reflectance. The TDL crosses the x-axis at the axis point.</p>
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<p><a href="#applsci-13-06352-f002" class="html-fig">Figure 2</a> reproduced from Fraser and Kaufmann [<a href="#B31-applsci-13-06352" class="html-bibr">31</a>].</p>
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<p>Data extracted from S2 images from 2021 over an area of interest with consistent reflectance in Reno, Nevada that experienced wide swings of aerosol concentration from regional wildfire smoke. The application of such invariant locations is described further in <a href="#sec2dot4-applsci-13-06352" class="html-sec">Section 2.4</a> below. DN refers to reflectance scaled by 10,000.</p>
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<p>Salon de Provence, France region: a calibration target (arrows) in S2 TOAR regional images 16 June 2021 under light haze (<b>a</b>,<b>d</b>) and 8 March 2021 under moderate haze from wildfire smoke (<b>b</b>,<b>e</b>). A Google Earth image (<b>c</b>) of the target shows the 30 m × 30 m black and white panels.</p>
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<p>The Reno QIA outlined in red on this S2 image from 6 March 2021 is located just northeast of the Reno, NV airport. The polygon was drawn to exclude vacant lots that might harbor unmanaged vegetation.</p>
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<p>Map showing locations of six QIAs located east of Los Angeles, CA. (Source: Maxar, Earthstar Geographics and the GIS User Community). QIA locations are designated as follows: Chino (a); Ontario (b); Highgrove (c); Fontana (d); Redlands (e); and Rochester (f).</p>
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<p>Google Earth closeup images of six QIAs: (<b>a</b>) Chino; (<b>b</b>) Ontario; (<b>c</b>) Highgrove; (<b>d</b>) Fontana; (<b>e</b>) Redlands; and (<b>f</b>) Rochester.</p>
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<p>Reno QIA reflectance curves plotted for the four VNIR bands of S2 (rows). Colored curves were derived from <span class="html-italic">n</span> = 3 or <span class="html-italic">n</span> = 4 percentile averages for each band and treatment for the low-Atm-I images (clear-appearing, lacking haze). Legend values are Atm-I or average Atm-I. Curves in black are for single images that exceed Sen2Cor AC capability. Though not as accurately, CMAC corrected the extremely high Atm-I curves for the visible bands. CMAC curves are tighter (more precise) than Sen2Cor in all bands.</p>
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<p>Seven reflectance curves for the Rochester QIA by treatment (columns) and bands (rows). Each curve represents an average of four images with similar average Atm-I. Dispersion is notable for Sen2Cor in the lower limb of visible bands where high precision is needed to support applications such as precision agriculture and AI feature extraction. In CMAC, the lower limb of reflectance is comparatively precise. NIR 8A curves are virtually identical between the two methods. Rochester experienced the highest range of Atm-I levels among the SoCal QIAs.</p>
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<p>Average CV% distribution for the 22 percentile steps combined for the six QIAs; (<span class="html-italic">n</span> = 132) of CMAC and Sen2Cor. Though approached very differently, both methods show similar trends.</p>
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<p>Percent error distribution for CMAC and Sen2Cor plotted according to the rank for the 1st through 3696th estimated error values.</p>
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<p>CMAC estimated percent error plotted together for all four VNIR bands.</p>
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<p>Percent error in Sen2Cor low surface reflectance estimates rapidly increase with Atm-I in all bands except NIR 8A. CMAC error shows a more gradual trend of increased error with increasing Atm-I level.</p>
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<p>A clip from the S2 Reno image whose data are shown in <a href="#applsci-13-06352-f010" class="html-fig">Figure 10</a> statistics for Atm-I = 1743 and as the 22-8-2021 statistics in <a href="#app1-applsci-13-06352" class="html-app">Supplementary Materials as “Reno QIA Curves.xlsx”</a>. As in all other image displays in this paper, these examples are screenshots from QGIS display. (<b>a</b>) TOAR representation made from the full tile stretch. A full tile image stretch can be taken to visually represent the degraded TOAR mathematics of the image. The alternative, a clipped image stretch, does not appropriately represent the unbiased mathematics of the image that confronts AI and other machine analysis; such clip stretches may visibly clear some haze (but are typically accompanied by color balance problems). Color balance is a valuable indicator of potential problems that could occur through use of machine analyses. (<b>b</b>) CMAC clearing of the image provides the color balance conferred by the TOAR image. The features within both are darkened through hypothesized diffuse shading from aerosol particles. (<b>c</b>) Sen2Cor correction of the same clip displaying color balance problems and residual haze.</p>
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<p>S2 image, 5 March 2021 closeup of the Mexican Gulf Coast north of Veracruz: (<b>a</b>) TOAR with smoke effects from fields burned before planting; (<b>b</b>) CMAC v1.1 corrected.</p>
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<p>L8 full tile of the Mexican Gulf Coast from 5 December 2021: (<b>a</b>) TOAR, (<b>b</b>) Atm-I, (<b>c</b>) LaSRC correction and (<b>d</b>) CMAC correction. The images were rotated from their collection angle to fit squarely.</p>
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<p>TOAR (<b>a</b>) and CMAC (<b>b</b>) views of 8-18-2021 Planet Labs Dove satellite image over Fargo, North Dakota (20210818_175123_23_105a_3B_AnalyticMS_clip.tif cohort PS2.SD).</p>
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29 pages, 7672 KiB  
Article
Thermographic Investigation on Fluid Oscillations and Transverse Interactions in a Fully Metallic Flat-Plate Pulsating Heat Pipe
by Luca Pagliarini, Luca Cattani, Vincent Ayel, Maksym Slobodeniuk, Cyril Romestant and Fabio Bozzoli
Appl. Sci. 2023, 13(10), 6351; https://doi.org/10.3390/app13106351 - 22 May 2023
Cited by 4 | Viewed by 1474
Abstract
The present investigation deals with the quantification of fluid oscillation frequencies in a metallic pulsating heat pipe tested at varying heat loads and orientations. The aim is to design a robust technique for the study of the inner fluid dynamics without adopting typical [...] Read more.
The present investigation deals with the quantification of fluid oscillation frequencies in a metallic pulsating heat pipe tested at varying heat loads and orientations. The aim is to design a robust technique for the study of the inner fluid dynamics without adopting typical experimental solutions, such as direct fluid visualizations through transparent inserts. The studied device is made of copper, and it is partially filled with a water–ethanol mixture (20 wt.% of ethanol). Heat fluxes locally exchanged between the working fluid and the device walls are first assessed through the inverse heat conduction problem resolution approach by processing outer wall temperature distributions acquired by thermography. The estimated local heat transfer quantities are therefore processed to quantify the fluid oscillatory behavior in every device branch during the intermittent flow and full activation regimes, thus providing a deeper insight into the heat transfer modes. After dealing with a further validation of the inverse approach in terms of oscillation frequency restoration capability, the wall-to-fluid heat fluxes referred to each channel are processed by means of the wavelet method. Scalograms and power spectra of the considered signals are presented for a time-based analysis of the working fluid oscillations, as well as for the identification of dominant oscillation frequencies. Fluid motion is then quantified in terms of the continuity of fluid oscillations and activity of channels by applying a scalogram denoising technique named K-means clustering method. Moreover, a statistical reduction of the channel-wise dominant oscillation frequencies is performed to provide useful references for the interpretation of the overall oscillatory behavior. The link between oscillations and transverse interactions is finally investigated. The vertical bottom-heated mode exhibits stronger fluid oscillations with respect to the horizontal mode, with fluid oscillation frequencies ranging from 0.78 up to 1 Hz. Nonetheless, the fluid motion is more stable in terms of oscillation frequency between channels when the device operates in the horizontal orientation probably due to negligible buoyancy effects. Moreover, thermal interactions between adjacent channels are found to be stronger when the oscillatory behavior presents similar features from channel to channel in horizontal orientation. The proposed method for fluid oscillation analyses in fully metallic flat-plate pulsating heat pipes can be effectively adopted to other flat-plate layouts without any need for transparent windows, thus reducing the overall complexity of experimental set-ups and providing, at the same time, a good insight into the inner fluid dynamics. Full article
(This article belongs to the Special Issue Recent Progress in Infrared Thermography)
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Figure 1
<p>Rear side of the studied FPPHP (<b>a</b>); sketch of the machined path for the working fluid and reference for the evaporator/condenser/adiabatic sections’ locations, as well as for the channels’ numeration and area framed by the MWIR camera (green box) (<b>b</b>).</p>
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<p>Investigated device orientations with respect to the gravity field.</p>
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<p>Three-dimensional sketch of a FPPHP portion within the adiabatic section; in red, areas of interest for the wall-to-fluid heat flux evaluation.</p>
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<p>Exact heat flux over time (<span class="html-italic">Amp</span> = 2000 W/m<sup>2</sup>, <span class="html-italic">f</span> = 1.2 Hz), used as input for the wavelet method (<b>a</b>); corresponding scalogram (<b>b</b>) and power spectrum (<b>c</b>).</p>
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<p>Restored heat flux, used as input for the wavelet method (<b>a</b>); corresponding scalogram (<b>b</b>) and power spectrum (<b>c</b>).</p>
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<p>Heat flux for a single axial coordinate referred to channel 8 (<span class="html-italic">z</span> = 0.02 m), <span class="html-italic">Q</span> = 100 W, horizontal orientation (<b>a</b>), corresponding scalogram (<b>b</b>) and power spectrum (<b>c</b>).</p>
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<p>Wall-to-fluid heat flux signals over time related to channel 9 and <span class="html-italic">Q</span> = 250 W for three different axial coordinates and corresponding power spectra; vertical BHM (<b>a</b>,<b>b</b>) and horizontal orientation (<b>c</b>,<b>d</b>).</p>
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<p>Wavelet scalograms for channels 4 (Ch4), 8 (Ch8) and 12 (Ch12) of the wall-to-fluid heat fluxes for the fixed axial coordinate <span class="html-italic">z</span> = 0.02 m at different heat loads to the evaporator (vertical BHM).</p>
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<p>Power spectra of the wall-to-fluid heat fluxes for the fixed axial coordinate <span class="html-italic">z</span> = 0.02 m related to every FPPHP channel at different heat loads to the evaporator (vertical BHM).</p>
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<p>Wavelet scalograms for channels 4 (Ch4), 8 (Ch8) and 12 (Ch12) of the wall-to-fluid heat fluxes for the fixed axial coordinate <span class="html-italic">z</span> = 0.02 m at different heat loads to the evaporator (horizontal orientation).</p>
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<p>Power spectra of the wall-to-fluid heat fluxes for the fixed axial coordinate <span class="html-italic">z</span> = 0.02 m related to every FPPHP channel at different heat loads to the evaporator (horizontal orientation).</p>
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<p>Scalogram of <a href="#applsci-13-06351-f010" class="html-fig">Figure 10</a>, <span class="html-italic">Ch 4</span>, having adjusted color scale around the zero value; low-power areas are present due to residual noise in the input heat flux rather than actual oscillations of the working fluid.</p>
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<p>Scalograms referred to channel 12 for the vertical BHM (<b>a</b>) and horizontal orientation (<b>b</b>), <span class="html-italic">Q</span> = 250 W; denoised scalograms (<b>c</b>,<b>d</b>) and frequency identification over time (<b>e</b>,<b>f</b>).</p>
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<p>Mean continuity time for average and high heat loads and the two considered orientations.</p>
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<p>Average activity time of channels as percentage of the observation window length.</p>
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<p>Average dominant fluid oscillation frequency in the overall device (<b>a</b>) and channel-wise standard deviation (<b>b</b>) for the two considered FPPHP orientations.</p>
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<p><math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mi>r</mi> <mi>a</mi> <mi>t</mi> <mi>i</mi> <mi>o</mi> </mrow> </msub> </mrow> </semantics></math> as a function of the power input to the evaporator for both considered device orientations.</p>
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<p>Wall-to-fluid heat fluxes corresponding to channel 7 and 8, heat flux by transverse conduction <span class="html-italic">q<sub>Fou</sub></span><sub>,7</sub><span class="html-italic"><sub>→</sub></span><sub>8</sub> (<b>a</b>), and corresponding power spectra (<b>b</b>) for the vertical BHM at <span class="html-italic">Q</span> = 200 W and <span class="html-italic">z</span> = 0.02 m.</p>
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<p>Wall-to-fluid heat fluxes corresponding to channel 7 and 8, heat flux by transverse conduction <span class="html-italic">q<sub>Fou</sub></span><sub>,7→8</sub> (<b>a</b>), and corresponding power spectra (<b>b</b>) for the horizontal orientation at <span class="html-italic">Q</span> = 200 W and <span class="html-italic">z</span> = 0.02 m.</p>
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15 pages, 2078 KiB  
Article
Objective Prediction of Human Visual Acuity Using Image Quality Metrics
by Julián Espinosa Tomás, Jorge Pérez Rodríguez, David Más Candela, Carmen Vázquez Ferri and Esther Perales
Appl. Sci. 2023, 13(10), 6350; https://doi.org/10.3390/app13106350 - 22 May 2023
Viewed by 1258
Abstract
This work addresses the objective prediction of human uncorrected decimal visual acuity, an unsolved challenge due to the contribution of both physical and neural factors. An alternative approach to assess the image quality of the human visual system can be addressed from the [...] Read more.
