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42 pages, 9592 KiB  
Article
Air Route Network Planning Method of Urban Low-Altitude Logistics UAV with Double-Layer Structure
by Zhuolun Li, Shan Li, Jian Lu and Sixi Wang
Drones 2025, 9(3), 193; https://doi.org/10.3390/drones9030193 - 6 Mar 2025
Abstract
With the rapid development of e-commerce, logistics UAVs (unmanned aerial vehicles) have shown great potential in the field of urban logistics. However, the large-scale operation of logistics UAVs has brought challenges to air traffic management, and the competitiveness of UAV logistics is still [...] Read more.
With the rapid development of e-commerce, logistics UAVs (unmanned aerial vehicles) have shown great potential in the field of urban logistics. However, the large-scale operation of logistics UAVs has brought challenges to air traffic management, and the competitiveness of UAV logistics is still weak compared with traditional ground logistics. Therefore, this paper constructs a double-layer route network structure that separates logistics transshipment from terminal delivery. In the delivery layer, a door-to-door distribution mode is adopted, and the transshipment node service location model is constructed, so as to obtain the location of the transshipment node and the service relationship. In the transshipment layer, the index of the route betweenness standard deviation (BSD) is introduced to construct the route network planning model. In order to solve the above model, a double-layer algorithm was designed. In the upper layer, the multi-objective simulated annealing algorithm (MOSA) is used to solve the transshipment node service location issue, and in the lower layer, the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) is adopted to solve the network planning model. Based on real obstacle data and the demand situation, the double-layer network was constructed through simulation experiments. To verify the network rationality, actual flights were carried out on some routes, and it was found that the gap between the UAV’s autonomous flight route time and the theoretical calculations was relatively small. The simulation results show that compared with the single-layer network, the total distance with the double-layer network was reduced by 62.5% and the structural intersection was reduced by 96.9%. Compared with the minimum spanning tree (MST) algorithm, the total task flight distance obtained with the NSGA-II was reduced by 42.4%. The BSD factors can mitigate potential congestion risks. The route network proposed in this paper can effectively reduce the number of intersections and make the UAV passing volume more balanced. Full article
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<p>Schematic diagram of layered network architecture.</p>
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<p>The logic of the delivery process.</p>
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<p>Rasterization of airspace.</p>
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<p>Topological connectivity and microstructure of air routes.</p>
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<p>Double-layer air route network.</p>
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<p>Route intersection type. (<b>a</b>) Functional intersection. (<b>b</b>) Structural intersection.</p>
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<p>Algorithm implementation framework.</p>
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<p>MOSA individual coding.</p>
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<p>Population and chromosome.</p>
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<p>Offspring generation flow.</p>
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<p>The route network planning environment and foundation. (<b>a</b>) Site analysis. (<b>b</b>) Network planning foundation.</p>
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<p>Three-dimensional layout of the final route network.</p>
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<p>The results of transshipment node service location in the upper model. (<b>a</b>) Transshipment node location. (<b>b</b>) The Pareto frontier.</p>
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<p>The final route network structure. (<b>a</b>) Route connection relationship. (<b>b</b>) Network topology comparison.</p>
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<p>The Pareto front of the lower model.</p>
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<p>Flight duration from different supply nodes to each demand node. (<b>a</b>) Average flight duration. (<b>b</b>) Composition of flight duration to demand nodes from supply node 1. (<b>c</b>) Composition of flight duration to demand nodes from supply node 2.</p>
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<p>Relationship between route betweenness and total UAV passing volume.</p>
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<p>Flight scenarios.</p>
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<p>Some routes.</p>
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<p>Comparative results.</p>
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<p>Route network comparison.</p>
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<p>Comparison of the structural intersection distribution.</p>
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<p>Comparison of flight duration. (<b>a</b>) Flight duration from supply node 1 to each demand node. (<b>b</b>) Flight duration from supply node 2 to each demand node.</p>
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<p>Comparison of the total UAV passing volumes.</p>
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<p>Sensitivity analysis.</p>
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14 pages, 5195 KiB  
Article
Determining Sex-Specific Gene Expression Differences in Human Chorion Trophoblast Cells
by Daphne D. Arena Goncharov, Ryan C. V. Lintao, Rheanna Urrabaz-Garza, Enkhtuya Radnaa, Ananth K. Kammala, Lauren S. Richardson and Ramkumar Menon
Int. J. Mol. Sci. 2025, 26(5), 2239; https://doi.org/10.3390/ijms26052239 - 2 Mar 2025
Viewed by 213
Abstract
Differences in male (M) and female (F) neonates’ premature birth outcomes and placental trophoblast inflammation have been observed but are unknown to occur within the fetal membrane trophoblast layer (chorion trophoblasts [CTC]). This study examined whether sex-based differences in gene expression and inflammatory [...] Read more.