This work addresses the objective prediction of human uncorrected decimal visual acuity, an unsolved challenge due to the contribution of both physical and neural factors. An alternative approach to assess the image quality of the human visual system can be addressed from the image and video processing perspective. Human tolerance to image degradation is quantified by mean opinion scores, and several image quality assessment algorithms are used to maintain, control, and improve the quality of processed images. The aberration map of the eye is used to obtain the degraded theoretical image from a set of natural images. The amount of distortion added by the eye to the natural image was quantified using different image processing metrics, and the correlation between the result of each metric and subjective visual acuity was assessed. The correlation obtained for a model based on a linear combination of the normalized mean square error metric and the feature similarity index metric was very good. It was concluded that the proposed method could be an objective way to determine subjects’ monocular and uncorrected decimal visual acuity with low uncertainty. Full article
(This article belongs to the Section Biomedical Engineering)
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Figure 1
<p>Flow chart of the proposed approach.</p>
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<p>Characteristics of the studied eyes. (<b>A</b>) Type of refractive error (myopia-hyperopia). (<b>B</b>) Spherical equivalent associated with age range. (<b>C</b>) Number of subjects associated with the subjective decimal VA ranges.</p>
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<p>Subjective VA vs. the values from calculating the normalized single metrics. (<b>A</b>) <span class="html-italic">nMSE</span> (Mean Square Error Normalized), (<b>B</b>) <span class="html-italic">nPSNR</span> (Peak Signal-to-Noise Ratio Normalized), (<b>C</b>) <span class="html-italic">SSIM</span> (Structural Similarity Index), (<b>D</b>) <span class="html-italic">GMSD</span> (Gradient Magnitude Similarity Deviation), (<b>E</b>) <span class="html-italic">MSSIM</span> (Multiscale Structural Similarity Index), (<b>F</b>) FSIM (Feature Similarity Index), (<b>G</b>) <span class="html-italic">nPSNR-HVS</span> (Peak Signal-to-Noise Ratio based on the Human Visual System). Red lines represent the fitting to the logistic function (5).</p>
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<p>Subjective VA vs. the values from calculating the linear combinations of normalized single metrics. Q_L is the linear combination of <span class="html-italic">nMSE</span> with the other metrics. (<b>A</b>) linear combination <span class="html-italic">nMSE</span> (Mean Square Error Normalized) with <span class="html-italic">nPSNR</span> (Peak Signal-to-Noise Ratio Normalized), (<b>B</b>) linear combination <span class="html-italic">nMSE</span> with <span class="html-italic">SSIM</span> (Structural Similarity Index), (<b>C</b>) linear combination <span class="html-italic">nMSE</span> with <span class="html-italic">GMSD</span> (Gradient Magnitude Similarity Deviation), (<b>D</b>) linear combination <span class="html-italic">nMSE</span> with <span class="html-italic">MSSIM</span> (Multiscale Structural Similarity Index), (<b>E</b>) linear combination <span class="html-italic">nMSE</span> with <span class="html-italic">FSIM</span> (Feature Similarity Index), (<b>F</b>) linear combination <span class="html-italic">nMSE</span> with <span class="html-italic">nPSNR-HVS</span> (Peak Signal-to-Noise Ratio based on the Human Visual System). Red lines represent the fitting to the logistic function (5).</p>
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20 pages, 6864 KiB  
Article
Challenges and Recommendations for Improved Identification of Low ILUC-Risk Agricultural Biomass
by Cato Sandford, Chris Malins and Calliope Panoutsou
Appl. Sci. 2023, 13(10), 6349; https://doi.org/10.3390/app13106349 - 22 May 2023
Cited by 1 | Viewed by 1502
Abstract
The “low indirect land use change risk” (“low ILUC-risk”) concept was developed to assess whether crop-based biofuels would compete with other land uses and cause the expansion of agricultural land. At the core of low ILUC-risk is an “additionality principle”, which requires that [...] Read more.
The “low indirect land use change risk” (“low ILUC-risk”) concept was developed to assess whether crop-based biofuels would compete with other land uses and cause the expansion of agricultural land. At the core of low ILUC-risk is an “additionality principle”, which requires that biofuel feedstock receive special treatment only if it is produced over and above the business-as-usual baseline. This paper examines and tests the European Commission’s methodology for calculating the baseline for yield improvement projects, by applying it to publicly available Eurostat data at national and NUTS2 scales. We assess from a statistical perspective how variation in regional yield trends would lead to differences in the long-term outcomes of low ILUC-risk certification; we conclude that, as currently designed, the methodology would over-state the amount of additional production in some cases and could hence incentivise the diversion of crops from other uses into the biofuel sector. We introduce the terms “tailwind additionality”, “headwind additionality”, and “additionality ratchet” to characterise the phenomena which contribute to this outcome. Our results lead us to recommendations which may enhance both the attractiveness and the robustness of the low ILUC-risk system. Full article
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<p>Sketch of the key steps for low ILUC-risk certification applicants; the focus of this paper is highlighted.</p>
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<p>Crops considered in this study.</p>
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<p>Processing pipeline for the Eurostat dataset.</p>
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<p>National annual yield for winter wheat for selected countries, with best fit slopes in the legend and the FAO global average slope for comparison.</p>
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<p>Yield slope per country for winter wheat (<b>left</b>) and sunflower seed (<b>right</b>), with FAOstat global average indicated as a vertical dashed line. Note that in <a href="#applsci-13-06349-f005" class="html-fig">Figure 5</a> and subsequent figures, countries which share the same colour are distinguished from each other by an overlaid pattern. See for instance the light grey bars for Ireland, UK, and Belgium towards the bottom of the left panel of <a href="#applsci-13-06349-f005" class="html-fig">Figure 5</a>.</p>
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<p>Yield slopes per country (dots) for cereal crops, with the FAOstat global average indicated for each. Note that countries are labelled by the two-letter country code; see <a href="#app1-applsci-13-06349" class="html-app">Annex D of the Supplementary Material</a>.</p>
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<p>Yield slopes per country (dots) for oil crops, with the FAOstat global average indicated for each.</p>
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<p>Yield slopes of winter wheat, for districts within each country. Note that this is a standard box-and-whisker plot. For each country, it indicates the median among districts (vertical orange lines), quartiles (coloured boxes), and range (whiskers). The global average yield slope is marked for reference (vertical dashed line). Here and in plots that follow, we eliminate districts with very small land area dedicated to a crop, as these may unduly skew the statistics.</p>
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<p>Yield slopes of rapeseed, for districts within each country. See the note below <a href="#applsci-13-06349-f008" class="html-fig">Figure 8</a> for more description.</p>
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<p>Above-baseline production for hypothetical Austrian low ILUC-risk projects which grow winter wheat and gain certification in 2012; the yield patterns are assumed to follow the district average. Note that each panel in this figure corresponds to a different NUTS2 district within Austria (AT); the district ID is indicated in the panel legend, along with the average above-baseline production in parentheses. For each district, three quantities are plotted: the district’s annual historical yield (solid line); the national historical yield (dotted line—these values are the same in each panel); and the calculated dynamic yield baseline for the district (black dashed line). Units are t/ha/yr.</p>
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<p>Above-baseline production for hypothetical Hungarian low ILUC-risk projects which grow winter wheat and gain certification in 2012; the yield patterns are assumed to follow the district average. See the caption for <a href="#applsci-13-06349-f010" class="html-fig">Figure 10</a> for further elaboration.</p>
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<p>Summary of new terms developed in this study.</p>
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<p>District-wise above-baseline yield for cereal crops in 2018, assuming low ILUC-risk certification in 2015, one column per district. Note that while each column corresponds to a NUTS2 district, the number of district labels on the x-axis has been reduced for visibility. Only districts with positive above-baseline yield are shown; the number of such districts as a fraction of the total in the dataset is indicated as an annotation in each panel.</p>
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<p>Yield data for winter wheat in district AT31, showing the two baseline options in black.</p>
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<p>Cumulative above-baseline yield (growing over 10 years), starting in the certification year. Note that the cumulative total at the end of the certification period is annotated for each case. The genuinely additional cumulative production is indicated in the lower-right and is rotated for distinction.</p>
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<p>Total additional yield of winter wheat in district AT31 (Austria) over the ten-year certification period, split into genuine and spurious improvement. Note that the vertical axis starts above zero in order to magnify the above-baseline portion.</p>
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18 pages, 646 KiB  
Article
Exploring the Antioxidant, Antidiabetic, and Antimicrobial Capacity of Phenolics from Blueberries and Sweet Cherries
by Ana C. Gonçalves, Ana R. Nunes, Sara Meirinho, Miguel Ayuso-Calles, Rocío Roca-Couso, Raúl Rivas, Amílcar Falcão, Gilberto Alves, Luís R. Silva and José David Flores-Félix
Appl. Sci. 2023, 13(10), 6348; https://doi.org/10.3390/app13106348 - 22 May 2023
Cited by 6 | Viewed by 2836
Abstract
(1) Background: Nowadays, special attention has been paid to red and purple fruits, including blueberries and sweet cherries, since they are highly attractive to consumers due to their organoleptic properties, standing out due to their vibrant red and purple colours and sweet flavour, [...] Read more.
(1) Background: Nowadays, special attention has been paid to red and purple fruits, including blueberries and sweet cherries, since they are highly attractive to consumers due to their organoleptic properties, standing out due to their vibrant red and purple colours and sweet flavour, and nutritional value. (2) Methods: The present study evaluated the phenolic profile of phenolic-enriched extracts from blueberries and sweet cherries and explored their antioxidant potential against DPPH, superoxide and nitric oxide radicals, and ferric species, and their potential to inhibit the α-glucosidase enzyme. Furthermore, their antimicrobial activity was also determined by microdilution method against four Gram-positive strains (Enterococcus faecalis ATCC 29212, Bacillus cereus ATCC 11778, Listeria monocytogenes LMG 16779, and Staphylococcus aureus ATCC 25923) and five Gram-negative strains (Salmonella enterica subsp. enterica ATCC 13311 serovar Typhimurium, Klebsiella pneumoniae ATCC 13883, Proteus mirabilis CECT 170, Serratia marcescens CECT 159, and Acinetobacter baumannii LMG 1025). (3) Results: By chromatographic techniques, eight anthocyanins were detected in blueberry coloured fraction and total extract, and five anthocyanins were detected in sweet cherry total extract and coloured fraction, while quercetin aglycone and chlorogenic acids were the dominant non-coloured compounds in blueberries and sweet cherries, respectively. All extracts demonstrated significant antioxidant properties, as well as the ability to inhibit the activity of α-glucosidase enzyme and the development of various microorganisms. (4) Conclusion: The obtained data evidence the promising biological potential of blueberries and sweet cherries, being highly correlated with the presence of phenolic compounds. Full article
(This article belongs to the Special Issue Antibacterial Activity of Plant Extracts)
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<p>Image of the samples studied in this work (image courtesy of the authors).</p>
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12 pages, 4330 KiB  
Article
Centrifugal Model Study of Seepage and Seismic Behavior in a Homogeneous Reservoir Dam with Parapet
by Young-Hak Lee, Soichiro Yamakawa, Tetsuo Tobita, Hyuk-Kee Hong, Hyo-Sung Song, Jae-Jung Kim and Dal-Won Lee
Appl. Sci. 2023, 13(10), 6347; https://doi.org/10.3390/app13106347 - 22 May 2023
Cited by 3 | Viewed by 1318
Abstract
This study examines the effectiveness of parapets in preventing overtopping failures of small-scale homogeneous reservoir dams under seismic loads. In this study, a parapet covered the entire width of the dam crest and was designed to ensure its weight is transmitted to the [...] Read more.
This study examines the effectiveness of parapets in preventing overtopping failures of small-scale homogeneous reservoir dams under seismic loads. In this study, a parapet covered the entire width of the dam crest and was designed to ensure its weight is transmitted to the dam crest. The test included four modes: initial mode, first seepage, seismic, and second seepage. The results show that without parapets the crack length and width expand significantly in the dam crest during the seismic mode, and the effect was large in the second seepage mode. The crack depth increased by 11.3–24 times during the seismic mode and expanded up to 73.3% of the dam height in the longitudinal direction along the axis of the crack formed in the dam crest during the second seepage mode. These findings suggest that the earthquake weakened the dam body, making it vulnerable to penetration. In contrast, the parapet structure effectively suppressed most of the tensile cracks by increasing the constraint force. Additionally, no crack expansion or tearing occurred during the second seepage mode post-earthquake, indicating improved seismic performance and suppression of seepage deformation. Full article
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<p>Relationship between rainfall and reservoir failure (Korea).</p>
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<p>Scenario setting.</p>
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<p>Design conditions and cross-sectional drawings for the model dam.</p>
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<p>Grain size distribution of construction material.</p>
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<p>Input seismic motion.</p>
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<p>EMB model deformation: (<b>a</b>) initial mode, (<b>b</b>) 1st seepage mode, (<b>c</b>) seismic mode, and (<b>d</b>) 2nd seepage mode (unit: mm).</p>
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<p>Parapet model deformation: (<b>a</b>) initial mode, (<b>b</b>) 1st seepage mode, (<b>c</b>) seismic mode, and (<b>d</b>) 2nd seepage mode (unit: mm).</p>
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<p>Displacement comparison in the EMB model and parapet model: (<b>a</b>) 1st seepage mode, (<b>b</b>) seismic mode, and (<b>c</b>) 2nd seepage mode.</p>
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<p>EMB model: (<b>a</b>) initial mode, (<b>b</b>) 1st seepage mode, (<b>c</b>) seismic mode, and (<b>d</b>) 2nd seepage mode (unit: mm).</p>
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<p>Parapet model: (<b>a</b>) initial mode, (<b>b</b>) 1st seepage mode, (<b>c</b>) seismic mode, and (<b>d</b>) 2nd seepage mode (unit: mm).</p>
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<p>Dam crest crack size: (<b>a</b>) crack length, (<b>b</b>) crack depth, and (<b>c</b>) crack width. IM: initial mode; FSM: 1st seepage mode; EM: seismic mode; SSM: 2nd seepage mode.</p>
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<p>Comparison of pore water pressure between the EMB and parapet models: (<b>a</b>) 1st seepage mode and (<b>b</b>) 2nd seepage mode.</p>
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18 pages, 899 KiB  
Article
Behavioral Indicator-Based Initial Flight Training Competency Assessment Model
by Hong Sun, Fangquan Yang, Peiwen Zhang and Qingqing Hu
Appl. Sci. 2023, 13(10), 6346; https://doi.org/10.3390/app13106346 - 22 May 2023
Cited by 2 | Viewed by 2372
Abstract
Ensuring training safety is paramount to flight schools. In response to the inadequacy of traditional flight training assessment for comprehensive quantitative evaluation of cadet competency, an initial flight training competency assessment standard based on behavioral indicators was developed and optimized using the VENN [...] Read more.
Ensuring training safety is paramount to flight schools. In response to the inadequacy of traditional flight training assessment for comprehensive quantitative evaluation of cadet competency, an initial flight training competency assessment standard based on behavioral indicators was developed and optimized using the VENN model. Firstly, the Assessor Score Measurement Form (ASMF) was constructed according to the requirements of the Training Evaluation Worksheet specification, such as typical subjects, observations, and completion criteria. Secondly, based on the basic principles of the experience of the flight expert and the Competency-Based Training and Assessment (CBTA), a matrix of correlations between the observations and each competency-based behavioral indicator was created to construct a competency assessment matrix. In addition, a two-dimensional model for representing competency items characterized by behavioral indicators was established and an optimization model for competency assessment criteria was constructed. Finally, through combining actual flight training data, the proposed method was validated in the flight screening check phase. The results show that the optimized flight training competency assessment scheme can be well quantified and matched to real instructor ratings with an accuracy of 84%. The assessment worksheet, the assessment matrix, and the VENN competency rating model can be adapted to the different teaching requirements of each flight phase, achieving a perfect match between the behavioral indicators and the competency items, which is highly versatile. The proposed model can more accurately reflect the core competencies of flight trainees, enable quantitative assessment of behavioral indicators and competency items, and provide support for subsequent training of trainees. Full article
(This article belongs to the Special Issue Research on Aviation Safety)
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<p>Initial flight training CBTA evaluation process.</p>
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<p><span class="html-italic">OB</span> rating vs. examiner rating analysis.</p>
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17 pages, 6935 KiB  
Article
Center-to-Center Distance’s Effect between Vertical Square Tubes of a Horizontal Array on Natural Convection Heat Transfer
by Zeyad Alsuhaibani, Mohamed Ali and Nader S. Saleh
Appl. Sci. 2023, 13(10), 6345; https://doi.org/10.3390/app13106345 - 22 May 2023
Cited by 1 | Viewed by 1379
Abstract
An experimental study on natural convection heat transfer from the outer surface of a horizontal array of vertical square tubes in the air is investigated. The array consists of three vertical square tubes at equally different center-to-center distances. Each tube has a square [...] Read more.
An experimental study on natural convection heat transfer from the outer surface of a horizontal array of vertical square tubes in the air is investigated. The array consists of three vertical square tubes at equally different center-to-center distances. Each tube has a square cross-section with a side length of 2.00 cm, 100 cm length, and is filled with sand. Each tube is heated by inserting an internal heating element with a constant heat flux at the center. Five center-to-center separation distance to hydraulic diameter ratios (S/D) are used at different heat flux ranges of 70–360 W/m2. Results show that at small S/D, the Nusselt number of any tube in the array is lower than that of the single tube up to a specific S/D and then increases as the ratio increases. Empirical correlations are obtained for each tube in the array at different S/D using the modified Rayleigh numbers only. General correlations using S/D as a parameter are obtained for each tube, and an overall general correlation using both S/D and the tube number (n) as parameters is obtained. The difference between the predicted and experimental Nusselt numbers is in the reasonable range even at high Rayleigh numbers. Full article
(This article belongs to the Topic Applied Heat Transfer)
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<p>Experimental setup; (<b>a</b>) schematic and (<b>b</b>) real photo.</p>
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<p>Specification of each tube; (<b>a</b>) cross-section showing the internal heating element at the center and (<b>b</b>) thermocouple locations along each tube.</p>
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<p>Local circumference average normalized temperatures along the vertical surface of the tube at different heat fluxes.</p>
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<p>Steady-state temperature profiles at the outer surface of the tubes at three different supplied powers.</p>
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<p>Temperature measurements in the middle of the tube at different heat fluxes at the three circumference surfaces: front (F), left (L), and right (R); (<b>a</b>) 108.0 W/m<sup>2</sup>, (<b>b</b>) 221.0 W/m<sup>2</sup>, and (<b>c</b>) 341.0 W/m<sup>2</sup>.</p>
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<p>Temperature measurements in the middle of the tube at different heat fluxes at the three circumference surfaces: front (F), left (L), and right (R); (<b>a</b>) 108.0 W/m<sup>2</sup>, (<b>b</b>) 221.0 W/m<sup>2</sup>, and (<b>c</b>) 341.0 W/m<sup>2</sup>.</p>
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<p>Thermocouple calibration against a platinum resistance thermometer using samples of seven thermocouples.</p>
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<p>Comparison of natural convection heat transfer from the used single tubes and that of Vliet and Liu [<a href="#B1-applsci-13-06345" class="html-bibr">1</a>] and Ali [<a href="#B2-applsci-13-06345" class="html-bibr">2</a>].</p>
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<p>The effect of a small center-to-center distance ratio on the natural convection heat transfer from the left tube in an array of three tubes; (<b>a</b>) <span class="html-italic">S</span>/<span class="html-italic">D</span> = 1.75 and (<b>b</b>) <span class="html-italic">S</span>/<span class="html-italic">D</span> = 2.75.</p>
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<p>Local Nusselt numbers versus the modified Rayleigh numbers for different <span class="html-italic">S</span>/<span class="html-italic">D</span> ratios of the left tube in the array of three tubes compared to that of the single tube.</p>
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<p>Local Nusselt numbers versus the modified Rayleigh numbers for different <span class="html-italic">S</span>/<span class="html-italic">D</span> ratios corresponding to the middle and right tube compared to that of a single tube; number 2 in (<b>a</b>), and number 3 in (<b>b</b>).</p>
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<p>Local Nusselt numbers versus <span class="html-italic">S</span>/<span class="html-italic">D</span> ratios for different modified Rayleigh numbers; tube number 1 in (<b>a</b>), tube number 2 in (<b>b</b>), and tube number 3 in (<b>c</b>).</p>
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<p>Local Nusselt numbers versus <span class="html-italic">S</span>/<span class="html-italic">D</span> ratios for different modified Rayleigh numbers; tube number 1 in (<b>a</b>), tube number 2 in (<b>b</b>), and tube number 3 in (<b>c</b>).</p>
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<p>The percentage of degradation and enhancement of Nu<sub>x</sub> versus <span class="html-italic">S</span>/<span class="html-italic">D</span> for different Rayleigh numbers for the different tubes in the array; (<b>a</b>) tube number 1, (<b>b</b>) tube number 2, and (<b>c</b>) tube number 3.</p>
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<p>Differences between the predicted and the experimental Nusselt numbers using <span class="html-italic">S</span>/<span class="html-italic">D</span> as a parameter; (<b>a</b>) tube number 1, (<b>b</b>) tube number 2, and (<b>c</b>) tube number 3.</p>
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<p>Differences between the predicted and the experimental Nusselt numbers using <span class="html-italic">S</span>/<span class="html-italic">D</span> and tube number (<span class="html-italic">n</span>) as parameters for the array.</p>
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15 pages, 18063 KiB  
Article
Geometric Error Parameterization of a CMM via Calibrated Hole Plate Archived Utilizing DCC Formatting
by Ming-Xian Lin and Tsung-Han Hsieh
Appl. Sci. 2023, 13(10), 6344; https://doi.org/10.3390/app13106344 - 22 May 2023
Cited by 1 | Viewed by 1704
Abstract
This study implemented the measurement results and administrative information obtained from the hole plate into the Digital Calibration Certificate (DCC). The DCC comprises three parts: Norms and Standards, Hierarchical Structure, and XML as Exchange Format. DCCs play a significant role in the field [...] Read more.
This study implemented the measurement results and administrative information obtained from the hole plate into the Digital Calibration Certificate (DCC). The DCC comprises three parts: Norms and Standards, Hierarchical Structure, and XML as Exchange Format. DCCs play a significant role in the field of metrology and statistics by ensuring data interoperability, correctness, and traceability during the conversion and transmission process. The hole plate is a length standard used for two-dimensional geometric error measurements. We evaluated the accuracy of the high-precision coordinate measuring machine (CMM) in measuring a hole plate and compared the measurement error results obtained from the hole plate with those of the laser interferometer, autocollimator, and angle square. The results show that the maximum difference in linear error is −0.30 μm, the maximum difference in angle error is −0.78″, and the maximum difference in squareness error is 4.54″. The XML is designed for machine-readability and is modeled and edited using the XMLSpy 2022 software, which is based on information published by PTB. The administrative management and measurement results tasks are presented in PDF format, which is designed for human-readability and ease of use. Overall, we implemented the measurement results and information obtained from the hole plate into the DCC. Full article
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<p>The schematic structure of the digital calibration certificate [<a href="#B9-applsci-13-06344" class="html-bibr">9</a>].</p>
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<p>Schematic of element type definitions in the DCC [<a href="#B28-applsci-13-06344" class="html-bibr">28</a>,<a href="#B38-applsci-13-06344" class="html-bibr">38</a>,<a href="#B39-applsci-13-06344" class="html-bibr">39</a>].</p>
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<p>Administrative information of CMM in XML type.</p>
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<p>The length standard known as the hole plate.</p>
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<p>Schematic diagram of the hole plate measurement in different direction (<b>a</b>) XY plane, (<b>b</b>) YZ plane, (<b>c</b>) XZ plane.</p>
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<p>(<b>a</b>) Experimental setup of the autocollimator (<b>b</b>) Experimental setup of the laser interferometer (<b>c</b>) Experimental setup of the angle square.</p>
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<p>(<b>a</b>) Measurement results for positioning error of the CMM using the hole plate and laser interferometer; (<b>b</b>) Measurement results for angular error of the CMM using the hole plate and autocollimator.</p>
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<p>Measurement results of (<b>a</b>) <span class="html-italic">X</span>-axis, (<b>b</b>) <span class="html-italic">Y</span>-axis, (<b>c</b>) <span class="html-italic">Z</span>-axis, and (<b>d</b>) Squareness error of CMM in XML type.</p>
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<p>The DCC implementation of administrative and measurement information of CMM in PDF.</p>
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18 pages, 379 KiB  
Article
Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
by Huifeng Fan, Ruwei Huang and Fengting Luo
Appl. Sci. 2023, 13(10), 6343; https://doi.org/10.3390/app13106343 - 22 May 2023
Cited by 1 | Viewed by 1273
Abstract
Aiming at the problem that the fully homomorphic encryption scheme based on single identity cannot satisfy the homomorphic operation of ciphertext under different identities, as well as the inefficiency of trapdoor function and the complexity of sampling algorithm, an improved lattice MIBFHE scheme [...] Read more.
Aiming at the problem that the fully homomorphic encryption scheme based on single identity cannot satisfy the homomorphic operation of ciphertext under different identities, as well as the inefficiency of trapdoor function and the complexity of sampling algorithm, an improved lattice MIBFHE scheme was proposed. Firstly, we combined MP12 trapdoor function with dual LWE algorithm to construct a new IBE scheme under the standard model, and prove that the scheme is IND-sID-CPA security under the selective identity. Secondly, we used the eigenvector method to eliminate the evaluation key, and transform the above efficient IBE scheme into a single identity IBFHE scheme to satisfy the homomorphic operation. Finally, we improved the ciphertext extension method of CM15 and constructed a new Link-mask system that supports the transformation of IBFHE scheme under the standard model, and then, converted the above IBFHE scheme into MIBFHE scheme based on this system. The comparative analysis results showed that the efficiency of this scheme is improved compared with similar schemes in the trapdoor generation and preimage sampling, and the dimension of lattice and ciphertext size are significantly shortened. Full article
28 pages, 1917 KiB  
Article
VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm
by Yiming Tang, Rui Chen and Bowen Xia
Appl. Sci. 2023, 13(10), 6342; https://doi.org/10.3390/app13106342 - 22 May 2023
Cited by 2 | Viewed by 1328
Abstract
Nowadays, most fuzzy clustering algorithms are sensitive to the initialization results of clustering algorithms and have a weak ability to handle high-dimensional data. To solve these problems, we developed the viewpoint-driven subspace fuzzy c-means (VSFCM) algorithm. Firstly, we propose a new cut-off distance. [...] Read more.
Nowadays, most fuzzy clustering algorithms are sensitive to the initialization results of clustering algorithms and have a weak ability to handle high-dimensional data. To solve these problems, we developed the viewpoint-driven subspace fuzzy c-means (VSFCM) algorithm. Firstly, we propose a new cut-off distance. Based on this, we establish the cut-off distance-induced clustering initialization (CDCI) method and use it as a new strategy for cluster center initialization and viewpoint selection. Secondly, by taking the viewpoint obtained by CDCI as the entry point of knowledge, a new fuzzy clustering strategy driven by knowledge and data is formed. Based upon these, we put forward the VSFCM algorithm combined with viewpoints, separation terms, and subspace fuzzy feature weights. Moreover, compared with the symmetric weights obtained by other subspace clustering algorithms, the weights of the VSFCM algorithm exhibit significant asymmetry. That is, they assign greater weights to features that contribute more, which is validated on the artificial dataset DATA2 in the experimental section. The experimental results compared with multiple advanced clustering algorithms on the three types of datasets validate that the proposed VSFCM algorithm has the best performance in five indicators. It is demonstrated that the initialization method CDCI is more effective, the feature weight allocation of VSFCM is more consistent with the asymmetry of experimental data, and it can achieve better convergence speed while displaying better clustering efficiency. Full article
(This article belongs to the Special Issue Fuzzy Control Systems: Latest Advances and Prospects)
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<p>The idea of the proposed VSFCM algorithm.</p>
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<p>The flowchart of the initial method CDCI.</p>
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<p>The flowchart of the clustering algorithm VSFCM.</p>
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<p>DATA1 data distribution map.</p>
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<p>(<b>a</b>) DATA2 data distribution map. (<b>b</b>) DATA2: x-y dimensional data distribution map. (<b>c</b>) DATA2: x-z data distribution map. (<b>d</b>) DATA2: y-z data distribution map.</p>
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<p>Breast Cancer data (Sammon mapping).</p>
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<p>(<b>a</b>) CDCI <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>−</mo> <mi>δ</mi> </mrow> </semantics></math> distribution map. (<b>b</b>) HDCCI <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>−</mo> <mi>δ</mi> </mrow> </semantics></math> distribution map.</p>
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<p>Histogram of the Olivetti face database running results.</p>
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17 pages, 6928 KiB  
Article
Computational Dynamics of Multi-Rigid-Body System in Screw Coordinate
by Jing-Shan Zhao, Song-Tao Wei and Xiao-Cheng Sun
Appl. Sci. 2023, 13(10), 6341; https://doi.org/10.3390/app13106341 - 22 May 2023
Cited by 2 | Viewed by 1760
Abstract
This paper investigates the kinematics and dynamics of multi-rigid-body systems in screw form. The Newton–Euler dynamics equations are established in screw coordinates. All forces and torques of the multi-rigid-body system can be solved straightforwardly since they are explicit in the form of screw [...] Read more.
This paper investigates the kinematics and dynamics of multi-rigid-body systems in screw form. The Newton–Euler dynamics equations are established in screw coordinates. All forces and torques of the multi-rigid-body system can be solved straightforwardly since they are explicit in the form of screw coordinates. The displacement and acceleration are unified in matrix form, which associates the kinematics and dynamics with variable of velocity. A one-step numerical algorithm only is needed to solve the displacements and accelerations. As a result, all absolute displacements, velocities, and accelerations are directly obtained by one kinematic equation. The kinematics and dynamics of Gough–Stewart platform validate this the method. In this paper, the kinematics and dynamics are carried out with the example of a Gough–Stewart platform, which represents the most complex multi-rigid-body system, to verify the computational dynamics method. The proposed algorithm is also fit for the kinematics and dynamics modeling of other multi-rigid-body systems. Full article
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<p>A multi-rigid-body system: (<b>a</b>) multi-rigid-body system; (<b>b</b>) a serial kinematic chain.</p>
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<p>The procedure for the kinematics on multi-rigid-body system.</p>
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<p>Newton-Euler parameters.</p>
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<p>Kinematics analysis of the Gough–Stewart platform: (<b>a</b>) kinematics of the Gough–Stewart platform; (<b>b</b>) initial condition of the first kinematic chain.</p>
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<p>Dynamic analysis of the Gough–Stewart platform: (<b>a</b>) dynamics of the first kinematic chain; (<b>b</b>) dynamics of the fixed leg <math display="inline"><semantics> <mrow> <msubsup> <mi>L</mi> <mrow> <mi>A</mi> <mi>B</mi> </mrow> <mi>p</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>⋯</mo> <mn>6</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>; (<b>c</b>) dynamics of the moving leg <math display="inline"><semantics> <mrow> <msubsup> <mi>L</mi> <mrow> <mi>B</mi> <mi>C</mi> </mrow> <mi>p</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>⋯</mo> <mn>6</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>Kinematics analysis of the Gough–Stewart platform.</p>
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<p>Relative displacements of the joints in each link: (<b>a</b>) relative angular displacements; (<b>b</b>) relative linear displacements.</p>
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<p>Relative velocities of each joint in each link: (<b>a</b>) relative angular velocities; (<b>b</b>) relative linear velocities.</p>
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<p>Absolute angular velocities of all legs: (<b>a</b>) absolute angular velocities of the fixed legs <math display="inline"><semantics> <mrow> <msubsup> <mi>L</mi> <mrow> <mi>A</mi> <mi>B</mi> </mrow> <mi>p</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>⋯</mo> <mn>6</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>; (<b>b</b>) absolute angular velocities of the moving legs <math display="inline"><semantics> <mrow> <msubsup> <mi>L</mi> <mrow> <mi>B</mi> <mi>C</mi> </mrow> <mi>p</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>⋯</mo> <mn>6</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>Absolute accelerations of the fixed legs <math display="inline"><semantics> <mrow> <msubsup> <mi>L</mi> <mrow> <mi>A</mi> <mi>B</mi> </mrow> <mi>p</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>⋯</mo> <mn>6</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> at their individual mass centers: (<b>a</b>) absolute linear accelerations of the mass centers of the fixed legs; (<b>b</b>) absolute angular accelerations of the fixed legs.</p>
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<p>Absolute accelerations of the moving legs <math display="inline"><semantics> <mrow> <msubsup> <mi>L</mi> <mrow> <mi>B</mi> <mi>C</mi> </mrow> <mi>p</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>⋯</mo> <mn>6</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> at their respective mass centers: (<b>a</b>) absolute linear accelerations of the mass centers of the moving legs; (<b>b</b>) absolute angular accelerations of the moving legs.</p>
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<p>Resultant driving force of each prismatic joint <math display="inline"><semantics> <mrow> <msubsup> <mi>j</mi> <mi>B</mi> <mi>p</mi> </msubsup> <mo>,</mo> <mrow> <mo>(</mo> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>⋯</mo> <mo>,</mo> <mn>6</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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11 pages, 2499 KiB  
Article
Valorization of Cassava By-Products: Cyanide Content and Quality Characteristics of Leaves and Peel
by Adnan Mukhtar, Sajid Latif, Ziba Barati and Joachim Müller
Appl. Sci. 2023, 13(10), 6340; https://doi.org/10.3390/app13106340 - 22 May 2023
Cited by 3 | Viewed by 2270
Abstract
Cassava production generates significant amounts of by-products such as leaves and tuber peel. Instead of considering them as waste, valorization aims to find sustainable ways to utilize them. However, the presence of cyanide and insoluble fibers poses a major obstacle to their conversion [...] Read more.
Cassava production generates significant amounts of by-products such as leaves and tuber peel. Instead of considering them as waste, valorization aims to find sustainable ways to utilize them. However, the presence of cyanide and insoluble fibers poses a major obstacle to their conversion into valuable products. Therefore, the objective of this study is to investigate the changes in cyanide concentration and quality of cassava leaves after mechanical pressing and in tuber peel after treatment with an enzyme solution. Frozen leaves were screw-pressed into their fractions: juice, and press cake. The results show that the cyanide level in the press cake was reduced to 73.56% and was concentrated by 97.48% in the juice compared to the frozen leaves. However, the crude protein values of the frozen leaves, juice, and press cake did not differ significantly (p > 0.05), and were 27.09%, 25.47%, and 23.82%, respectively. In addition, the results for the peel revealed that pretreatment with Viscozyme® L, which assists in the mechanical peeling of cassava tubers, also contributed to a reduction in cyanide and insoluble fiber in the peel. Cyanide content was lowered by 53.89–58.94% in enzyme-treated peel from all three runs (ETP1-3) when compared to fresh peel (FP), while the reduction was only 8.63% in the control peel (CP) treated with hot water without enzyme solution. The insoluble fibers in cassava peel, such as neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude fiber (CF), were also degraded more effectively after treatment with an enzyme solution than with hot water. Full article
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<p>Screw pressing of cassava leaves into their fractions: juice, and press cake.</p>
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<p>Viscozyme<sup>®</sup> L pretreatment and mechanical peeling of cassava tubers.</p>
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<p>Dry matter, organic matter, ash, crude protein, and cyanide content of frozen cassava leaves, juice and press cake. Different letters for the same parameter represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Dry matter, organic matter, ash, neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), crude fiber (CF), and cyanide content of fresh cassava tuber peel (FP), control peel (CP) treated with hot water without enzyme solution, and enzyme-treated peel (ETP1-3) obtained after three separate runs with the same enzyme solution. Different letters for the same parameters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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32 pages, 7857 KiB  
Article
New Game Artificial Intelligence Tools for Virtual Mine on Unreal Engine
by Fares Abu-Abed and Sergey Zhironkin
Appl. Sci. 2023, 13(10), 6339; https://doi.org/10.3390/app13106339 - 22 May 2023
Cited by 7 | Viewed by 4827
Abstract
Currently, the gamification of virtual reality for training miners, especially for emergencies, and designing the extraction of minerals in difficult technological conditions has been embodied in the Virtual Mine software and hardware. From a software development point of view, Virtual Mine is indistinguishable [...] Read more.
Currently, the gamification of virtual reality for training miners, especially for emergencies, and designing the extraction of minerals in difficult technological conditions has been embodied in the Virtual Mine software and hardware. From a software development point of view, Virtual Mine is indistinguishable from other virtual reality games, and this offers a chance to use the potential of rapidly developing game software in mining, including engines, 3D modeling tools, audio editors, etc., to solve a wide range of game development tasks. The chosen direction will optimize the work of developers by providing a tool for developing game artificial intelligence to solve problems that require implementing the behavior of game agents without using a rigidly defined choice of scenarios or chains of these scenarios. The aim of the work is to expand the possibilities of working with game artificial intelligence on the Unreal Engine game engine to make it more functional. As a result, a tool has been obtained that can be used to optimize the time and improve the quality of the development of game artificial intelligence for Virtual Mine using flexible development approaches. The asset editor was developed, application modes and their working tabs were defined, and a graphical node system for the behavioral graph editor was created. A system for executing a behavioral graph is given; algorithms for its operation and features for executing nodes of a behavioral graph are presented. Full article
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<p>Simulation of actions in Virtual Mine on the ProExpVR platform: (<b>A</b>) preparation for blasting; (<b>B</b>) remote control of the mine loader; (<b>C</b>) installation of an explosion initiation network; (<b>D</b>) mine transport accident model (Reprinted from Ref. [<a href="#B32-applsci-13-06339" class="html-bibr">32</a>]).</p>
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<p>Game virtual 3D models of coal and ore mine accidents (ProExpVR platform): (<b>A</b>) trolley hooking and dragging; (<b>B</b>) roof collapse; (<b>C</b>) fall from a height; (<b>D</b>) ignition during welding (Reprinted from Refs. [<a href="#B32-applsci-13-06339" class="html-bibr">32</a>,<a href="#B33-applsci-13-06339" class="html-bibr">33</a>]).</p>
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<p>Coal firefighting training in an underground mine using Virtual Mine VR Professionals: (<b>A</b>) approaching the source of fire; (<b>B</b>) preparing the fire extinguisher; (<b>C</b>,<b>D</b>) fire extinguishing from different angles; (<b>E</b>) leaving work (Reprinted from Ref. [<a href="#B33-applsci-13-06339" class="html-bibr">33</a>]).</p>
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<p>The structure of the program modules of the Virtual Mine system on the Kinect gaming platform (Reprinted from Ref. [<a href="#B35-applsci-13-06339" class="html-bibr">35</a>]).</p>
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<p>Construction and use of a virtual mine model using Kinect for Microsoft Xbox: (<b>A</b>) a three-dimensional model of a mine working in AutoCAD format; (<b>B</b>) transferring digital avatar control gestures using Kinect; (<b>C</b>) game visualization of the mine working model after Blender processing; (<b>D</b>) an example of driving through a mine working based on the PhysX game engine (Reprinted from Refs. [<a href="#B34-applsci-13-06339" class="html-bibr">34</a>,<a href="#B35-applsci-13-06339" class="html-bibr">35</a>]).</p>
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<p>Application of gaming virtual reality based on Godot engine for teaching students at the University of Žilina (Reprinted from Ref. [<a href="#B36-applsci-13-06339" class="html-bibr">36</a>]).</p>
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<p>Unreal Engine Module Structure.</p>
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<p>Various Unreal Engine Menu Bars and Toolbars.</p>
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<p>Menu Builder Inheritance Hierarchy.</p>
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<p>Menu expansion process.</p>
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<p>FAssetEditorToolkit block diagram.</p>
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<p>Block diagram of FWorkflowCentricApplication.</p>
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<p>Utility System asset editor tab structure.</p>
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<p>Blackboard tab layout.</p>
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<p>Graphical representation of select and task nodes.</p>
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<p>Scheme of the device of the graphic system of nodes.</p>
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<p>Graph context menu.</p>
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<p>Handling an invalid connection.</p>
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<p>Connection rendering policy trunk.</p>
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<p>Slate tree of node elements.</p>
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<p>The structure of the Slate elements of a node.</p>
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<p>Highlighting the contour of connected active and inactive nodes.</p>
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<p>Root node.</p>
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<p>Computing utility estimates.</p>
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12 pages, 4347 KiB  
Article
Yb-Doped All-Fiber Amplifier with Low-Intensity Noise in mHz Range Oriented to Space-Borne Gravitational Wave Detection
by Zaiyuan Wang, Jiehao Wang, Fan Li, Yuhang Li, Long Tian and Qiang Liu
Appl. Sci. 2023, 13(10), 6338; https://doi.org/10.3390/app13106338 - 22 May 2023
Cited by 1 | Viewed by 1459
Abstract
We present a low-intensity noise single-frequency Yb-doped all-fiber amplifier oriented to space-borne gravitational wave detection. Relative intensity noise (RIN) below −70 dBc/Hz @ 1 mHz~1 Hz was achieved by virtue of feedback-loop-based intensity noise suppression. Based on systematic noise analysis and experimental investigation, [...] Read more.
We present a low-intensity noise single-frequency Yb-doped all-fiber amplifier oriented to space-borne gravitational wave detection. Relative intensity noise (RIN) below −70 dBc/Hz @ 1 mHz~1 Hz was achieved by virtue of feedback-loop-based intensity noise suppression. Based on systematic noise analysis and experimental investigation, we found that the pump noise and temperature-dependent noise of the fiber splitter and the photodetector contributed mainly to the RIN of the fiber amplifier. Therefore, we carefully designed a feedback-loop-based Yb-doped all-fiber amplifier, and finely stabilized the temperature of the pump diode, fiber splitters, and photodetectors. Consequently, the RIN can be suppressed down to −72.5 dBc/Hz around 1 mHz. This low-intensity all-fiber Yb-doped amplifier can be used for space-borne gravitational-wave detection. Full article
(This article belongs to the Special Issue Advances in Optical and Optoelectronic Devices and Systems)
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<p>Schematic for feedback-loop-based intensity noise suppression in the fiber amplifier.</p>
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<p>Schematic for the low noise fiber amplifier. ISO: isolator, YDF: Yb-doped fiber, CPS: cladding power stripper, MM Pump LD: multi-mode pump laser diode. Red line and black line represent laser signal path and electrical signal path respectively.</p>
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<p>Output power and slope efficiency measured versus pump current of the pump diode.</p>
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<p>Optical spectrum of the fiber amplifier at an output power of 2.5 W. Inset: Optical spectra of the seed and amplifier.</p>
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<p>Experimental setup for the additional linewidth measurement.</p>
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<p>Additional linewidth measurement results of the amplified laser.</p>
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<p>RIN of the amplifier output.</p>
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<p>Measurements of temperature fluctuations and voltage fluctuations.</p>
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<p>Measurements of the voltage fluctuations with temperature stabilization of the fiber splitters.</p>
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<p>Measurements of the RIN with temperature stabilization of the fiber splitters.</p>
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<p>(<b>a</b>) Photograph of the packaged photodetector; (<b>b</b>) Screen of the detailed parameters in SLICE-QTC.</p>
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<p>(<b>a</b>) Voltage fluctuations of the fiber splitter, pump LD, in-loop and out-of-loop photodetectors; (<b>b</b>) RIN results measured by G12180-130A and DET01CFC.</p>
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25 pages, 7418 KiB  
Article
Application of Cluster Analysis for Classification of Vibration Signals from Drilling Stand Aggregates
by Patrik Flegner, Ján Kačur, Rebecca Frančáková, Milan Durdán and Marek Laciak
Appl. Sci. 2023, 13(10), 6337; https://doi.org/10.3390/app13106337 - 22 May 2023
Cited by 4 | Viewed by 1283
Abstract
Rotary drilling technology with diamond tools is still essential in progressively extracting the earth’s resources. Since investigating the disintegration mechanism in actual conditions is very difficult, the practice must start with laboratory research. Identifying and classifying the drilling stand and its aggregates as [...] Read more.
Rotary drilling technology with diamond tools is still essential in progressively extracting the earth’s resources. Since investigating the disintegration mechanism in actual conditions is very difficult, the practice must start with laboratory research. Identifying and classifying the drilling stand and its aggregates as objects will contribute to the clarification of certain problems related to streamlining the process, optimizing the working regime, preventing emergencies, and reducing energy and economic demands. For these purposes, the cluster method was designed and applied. Applying the clustering method has a significant place in complex and dynamic processes. Eight vibration signals were measured and processed during the operation of the aggregates, such as the motor, pump, and hydrogenerator, with a sampling frequency of 18 kHz and a time interval of 30 s. Subsequently, 16 symptoms were designed and numerically calculated in the time and frequency domain, creating the symptom vector of the aggregate. The aim of the study and article was the classification of aggregates as objects into recognizable clusters. The results show that the strong symptoms include a measure of variability, variance in the signal, and kurtosis. The weak symptoms are skewness and the moment of the signal spectrum. Visualization in the symptom plane and space proved their influence on cluster formation. According to the cluster analysis results, six to seven clusters presenting the activity of the aggregates were classified. It was found that the boundaries between the clusters were not sharp. As part of the research, the centroids of clusters of aggregates and the distances between them were calculated. Classified clusters can rebuild reference clusters for objects with a similar character in a broader context. Full article
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<p>General scheme of recognition in technical diagnostics.</p>
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<p>A simple model for the classification of drilling stand aggregates.</p>
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<p>Laboratory horizontal drilling stand: (<b>a</b>) 1—feed mechanism; 2—sheet metal cover of the working tool; 3—stand; (<b>b</b>) 4—slide; 5—control system for the drive spindle; 6—double acting hydraulic cylinder; (<b>c</b>) 7—core barrel; 8—drilling bit; 9—centering sled clamping mechanism.</p>
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<p>Waveforms of values of selected symptoms of all aggregates <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>4</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>5</mn> </msub> </semantics></math>: (<b>a</b>) symptom <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math>—mean values; (<b>b</b>) symptom <math display="inline"><semantics> <msub> <mi>s</mi> <mn>2</mn> </msub> </semantics></math>—dispersion values; (<b>c</b>) symptom <math display="inline"><semantics> <msub> <mi>s</mi> <mn>4</mn> </msub> </semantics></math>—kurtosis values; (<b>d</b>) symptom <math display="inline"><semantics> <msub> <mi>s</mi> <mn>5</mn> </msub> </semantics></math>—maximum values.</p>
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<p>Waveforms of values of selected symptoms of all aggregates <math display="inline"><semantics> <msub> <mi>s</mi> <mn>7</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>11</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>12</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math>: (<b>a</b>) symptom <math display="inline"><semantics> <msub> <mi>s</mi> <mn>7</mn> </msub> </semantics></math>—rms value; (<b>b</b>) symptom <math display="inline"><semantics> <msub> <mi>s</mi> <mn>11</mn> </msub> </semantics></math>—entropy values; (<b>c</b>) symptom <math display="inline"><semantics> <msub> <mi>s</mi> <mn>12</mn> </msub> </semantics></math>—norm autocorrelation values; (<b>d</b>) symptom <math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math>—norm spectrum power values.</p>
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<p>Waveforms of values of selected symptoms of all aggregates <math display="inline"><semantics> <msub> <mi>s</mi> <mn>3</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>13</mn> </msub> </semantics></math>: (<b>a</b>) symptom <math display="inline"><semantics> <msub> <mi>s</mi> <mn>3</mn> </msub> </semantics></math>—skewness values; (<b>b</b>) symptom <math display="inline"><semantics> <msub> <mi>s</mi> <mn>13</mn> </msub> </semantics></math>—moment spectrum values.</p>
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<p>Motor as the aggregate: (<b>a</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>8</mn> </msub> </semantics></math>; (<b>b</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>9</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math>.</p>
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<p>Pump as the aggregate: (<b>a</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>8</mn> </msub> </semantics></math>; (<b>b</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>9</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math>.</p>
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<p>Hydrogenerator as aggregate: (<b>a</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>8</mn> </msub> </semantics></math>; (<b>b</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>9</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math>.</p>
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<p>Motor and pump as aggregates: (<b>a</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>8</mn> </msub> </semantics></math>; (<b>b</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>9</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math>.</p>
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<p>Motor and hydrogenerator as aggregates: (<b>a</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>8</mn> </msub> </semantics></math>; (<b>b</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>9</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math>.</p>
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<p>Pump and hydrogenerator as aggregates: (<b>a</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>8</mn> </msub> </semantics></math>; (<b>b</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>9</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math>.</p>
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<p>All stand aggregates without drill bit: (<b>a</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>8</mn> </msub> </semantics></math>; (<b>b</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>9</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math>.</p>
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<p>All stand aggregates with drill bit: (<b>a</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>8</mn> </msub> </semantics></math>; (<b>b</b>) symptom values <math display="inline"><semantics> <msub> <mi>s</mi> <mn>9</mn> </msub> </semantics></math>–<math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math>.</p>
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<p>Cluster graphs of all objects in two-dimensional symptom space with symptoms <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>4</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>5</mn> </msub> </semantics></math>: (<b>a</b>) graph of symptoms <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>s</mi> <mn>2</mn> </msub> </semantics></math> in the plane; (<b>b</b>) graph of symptoms symptoms <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>s</mi> <mn>4</mn> </msub> </semantics></math> in the plane; (<b>c</b>) graph of symptoms <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>s</mi> <mn>5</mn> </msub> </semantics></math> in the plane; (<b>d</b>) graph of symptoms <math display="inline"><semantics> <msub> <mi>s</mi> <mn>2</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>s</mi> <mn>4</mn> </msub> </semantics></math> in the plane.</p>
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<p>Cluster graphs of all objects in the two-dimensional symptom space with symptoms <math display="inline"><semantics> <msub> <mi>s</mi> <mn>7</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>11</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>12</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math>: (<b>a</b>) graph of symptoms <math display="inline"><semantics> <msub> <mi>s</mi> <mn>7</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>s</mi> <mn>11</mn> </msub> </semantics></math> in the plane; (<b>b</b>) graph of symptoms <math display="inline"><semantics> <msub> <mi>s</mi> <mn>7</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>s</mi> <mn>12</mn> </msub> </semantics></math> in the plane; (<b>c</b>) graph of symptoms <math display="inline"><semantics> <msub> <mi>s</mi> <mn>11</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>s</mi> <mn>12</mn> </msub> </semantics></math> in the plane; (<b>d</b>) graph of symptoms <math display="inline"><semantics> <msub> <mi>s</mi> <mn>12</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>s</mi> <mn>16</mn> </msub> </semantics></math> in the plane.</p>
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<p>Clusters of symptom vectors of the drill stand aggregates in a three-dimensional symptom space: (<b>a</b>) graph of symptoms: mean, dispersion, and energy values in space; (<b>b</b>) graph of symptoms: energy, entropy, and norm autocorrelation values in space; (<b>c</b>) graph of symptoms: entropy, power and norm power spectrum values in space; (<b>d</b>) graph of symptoms: power, norm autocorrelation and norm power spectrum values in space.</p>
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<p>The location of clusters of symptom vectors of the drill stands aggregates in three-dimensional symptom space: (<b>a</b>) graph of symptoms: maximum, mean, and entropy values in space; (<b>b</b>) graph of symptoms: maximum, mean, and kurtosis in space; (<b>c</b>) graph of symptoms: energy, entropy, and moment power spectrum values in space; (<b>d</b>) graph of symptoms: kurtosis, energy, and norm power spectrum values in space.</p>
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<p>Centroids of the symptoms of the aggregates as objects in a three-dimensional symptom vector: (<b>a</b>) symptom centroids: mean, dispersion, and kurtosis values; (<b>b</b>) symptom centroids: maximum, peak2peak, and energy values; (<b>c</b>) symptom centroids: norm, power, and rms values; (<b>d</b>) symptom centroids: entropy, norm autocorrelation, and moment spectrum values; (<b>e</b>) symptom centroids: energy, entropy, and norm autocorrelation values; (<b>f</b>) symptom centroids: norm spectrum, norm power spectrum, and moment power spectrum.</p>
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14 pages, 4924 KiB  
Article
Studying the Tribological Properties of Coffee Oil-Loaded Water-Based Green Lubricant
by Raimondas Kreivaitis, Milda Gumbytė, Artūras Kupčinskas, Jolanta Treinytė, Kiril Kazancev and Eglė Sendžikienė
Appl. Sci. 2023, 13(10), 6336; https://doi.org/10.3390/app13106336 - 22 May 2023
Viewed by 1308
Abstract
Lubrication is the primary solution to reduce friction and wear. However, conventional lubricants cause pollution when not properly disposed of or due to accidental leaks. Therefore, environmentally friendly lubricating fluids are welcome in any application where they can meet the performance requirements. This [...] Read more.
Lubrication is the primary solution to reduce friction and wear. However, conventional lubricants cause pollution when not properly disposed of or due to accidental leaks. Therefore, environmentally friendly lubricating fluids are welcome in any application where they can meet the performance requirements. This study suggests using coffee oil produced from spent coffee grounds to improve the lubricity of water-based lubricating fluid. Bis(2-hydroxyethyl)ammonium oleate protic ionic liquid facilitates the dispersion of coffee oil in water. Kinematic viscosity, wettability, corrosion prevention, and lubricity tests were performed to evaluate the tribological properties provided by these additives. It was observed that a higher amount of coffee oil could be dispersed with the introduction of a higher amount of protic ionic liquid. In this study, ten wt.% of coffee oil was successfully dispersed using one wt.% of protic ionic liquid. Introducing additives increased dispersions’ viscosity, improved wettability, provided protection against corrosion, and reduced wear and friction. It was proposed that polar molecules of protic ionic liquid were responsible for most of the improvement, while coffee oil contributed by increasing viscosity. Further studies could be directed toward determining rational concentration to meet each particular application’s requirements. Full article
(This article belongs to the Special Issue Recent Trends in Biomass Materials)
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<p>The visual appearance of investigated lubricating samples observed half an hour after the preparation.</p>
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<p>UV-Vis spectra of coffee oil (<b>a</b>) and a lubricating sample with ten wt.% of CO and one wt.% of PIL (<b>b</b>).</p>
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<p>Kinematic viscosity as a function of the amount of coffee oil in investigated lubricating samples at the temperatures of 30 °C (<b>a</b>) and 70 °C (<b>b</b>).</p>
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<p>The density of investigated lubricating samples as a function of test temperature.</p>
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<p>Contact angle as a function of the amount of coffee oil measured immediately (<b>a</b>) and after 60 s (<b>b</b>).</p>
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<p>Corroded filter paper observed after cast iron chip corrosion test when several CO and PIL concentrations were investigated.</p>
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<p>Mean values of the coefficient of friction observed in tribo-test.</p>
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<p>Variation of the coefficient of friction recorded during the tribo-test when lubricating samples having 2.5% (<b>a</b>), 5% (<b>b</b>), and 10% (<b>c</b>) of coffee oil where investigated.</p>
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<p>Wear scar diameter (<b>a</b>) and wear volume (<b>b</b>) observed after the tribo-tests.</p>
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<p>The cross-section profiles of the wear traces on the plate observed after tribo-test.</p>
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<p>Wear scars on the balls and segments of wear traces on the plates observed after the tribo-test when lubricating with different CO and PIL concentrations.</p>
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18 pages, 5159 KiB  
Article
Performance and Modification Mechanism of Recycled Glass Fiber of Wind Turbine Blades and SBS Composite-Modified Asphalt
by Yihua Nie, Qing Liu, Zhiheng Xiang, Shixiong Zhong and Xinyao Huang
Appl. Sci. 2023, 13(10), 6335; https://doi.org/10.3390/app13106335 - 22 May 2023
Cited by 9 | Viewed by 2024
Abstract
Efficient disposal of composite materials recycled from wind turbine blades (WTB) at end-of-life needs to be solved urgently. To investigate the modification effects and mechanism on SBS-modified asphalt of the recycled glass fiber (GF) from WTB, GF-WTB/SBS composite-modified asphalt was prepared. Dynamic shear [...] Read more.
Efficient disposal of composite materials recycled from wind turbine blades (WTB) at end-of-life needs to be solved urgently. To investigate the modification effects and mechanism on SBS-modified asphalt of the recycled glass fiber (GF) from WTB, GF-WTB/SBS composite-modified asphalt was prepared. Dynamic shear rheometer (DSR) and bending beam rheometer (BBR) were adopted to evaluate its performance. FTIR, SEM, EDS, and AFM methods were used to assess coupling agent pretreatment effects on GF-WTB and observe the modification mechanism. The macroscopic tests show that reasonable addition of GF-WTB effectively raises the high-temperature performance and low-temperature crack resistance evaluation index k-value of SBS-modified asphalt, and the optimal content is 2 wt% GF-WTB with 4 wt% SBS. FTIR, SEM, and EDS tests show GF-WTB can be successfully grafted by UP152 coupling agent and show that adhesion of the GF-WTB to the SBS-modified asphalt can be improved. AFM observation shows SBS and GF-WTB have good compatibility, improving the asphalt elasticity and toughness. This study provides a feasible solution for environmentally friendly regeneration of the composite materials from WTB and contributes to the development of the secondary modifier of SBS-modified asphalt. Full article
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<p>Raw GFRP composites from wind turbine blade at end-of-life: (<b>a</b>) wind turbine blade at end-of-life; (<b>b</b>) recycled GFRP.</p>
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<p>Three types of GF particles obtained after grading of GFRP pieces: (<b>a</b>) &lt;0.075 mm; (<b>b</b>) 0.15–0.075 mm; (<b>c</b>) 0.3–0.15 mm.</p>
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<p>Flowchart of GF-WTB/SBS composite-modified asphalt preparation.</p>
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<p>Effect of GF-WTB content on rotational viscosity under certain SBS dosage.</p>
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<p>The logarithm of the complex shear modulus of GF-WTB/SBS composite-modified asphalt: (<b>a</b>) SBS2 + WTBx; (<b>b</b>) SBS3 + WTBx; (<b>c</b>) SBS4 + WTBx; (<b>d</b>) SBS5 + WTBx.</p>
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<p>The logarithm of the complex shear modulus of GF-WTB/SBS composite-modified asphalt: (<b>a</b>) SBS2 + WTBx; (<b>b</b>) SBS3 + WTBx; (<b>c</b>) SBS4 + WTBx; (<b>d</b>) SBS5 + WTBx.</p>
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<p>The rutting factor and high-temperature PG of GF-WTB/SBS composite-modified asphalt: (<b>a</b>) SBS2 + WTBx; (<b>b</b>) SBS3 + WTBx; (<b>c</b>) SBS4 + WTBx; (<b>d</b>) SBS5 + WTBx.</p>
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<p>The rutting factor and high-temperature PG of GF-WTB/SBS composite-modified asphalt: (<b>a</b>) SBS2 + WTBx; (<b>b</b>) SBS3 + WTBx; (<b>c</b>) SBS4 + WTBx; (<b>d</b>) SBS5 + WTBx.</p>
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<p>Effect of GF-WTB content on BBR k-value under certain SBS dosage and temperature: (<b>a</b>) SBS2 + WTBx; (<b>b</b>) SBS3 + WTBx; (<b>c</b>) SBS4 + WTBx; (<b>d</b>) SBS5 + WTBx.</p>
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<p>Effect of GF-WTB content on BBR k-value under certain SBS dosage and temperature: (<b>a</b>) SBS2 + WTBx; (<b>b</b>) SBS3 + WTBx; (<b>c</b>) SBS4 + WTBx; (<b>d</b>) SBS5 + WTBx.</p>
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<p>Chemical reaction mechanism between GF-WTB and UP152.</p>
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<p>FTIR spectrograms of untreated GF-WTB and UP152-modified GF-WTB.</p>
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<p>SEM photographs of the GF-WTB: (<b>a</b>) untreated; (<b>b</b>) UP152-modified.</p>
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<p>EDS element spectrum of the GF-WTB: (<b>a</b>) untreated; (<b>b</b>) UP152-modified.</p>
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<p>AFM of: (<b>a</b>) matrix asphalt; (<b>b</b>) SBS-modified asphalt; (<b>c</b>) GF-WTB/SBS composite-modified asphalt.</p>
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21 pages, 9854 KiB  
Article
Calculation Method of Loose Pressure in Surrounding Rock Mass
by Hongjie Gao, Weibin Ma, Wenhao Zou, Jinlong Zhang, Xinyu Li and Jiaqiang Han
Appl. Sci. 2023, 13(10), 6334; https://doi.org/10.3390/app13106334 - 22 May 2023
Cited by 2 | Viewed by 1272
Abstract
With the implementation of the regional coordinated development strategy, traffic flow has grown explosively. The construction of a larger tunnel section becomes an effective way to solve the highway network’s insufficient transport capability problem. Currently, there is little research on the factors influencing [...] Read more.
With the implementation of the regional coordinated development strategy, traffic flow has grown explosively. The construction of a larger tunnel section becomes an effective way to solve the highway network’s insufficient transport capability problem. Currently, there is little research on the factors influencing and methods of calculating the loose pressure in the surrounding rock mass for highway tunnels with super-large cross-sections. Based on the Bifurcation Tunnel, which is one of the sign projects in the past five years, this paper discusses the influencing factors for the range of loose zone in deeply buried tunnels using a combination of a numerical analysis and an orthogonal test. The weight of influencing factors is calculated via an efficiency evaluation method. This paper establishes a limit analysis model of the loose pressure in the surrounding rock mass under a non-linear failure criterion based on the fitted boundary function and upper bound limit analysis method and deduces the correlations of the loose pressure. The distribution law of the loose pressure, obtained via the limit analysis method, is consistent with the pressure-monitoring results, verifying the correctness of the proposed calculation method. This study can provide a calculation basis for the design of a supporting structure and the selection of similar super-section tunnel projects. Full article
(This article belongs to the Special Issue Advances in Tunnel and Underground Construction)
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<p>Empirical formula classification.</p>
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<p>Route planning of the Greater Bay Area.</p>
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<p>Plan view of bifurcation section.</p>
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<p>Engineering geologic profile of the project.</p>
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<p>Site photos of the drill core. (<b>a</b>) Small clear-distance section; (<b>b</b>) large-span section.</p>
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<p>Distribution of test points. (<b>a</b>) Comprehensive test; (<b>b</b>) orthogonal test.</p>
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<p>Numerical model.</p>
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<p>Maximum principal stress vector cloud diagram (No. 16).</p>
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<p>Search path and loose zone boundary.</p>
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<p>Loose zone of surrounding rock mass.</p>
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<p>The fitting curve of the surrounding rock’s loose zone.</p>
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<p>Failure mechanisms.</p>
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<p>The velocity field schematic diagram.</p>
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<p>The tangential method.</p>
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<p>Flow of calculation for the loose pressure in the surrounding rock mass.</p>
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<p>Installation of the earth pressure cell. (<b>a</b>) Arch crown; (<b>b</b>) haunch.</p>
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<p>Comparison between the calculated and measured values.</p>
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16 pages, 1531 KiB  
Article
Steady-State Crack Growth in Nanostructured Quasi-Brittle Materials Governed by Second Gradient Elastodynamics
by Yury Solyaev
Appl. Sci. 2023, 13(10), 6333; https://doi.org/10.3390/app13106333 - 22 May 2023
Cited by 2 | Viewed by 1126
Abstract
The elastodynamic stress field near a crack tip propagating at a constant speed in isotropic quasi-brittle material was investigated, taking into account the strain gradient and inertia gradient effects. An asymptotic solution for a steady-state Mode-I crack was developed within the simplified strain [...] Read more.
The elastodynamic stress field near a crack tip propagating at a constant speed in isotropic quasi-brittle material was investigated, taking into account the strain gradient and inertia gradient effects. An asymptotic solution for a steady-state Mode-I crack was developed within the simplified strain gradient elasticity by using a representation of the general solution in terms of Lamé potentials in the moving framework. It was shown that the derived solution predicts the nonsingular stress state and smooth opening profile for the growing cracks that can be related to the presence of the fracture process zone in the micro-/nanostructured quasi-brittle materials. Note that similar asymptotic solutions have been derived previously only for Mode-III cracks (under antiplane shear loading). Thus, the aim of this study is to show the possibility of analytical assessments on the elastodynamic crack tip fields for in-plane loading within gradient theories. By using the derived solution, we also performed analysis of the angular distribution of stresses and tractions for the moderate speed of cracks. It was shown that the usage of the maximum principal stress criterion within second gradient elastodynamics allows us to describe a directional stability of Mode-I crack growth and an increase in the dynamic fracture toughness with the crack propagation speed that were observed in the experiments with quasi-brittle materials. Therefore, the possibility of the effective application of regularized solutions of strain gradient elasticity for the refined analysis of dynamic fracture processes in the quasi-brittle materials with phenomenological assessments on the cohesive zone effects is shown. Full article
(This article belongs to the Special Issue Novel Nanomaterials and Nanostructures)
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<p>Illustration for the growing crack problem with global and local (moving) coordinate systems. Opening mode under remotely applied loading is considered. Solution is found in the vicinity of a crack tip, where <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>r</mi> <mo>¯</mo> </mover> <mo>≪</mo> <mn>1</mn> </mrow> </semantics></math>, i.e., <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>≪</mo> <mi>l</mi> </mrow> </semantics></math>.</p>
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<p>Influence of Poisson’s ratio (<b>a</b>) and Mach number (<b>b</b>) of the deformations of small circles around the crack tip in SGET (left) and classical (right) asymptotic steady-state solutions.</p>
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<p>Influence of dilatational (<b>a</b>) and rotational (<b>b</b>) amplitudes on the deformed state around the tip of crack.</p>
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<p>Typical dependence of normalized tractions and double tractions on angular coordinate (<b>a</b>) and in-plane distribution of normalized hoop traction <math display="inline"><semantics> <msub> <mover accent="true"> <mi>t</mi> <mo>^</mo> </mover> <mi>θ</mi> </msub> </semantics></math> ((<b>b</b>), crack is shown by thick black line) in SGET asymptotic solution.</p>
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<p>Angular distribution of normalized hoop traction (<b>a</b>) and radial traction (<b>b</b>) for different values of Mach number and Poisson’s ratio.</p>
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<p>Angular distribution of normalized stress components <math display="inline"><semantics> <msub> <mover accent="true"> <mi>σ</mi> <mo>^</mo> </mover> <mrow> <mi>θ</mi> <mi>θ</mi> </mrow> </msub> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <msub> <mover accent="true"> <mi>σ</mi> <mo>^</mo> </mover> <mrow> <mi>r</mi> <mi>θ</mi> </mrow> </msub> </semantics></math> (<b>b</b>), <math display="inline"><semantics> <msub> <mover accent="true"> <mi>σ</mi> <mo>^</mo> </mover> <mrow> <mi>r</mi> <mi>r</mi> </mrow> </msub> </semantics></math> (<b>c</b>) evaluated at different distances from the crack tip and for the values of Mach number <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math> (solid lines) and <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> (dashed lines).</p>
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<p>Angular distribution of normalized maximum principal stress (<b>a</b>) and maximum shear stress (<b>b</b>) and distribution of the normalized maximum principal stress along the crack propagation direction (along <math display="inline"><semantics> <msub> <mi>x</mi> <mn>1</mn> </msub> </semantics></math> at <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>) for different values of amplitude ratio <math display="inline"><semantics> <msub> <mi>k</mi> <mi>θ</mi> </msub> </semantics></math> (<b>c</b>). The values of Mach number are <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math> (solid lines) and <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> (dashed lines).</p>
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17 pages, 5626 KiB  
Article
Decision-Refillable-Based Shared Feature-Guided Fuzzy Classification for Personal Thermal Comfort
by Zhaofei Xu, Weidong Lu, Zhenyu Hu, Wei Yan, Wei Xue, Ta Zhou and Feifei Jiang
Appl. Sci. 2023, 13(10), 6332; https://doi.org/10.3390/app13106332 - 22 May 2023
Viewed by 1146
Abstract
Different types of buildings in different climate zones have their own design specifications and specific user populations. Generally speaking, these populations have similar sensory feedbacks in their perception of environmental thermal comfort. Existing thermal comfort models do not incorporate personal thermal comfort models [...] Read more.
Different types of buildings in different climate zones have their own design specifications and specific user populations. Generally speaking, these populations have similar sensory feedbacks in their perception of environmental thermal comfort. Existing thermal comfort models do not incorporate personal thermal comfort models for specific populations. In terms of an algorithm, the existing work constructs machine learning models based on an established human thermal comfort database with variables such as indoor temperature, clothing insulation, et al., and has achieved satisfactory classification results. More importantly, such thermal comfort models often lack scientific interpretability. Therefore, this study selected a specific population as the research object, adopted the 0-order Takagi–Sugeno–Kang (TSK) fuzzy classifier as the base training unit, and constructed a shared feature-guided new TSK fuzzy classification algorithm with extra feature compensation (SFG-TFC) to explore the perception features of the population in the thermal environment of buildings and to improve the classification performance and interpretability of the model. First, the shared features of subdatasets collected in different time periods were extracted. Second, the extra features of each subdataset were independently trained, and the rule outputs corresponding to the key shared features were reprojected into the corresponding fuzzy classifiers. This strategy not only highlights the guiding role of shared features but also considers the important compensation effect of extra features; thereby, improving the classification performance of the entire classification model. Finally, the least learning machine (LLM) was used to solve the parameters of the “then” part of each basic training unit, and these output weights were integrated to enhance the generalization performance of the model. The experimental results demonstrate that SFG-TFC has better classification performance and interpretability than the classic nonfuzzy algorithms support vector machine (SVM) and deep belief network (DBN), the 0-order TSK, and the multilevel optimization and fuzzy approximation algorithm QI-TSK. Full article
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<p>The training structure of SFG-TFC.</p>
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<p>Sampling situation.</p>
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<p>Schematic diagram of the experimental flow.</p>
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<p>Selected features for the development of SFG-TFC personal thermal comfort models. Data1, Data2 and Data3 were collected in April 2020, April 2021, and December 2021, respectively.</p>
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<p>Schematic diagram of thermal sensation classification.</p>
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<p>Schematic diagram of the classification performance.</p>
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<p>Schematic diagram of the generalization performance.</p>
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<p>Performance comparison of SFG-TFC with and without shared feature guidance.</p>
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<p>Performance comparison of the SFG-TFC with and without output weight integration.</p>
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<p>Division of fuzzy partitions.</p>
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<p>Partial rule descriptions.</p>
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16 pages, 1576 KiB  
Article
Detecting Fake Reviews in Google Maps—A Case Study
by Paweł Gryka and Artur Janicki
Appl. Sci. 2023, 13(10), 6331; https://doi.org/10.3390/app13106331 - 22 May 2023
Cited by 5 | Viewed by 3673
Abstract
Many customers rely on online reviews to make an informed decision about purchasing products and services. Unfortunately, fake reviews, which can mislead customers, are increasingly common. Therefore, there is a growing need for effective methods of detection. In this article, we present a [...] Read more.
Many customers rely on online reviews to make an informed decision about purchasing products and services. Unfortunately, fake reviews, which can mislead customers, are increasingly common. Therefore, there is a growing need for effective methods of detection. In this article, we present a case study showing research aimed at recognizing fake reviews in Google Maps places in Poland. First, we describe a method of construction and validation of a dataset, named GMR–PL (Google Maps Reviews—Polish), containing a selection of 18 thousand fake and genuine reviews in Polish. Next, we show how we used this dataset to train machine learning models to detect fake reviews and the accounts that published them. We also propose a novel metric for measuring the typicality of an account name and a metric for measuring the geographical dispersion of reviewed places. Initial recognition results were promising: we achieved an F1 score of 0.92 and 0.74 when detecting fake accounts and reviews, respectively. We believe that our experience will help in creating real-life review datasets for other languages and, in turn, will help in research aimed at the detection of fake reviews on the Internet. Full article
(This article belongs to the Special Issue Signal, Multimedia, and Text Processing in Cybersecurity Context)
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<p>Summarized results of experts’ voting.</p>
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<p>Number of reviews in each cluster.</p>
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<p>Histograms of numbers of reviews per account for (<b>a</b>) fake and (<b>b</b>) genuine accounts. The last bin contains accounts with 200 or more reviews.</p>
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<p>Geographical locations of reviewed places, shown on map of Poland, for (<b>a</b>) fake and (<b>b</b>) genuine reviews.</p>
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<p>Histograms of review ratings for (<b>a</b>) fake and (<b>b</b>) genuine reviews.</p>
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<p>Histograms of account name scores (values above 400 k were skipped). (<b>a</b>) Fake accounts (zoomed); (<b>b</b>) genuine accounts (zoomed).</p>
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<p>Histograms of geographic distribution of reviews. (<b>a</b>) Fake reviews (zoomed); (<b>b</b>) genuine reviews.</p>
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21 pages, 5147 KiB  
Article
Surface Damage Identification of Wind Turbine Blade Based on Improved Lightweight Asymmetric Convolutional Neural Network
by Li Zou, Haowen Cheng and Qianhui Sun
Appl. Sci. 2023, 13(10), 6330; https://doi.org/10.3390/app13106330 - 22 May 2023
Cited by 3 | Viewed by 1458
Abstract
Wind turbine blades are readily damaged by the workplace environment and frequently experience flaws such as surface peeling and cracking. To address the problems of cumbersome operation, high cost, and harsh application conditions with traditional damage identification methods, and to cater to the [...] Read more.
Wind turbine blades are readily damaged by the workplace environment and frequently experience flaws such as surface peeling and cracking. To address the problems of cumbersome operation, high cost, and harsh application conditions with traditional damage identification methods, and to cater to the wide application of mobile terminal devices such as unmanned aerial vehicles, a novel lightweight asymmetric convolution neural network is proposed. The network introduces a lightweight asymmetric convolution module based on the improved asymmetric convolution, which applies depthwise separable convolution and channel shuffle to ensure efficient feature extraction capability while achieving a lightweight design. An enhanced Convolutional Block Attention Module (CBAM) embedded with a spatial attention module with a selective kernel, enhances the acquisition of spatial locations of damage features by combining multi-scale feature information. Experiments are carried out to verify the efficacy and the generalizability of the network proposed for the recognition task. A comparison experiment of common lightweight networks based on transfer learning is also conducted. The experimental results show that the lightweight network proposed in this article has better experimental metrics, including 99.94% accuracy, 99.88% recall, and 99.92% precision. Full article
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<p>Schematic diagram of backbone-enhanced convolution kernel.</p>
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<p>Schematic diagram of backbone-enhanced convolution kernel.</p>
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<p>Diagram of the depthwise separable convolution process.</p>
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<p>Schematic diagram of the channel shuffle process.</p>
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<p>Structure diagram of AC Block and LAC Block. (<b>a</b>) AC Block. (<b>b</b>) LAC Block.</p>
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<p>Diagram of the overall Selective Kernel process.</p>
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<p>Structure diagram of SAM and SK_SAM. (<b>a</b>) SAM. (<b>b</b>) SK_SAM.</p>
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<p>Structure diagram of CBAM and E_CBAM. (<b>a</b>) CBAM. (<b>b</b>) E_CBAM.</p>
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<p>Three kinds of blade images. (<b>a</b>) Normal. (<b>b</b>) Cracks. (<b>c</b>) Surface shedding.</p>
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<p>LACB_ECBAM Net’s accuracy growth at different batch sizes: (<b>a</b>) 16, (<b>b</b>) 32, and (<b>c</b>) 64.</p>
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<p>Structure diagram of AlexNet’s modifications. (<b>a</b>) Original AlexNet. (<b>b</b>) AlexNet + AC Block. (<b>c</b>) AlexNet + LAC Block. (<b>d</b>) AlexNet + CBAM. (<b>e</b>) AlexNet + E_CBAM. (<b>f</b>) AlexNet + LAC Block + E_CBAM.</p>
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<p>Structure diagram of AlexNet’s modifications. (<b>a</b>) Original AlexNet. (<b>b</b>) AlexNet + AC Block. (<b>c</b>) AlexNet + LAC Block. (<b>d</b>) AlexNet + CBAM. (<b>e</b>) AlexNet + E_CBAM. (<b>f</b>) AlexNet + LAC Block + E_CBAM.</p>
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<p>Main structure diagram of the six lightweight networks. (<b>a</b>) ResNeXt50 (32 × 4d). (<b>b</b>) Xception. (<b>c</b>) MobileNet_V2. (<b>d</b>) ShuffleNet_V2. (<b>e</b>) EfficientNet-B0.</p>
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<p>Main structure diagram of the six lightweight networks. (<b>a</b>) ResNeXt50 (32 × 4d). (<b>b</b>) Xception. (<b>c</b>) MobileNet_V2. (<b>d</b>) ShuffleNet_V2. (<b>e</b>) EfficientNet-B0.</p>
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<p>Main structure diagram of the six lightweight networks. (<b>a</b>) ResNeXt50 (32 × 4d). (<b>b</b>) Xception. (<b>c</b>) MobileNet_V2. (<b>d</b>) ShuffleNet_V2. (<b>e</b>) EfficientNet-B0.</p>
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18 pages, 9957 KiB  
Article
Numerical and Analytical Analysis of the Low-Frequency Magnetic Fields Generated by Three-Phase Underground Power Cables with Solid Bonding
by Eduard Lunca, Silviu Vornicu and Alexandru Sălceanu
Appl. Sci. 2023, 13(10), 6328; https://doi.org/10.3390/app13106328 - 22 May 2023
Viewed by 2010
Abstract
There is a special concern for measuring and simulating low-frequency magnetic fields generated by underground power cables, particularly in human exposure studies. In the present study, an accurate 2D finite element model for computing magnetic fields generated by three-phase underground power cables with [...] Read more.
There is a special concern for measuring and simulating low-frequency magnetic fields generated by underground power cables, particularly in human exposure studies. In the present study, an accurate 2D finite element model for computing magnetic fields generated by three-phase underground power cables with solid bonding is proposed. The model is developed in ANSYS Maxwell 2D low-frequency electromagnetic field simulation software for a typical 12/20 kV (medium-voltage) three-phase underground power cable in both trefoil and flat formations, but it can be adapted to any cable system. Model validation is achieved by analytical computations conducted with a software tool based on the Biot–Savart law and the superposition principle. RMS magnetic flux density profiles calculated at various heights above the ground with these two methods correlate very well. This is also true for induced shield currents. The application of the finite element model to multiple three-phase power cables laid together is also considered. Full article
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<p>Typical cable formations: (<b>a</b>) trefoil formation; (<b>b</b>) flat formation.</p>
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<p>Solid bonding.</p>
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<p>NA2XS(F)2Y cable.</p>
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<p>Physical layout of the analyzed three-phase cable system: (<b>a</b>) trefoil formation; (<b>b</b>) flat formation with spacing (all dimensions are given in mm).</p>
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<p><b>The 2D</b> FEM model for computing magnetic fields from a three-phase underground cable system with solid bonding: (<b>a</b>) global geometric model; (<b>b</b>) discretized section around the power cables; (<b>c</b>) simplified cable model; (<b>d</b>) coupled circuit for shield bonding.</p>
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<p><b>The 2D</b> FEM model for computing magnetic fields from a three-phase underground cable system with solid bonding: (<b>a</b>) global geometric model; (<b>b</b>) discretized section around the power cables; (<b>c</b>) simplified cable model; (<b>d</b>) coupled circuit for shield bonding.</p>
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<p>Example of simulation results for the three-phase power cable in trefoil formation: (<b>a</b>) instantaneous magnetic flux density profiles and calculated RMS magnetic flux density profile at the height <span class="html-italic">h</span> = 1 m above the ground; (<b>b</b>) a momentary magnetic field distribution in the cross section of the power cable (<span class="html-italic">t</span> = 16.94 ms).</p>
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<p>Example of simulation results for the three-phase power cable in flat formation: (<b>a</b>) instantaneous magnetic flux density profiles and calculated RMS magnetic flux density profile at the height <span class="html-italic">h</span> = 1 m above the ground; (<b>b</b>) a momentary magnetic field distribution in the cross section of the power cable (<span class="html-italic">t</span> = 15.56 ms).</p>
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<p><b>The 2D</b> analytical calculation of the magnetic flux density generated by a three-phase power cable with solid bonding.</p>
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<p>Example of results obtained by analytical computation (the total RMS magnetic flux density and its transversal components at the height of 1 m above the ground): (<b>a</b>) for the three-phase power cable in trefoil formation; (<b>b</b>) for the three-phase power cable in flat formation.</p>
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<p>Comparison between numerical and analytical results (RMS magnetic flux density profiles at the height of 1 m above the ground): (<b>a</b>) for the three-phase power cable in trefoil formation; (<b>b</b>) for the three-phase power cable in flat formation.</p>
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<p>Magnetic flux density profiles at several heights above the ground, obtained by numerical simulation: (<b>a</b>) for trefoil formation; (<b>b</b>) for flat formation.</p>
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<p>Magnetic flux density at the central axis of the three-phase power cable with solidly bonded shields and non-bonded shields, obtained by numerical simulation: (<b>a</b>) for trefoil formation; (<b>b</b>) for flat formation.</p>
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<p>Magnetic flux density at the central axis of the three-phase underground power cable with solidly bonded shields and non-bonded shields (50 mm<sup>2</sup> copper wire shield), obtained by numerical simulation: (<b>a</b>) for trefoil formation; (<b>b</b>) for flat formation.</p>
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<p><b>The 2D</b> FEM model for computing magnetic fields from an arrangement of two adjacent three-phase power cables (with solid bonding) in flat formation.</p>
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<p>RMS magnetic flux density profiles at the height of 1 m above the ground as a function of phase sequence.</p>
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<p>The 2D distribution of the (maximum) instantaneous magnetic flux density around the considered power cable arrangement.</p>
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16 pages, 2063 KiB  
Article
Fast, Lightweight, and Efficient Cybersecurity Optimization for Tactical–Operational Management
by Manuel Domínguez-Dorado, David Cortés-Polo, Javier Carmona-Murillo, Francisco J. Rodríguez-Pérez and Jesús Galeano-Brajones
Appl. Sci. 2023, 13(10), 6327; https://doi.org/10.3390/app13106327 - 22 May 2023
Cited by 4 | Viewed by 1649
Abstract
The increase in frequency and complexity of cyberattacks has heightened concerns regarding cybersecurity and created an urgent need for organizations to take action. To effectively address this challenge, a comprehensive and integrated approach is required involving a cross-functional cybersecurity workforce that spans tactical [...] Read more.
The increase in frequency and complexity of cyberattacks has heightened concerns regarding cybersecurity and created an urgent need for organizations to take action. To effectively address this challenge, a comprehensive and integrated approach is required involving a cross-functional cybersecurity workforce that spans tactical and operational levels. In this context there can be various combinations of cybersecurity actions that affect different functional domains and that allow for meeting the established requirements. In these cases, agreement will be needed, but finding high-quality combinations requires analysis from all perspectives on a case-by-case basis. With a large number of cybersecurity factors to consider, the size of the search space of potential combinations becomes unmanageable without automation. To solve this issue, we propose Fast, Lightweight, and Efficient Cybersecurity Optimization (FLECO), an adaptive, constrained, and multi-objective genetic algorithm that reduces the time required to identify sets of high-quality cybersecurity actions. FLECO enables productive discussions on viable solutions by the cross-functional cybersecurity workforce within an organization, fostering managing meetings where decisions are taken and boosting the overall cybersecurity management process. Our proposal is novel in its application of evolutionary computing to solve a managerial issue in cybersecurity and enhance the tactical–operational cybersecurity management process. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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<p>The ULEO breakdown from functions to expected outcomes, each assigned a DLI.</p>
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<p>Chromosome definition from the ULEO.</p>
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<p>Approximation achieved by FLECO of the constrained solutions space. Each green dot is a feasible, high-quality solution found by FLECO.</p>
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14 pages, 3213 KiB  
Article
Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach
by Hangkun Xu and Runzi Liu
Appl. Sci. 2023, 13(10), 6326; https://doi.org/10.3390/app13106326 - 22 May 2023
Cited by 1 | Viewed by 1105
Abstract
With the increase of the number of satellites in space, satellite tracking, telemetry, and command (TT&C) is becoming more and more important for aerospace. This paper proposes a method for a low time fragmentation oriented. TT&C task planning method based on Double Deep [...] Read more.
With the increase of the number of satellites in space, satellite tracking, telemetry, and command (TT&C) is becoming more and more important for aerospace. This paper proposes a method for a low time fragmentation oriented. TT&C task planning method based on Double Deep Q-Network (DDQN). This method mainly solves the problem of poor responses to emergency tasks caused by the large amount of time fragments of equipment under traditional TT&C task-planning methods. Firstly, a multi-objective optimization model aiming at minimizing time fragments and maximizing task revenue is formulated. Then, according to the conflict characteristics of tasks, a task-planning conflict graph is proposed, based on which a TT&C task-planning problem is transferred into an independent set problem. Finally, DDQN is combined with graph structure embedding to solve the transferred independent set problem. The experimental results demonstrate that the proposed method reduces the time fragment of TT&C equipment by 32% and shortens the response time of emergency tasks by 36% compared to existing methods. Full article
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<p>TT&amp;C scenario.</p>
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<p>Time fragments.</p>
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<p>Conflicts among visible windows.</p>
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<p>Graph learning-based framework for solving the TT&amp;C task-planning problem.</p>
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<p>Directed graph of visible windows.</p>
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<p>The change graph of loss function with the number of training steps.</p>
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<p>Total time fragments vs. number of tasks.</p>
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<p>Average response time of emergency tasks.</p>
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<p>Total revenue of tasks vs. number of tasks.</p>
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22 pages, 7882 KiB  
Article
“Migrate-Transfer-Control” Support System of Surrounding Rock in the Deep Roadway and Its Application
by Tao Qin, Binyang Duan, Yanwei Duan, Yaozu Ni, Xiangang Hou, Pingyun Ma and Yue Yang
Appl. Sci. 2023, 13(10), 6325; https://doi.org/10.3390/app13106325 - 22 May 2023
Cited by 3 | Viewed by 952
Abstract
After coal mining enters the deep, the mining environment changes dramatically, and engineering disasters become increasingly prominent, which are mostly related to rock instability and failure. As traditional support is difficult to meet production needs, it is necessary to improve the support system. [...] Read more.
After coal mining enters the deep, the mining environment changes dramatically, and engineering disasters become increasingly prominent, which are mostly related to rock instability and failure. As traditional support is difficult to meet production needs, it is necessary to improve the support system. Based on the engineering background of the Pinggang mining roadway, this work studies the migration law of overlying strata in deep goaf by theoretical analysis and numerical simulation. The results show that the vertical stress and plastic failure range of the surrounding rock in front of the working face increase with the advance distance and when the working face advances to the first square, reaching the maximum. A stope spatial model considering the influence of horizontal stress is established. Combined with the theory of key strata, the stress transfer characteristics of overlying strata are analyzed. It can be seen that 0~30 m in front of the coal wall of the working face is the influence range of advanced abutment pressure, and the dynamic mining pressure in this range has a great influence. The inclined direction of the working face, 0~20 m away from the coal wall of the roadway, is the influence range of the solid coal abutment pressure. On this basis, the “migration- transfer- control” technical system of surrounding rock in deep stope face is put forward, i.e., the stress transfer of surrounding rock is caused by overlying rock migration, and the large deformation of surrounding rock is controlled by supporting means. Based on the original support scheme of the roadway, three reinforcement schemes are designed for the roof, the sidewalls, and both the roof and sides. The deformation control effect of the reinforcement scheme is far greater than that of the single factor, and the field monitoring effect is good. The research results aim to provide theoretical and technical support for the deformation control of mining roadways in the deep mining process. Full article
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<p>Roadway section support on the first right working face.</p>
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<p>Bending moment diagram of clamped rock beam. (<b>a</b>) Beam point analysis. (<b>b</b>) Fixed rock beam bending moment diagram.</p>
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<p>Stress state of each point of the fixed beam. (<b>a</b>) Point a. (<b>b</b>) Point b. (<b>c</b>) Point c. (<b>d</b>) Point d. (<b>e</b>) Point e.</p>
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<p>Section of the normal stress and shear stress diagram.</p>
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<p>Schematic diagram of cantilever rock beam. (<b>a</b>) cantilever rock beam. (<b>b</b>) cantilever rock beam bending moment diagram.</p>
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<p>Points on cantilever rock beam.</p>
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<p>Stress state of each point of cantilever rock beam. (<b>a</b>) Point a. (<b>b</b>) Point b. (<b>c</b>) Point c.</p>
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<p>Shear moment diagram on section.</p>
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<p>Numerical simulation model.</p>
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<p>Coal seam mining different distance vertical stress nephogram. (<b>a</b>) 10 m. (<b>b</b>) 20 m. (<b>c</b>) 30 m. (<b>d</b>) 40 m. (<b>e</b>) 50 m. (<b>f</b>) 60 m. (<b>g</b>) 80 m. (<b>h</b>) 100 m. (<b>i</b>) 120 m. (<b>j</b>) 180 m. (<b>k</b>) 240 m.</p>
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<p>Coal seam mining different distance vertical stress nephogram. (<b>a</b>) 10 m. (<b>b</b>) 20 m. (<b>c</b>) 30 m. (<b>d</b>) 40 m. (<b>e</b>) 50 m. (<b>f</b>) 60 m. (<b>g</b>) 80 m. (<b>h</b>) 100 m. (<b>i</b>) 120 m. (<b>j</b>) 180 m. (<b>k</b>) 240 m.</p>
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<p>Vertical stress curves at different distances.</p>
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<p>Plastic zone of surrounding rock under different advancing distances. (<b>a</b>) 10 m. (<b>b</b>) 20 m. (<b>c</b>) 30 m. (<b>d</b>) 40 m. (<b>e</b>) 50 m. (<b>f</b>) 60 m (half the length of the working face). (<b>g</b>) 80 m. (<b>h</b>) 100 m. (<b>i</b>) 120 m (the length of the working face). (<b>j</b>) 180 m. (<b>k</b>) 240 m.</p>
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<p>Stope space model considering the influence of horizontal stress.</p>
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<p>Overburden structure before and after thick hard rock breaking. (<b>a</b>) Thick and hard rock strata not broken. (<b>b</b>) Thick and hard rock strata broken.</p>
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<p>Stress distribution around goaf.</p>
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<p>Working face advancing different distance stress transfer characteristics.</p>
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<p>Distribution characteristics of abutment stress of solid coal side in gob-side roadway. (<b>a</b>) Upper roadway. (<b>b</b>) Lower roadway.</p>
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<p>Elastic–plastic deformation zone and stress distribution of surrounding rock of circular roadway [<a href="#B31-applsci-13-06325" class="html-bibr">31</a>].</p>
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<p>Simplified mechanical model of circular roadway.</p>
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<p>Schematic diagram of “Migrate-Transfer-Control” technology system in deep roadway.</p>
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<p>Schematic diagram of different support schemes. (<b>a</b>) The original support scheme. (<b>b</b>) Roof strengthening support scheme. (<b>c</b>) Strengthening support scheme of sidewall. (<b>d</b>) Strengthening support scheme of roof and sidewall.</p>
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<p>Vertical stress cloud diagram of different support schemes. (<b>a</b>) The original support scheme. (<b>b</b>) Roof strengthening support scheme. (<b>c</b>) Strengthening support scheme of sidewall. (<b>d</b>) Strengthening support scheme of roof and sidewall.</p>
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<p>Vertical stress cloud diagram of different support schemes. (<b>a</b>) The original support scheme. (<b>b</b>) Roof strengthening support scheme. (<b>c</b>) Strengthening support scheme of sidewall. (<b>d</b>) Strengthening support scheme of roof and sidewall.</p>
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<p>Vertical displacement cloud diagram of different support schemes. (<b>a</b>) The original support scheme. (<b>b</b>) Roof strengthening support scheme. (<b>c</b>) Strengthening support scheme of sidewall. (<b>d</b>) Strengthening support scheme of roof and sidewall.</p>
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<p>Displacement variation curve of roadway surrounding rock during mining period.</p>
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21 pages, 702 KiB  
Article
Multiview Fusion Using Transformer Model for Recommender Systems: Integrating the Utility Matrix and Textual Sources
by Thi-Linh Ho, Anh-Cuong Le and Dinh-Hong Vu
Appl. Sci. 2023, 13(10), 6324; https://doi.org/10.3390/app13106324 - 22 May 2023
Cited by 3 | Viewed by 1785
Abstract
Recommender systems are challenged with providing accurate recommendations that meet the diverse preferences of users. The main information sources for these systems are the utility matrix and textual sources, such as item descriptions, users’ reviews, and users’ profiles. Incorporating diverse sources of information [...] Read more.
Recommender systems are challenged with providing accurate recommendations that meet the diverse preferences of users. The main information sources for these systems are the utility matrix and textual sources, such as item descriptions, users’ reviews, and users’ profiles. Incorporating diverse sources of information is a reasonable approach to improving recommendation accuracy. However, most studies primarily use the utility matrix, and when they use textual sources they do not integrate them with the utility matrix. This is due to the risk of combined information causing noise and reducing the effectiveness of good sources. To overcome this challenge, in this study we propose a novel method that utilizes the Transformer Model, a deep learning model that efficiently integrates textual and utility matrix information. The study suggests feature extraction techniques suitable for each information source and an effective integration method in the Transformer model. The experimental results indicate that the proposed model significantly improves recommendation accuracy compared to the baseline model (MLP) for the Mean Absolute Error (MAE) metric, with a reduction range of 10.79% to 31.03% for the Amazon sub-datasets. Furthermore, when compared to SVD, which is known as one of the most efficient models for recommender systems, the proposed model shows a decrease in the MAE metric by a range of 34.82% to 56.17% for the Amazon sub-datasets. Our proposed model also outperforms the graph-based model with an increase of up to 108% in Precision, a decrease of up to 65.37% in MAE, and a decrease of up to 59.24% in RMSE. Additionally, experimental results on the Movielens and Amazon datasets also demonstrate that our proposed model, which combines information from the utility matrix and textual sources, yields better results compared to using only information from the utility matrix. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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<p>The Transformer encoder architecture.</p>
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<p>The process of breaking down the utility matrix into matrices of latent factors.</p>
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<p>Multiview fusion with transformer model for recommender systems. (The symbol * indicates the amount of item attribute information used in the model may vary depending on the dataset and the intended use of the organization).</p>
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<p>The experimental results for the Precision metric with the Movielens dataset.</p>
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<p>The experimental results for the MAE, RMSE, and Precision with the Amazon-Toys and Games dataset.</p>
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<p>The experimental results for the MAE, Precision with the Amazon-Video and Games dataset.</p>
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<p>The experimental results for the MAE, Precision with the Amazon-Electronic dataset.</p>
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