Differences in male (M) and female (F) neonates’ premature birth outcomes and placental trophoblast inflammation have been observed but are unknown to occur within the fetal membrane trophoblast layer (chorion trophoblasts [CTC]). This study examined whether sex-based differences in gene expression and inflammatory marker expression can be observed in CTCs under control or infectious inflammatory conditions modeling preterm birth. CTCs from six different patient-derived fetal membrane samples (3M/3F) were cultured and divided into experimental (Lipopolysaccharide [LPS]) and control groups for 6, 12, or 24 h. RNA from CTCs was subjected to RNA-seq, while cytokine multiplex or ELISA detected pro-/anti-inflammatory cytokines, progesterone, and soluble HLA-G in cell supernatants. CTC-M and CTC-F showed sex, time, and stimulant-dependent differential gene expression profiles. Cytokine analysis demonstrated a significantly lower IL-6 production in control CTC-M than in CTC-F. No sex-dependent responses were observed after LPS treatment regarding cytokines. CTC-M produced significantly lower progesterone than CTC-F. The theories of sexual dimorphism linked to placental inflammation may not extend to CTCs. This study supports that the chorion acts as a “great wall” protecting the fetus by being refractory to insults. Further examination into the weaknesses of the chorion barrier and sex-dependent responses of fetal membranes is needed. Full article
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<p>Confirmation of genotypic sex of CTCs. (<b>A</b>) Phase-contrast images of three biological replicates of CTCs from female and male placentas. Scale bar, 100 µm. (<b>B</b>) Localization of mRNAs in X and Y chromosomes of female and male CTCs. X-axis represents chromosome position, while Y-axis represents median of read density. The red arrow indicates the Y chromosome. (<b>C</b>) Electrophoresis gel profile of <span class="html-italic">SRY</span> gene amplicon (137 bp) in female and male CTCs, with GAPDH (452 bp) as control.</p>
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<p>Baseline gene expression differences between female and male CTCs. (<b>A</b>) Heatmap and hierarchical clustering of top differentially expressed genes in female and male CTCs. Upregulated genes are represented in red, and downregulated genes are represented in blue. (<b>B</b>) Volcano plots of differentially expressed genes between female and male CTCs. Genes significantly enriched in female CTCs are shown in red dots, while genes significantly enriched in male CTCs are shown in blue dots. (<b>C</b>) Location of differentially expressed genes between female and male CTCs with respect to human genome. (<b>D</b>) Bar graph of gene ontology enrichment analysis. Y-axis indicates the number of genes overlapping the GO term. (<b>E</b>) Dot plot of gene ontology (GO) enrichment analysis. Diameter indicates the number of genes overlapping the GO term. Color indicates the enrichment <span class="html-italic">p</span>-value. (<b>F</b>) Dot plot of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Diameter indicates the number of genes overlapping the KEGG term. Color indicates the enrichment <span class="html-italic">p</span>-value.</p>
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<p>Differences in intermediate filament expression and cellular morphology between male and female CTCs at baseline and in response to LPS. Immunostaining of CK-18 (red) and vimentin (green) after 24 h treatment, with DAPI as nuclear stain. Merged immunofluorescence and brightfield images are also shown. Scale bar, 200 µm.</p>
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<p>Differential transcriptomic and cellular responses of female and male CTCs to LPS. (<b>A</b>,<b>B</b>) Volcano plots of differentially expressed genes in (<b>A</b>) female and (<b>B</b>) male CTCs in response to LPS. Significantly upregulated genes in response to LPS are shown in red dots, while significantly downregulated genes are shown in blue dots. (<b>C</b>) Venn diagram of common differentially expressed genes between female and male CTCs. (<b>D</b>) Dot plot of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for female CTCs. Diameter indicates the number of genes overlapping the KEGG term. Color indicates the enrichment <span class="html-italic">p</span>-value. (<b>E</b>) Measurement of IL-6, IL-8, and progesterone production in female and male CTCs at baseline and in response to LPS. Data are presented as mean ± standard error of the mean (SEM). <span class="html-italic">p</span> values: **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ns, not significant.</p>
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13 pages, 5542 KiB  
Article
Microstructure and Texture Evolution of High Permeability Grain-Oriented Silicon Steel
by Yujie Fu and Lifeng Fan
Metals 2025, 15(3), 268; https://doi.org/10.3390/met15030268 - 28 Feb 2025
Viewed by 129
Abstract
Industrialization trial production of high permeability (Hi-B) steel was carried out by “one cold rolled + decarburization and nitridation technologies”. The finished product reached the level of 23Q100 with an average grain size of 5.47 cm, magnetic flux density B8 of 1.902T, [...] Read more.
Industrialization trial production of high permeability (Hi-B) steel was carried out by “one cold rolled + decarburization and nitridation technologies”. The finished product reached the level of 23Q100 with an average grain size of 5.47 cm, magnetic flux density B8 of 1.902T, and the iron loss P1.7/50 of 0.975 W/Kg. The evolution law of the microstructure and texture under different processes was analyzed with the help of OM, EBSD, and XRD. The results showed that the microstructure of the hot rolled plate was equiaxed crystals in the surface layer, a mixture of recrystallization grains and banded fiber in the quarter of the thickness layer, and banded fiber in the center layer. The texture gradient of the hot rolled plate from the surface layer to the center layer was {112}<111> + {110}<114> → {441}<014> → {001}~{111}<110>. The texture of the normalized plate was in major {110}<113> in the surface layer, diffuse α-fiber texture and {441}<014> in the quarter of the thickness layer, and sharp α texture {001}~{111}<110> in the center layer. The texture of the cold-rolled sheet was concentrated in {001}~{332}<110>. The average grain size of the decarburizing and nitriding sheet was 26.4 μm, and the texture of the first recrystallization is sharp α*-fiber and weak {111}<112>. The finished product has a sharp single Goss texture. For Hi-B steel, the Goss secondary nucleus originated from the surface layer to 1/4 layer of the hot rolled plate and reached the highest content of 11.5% in the quarter of the thickness. The content of the Goss texture decreased with the subsequent normalization and cold rolling, then the Goss grains nucleated again during the decarburization annealing and high temperature annealing processes. Full article
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<p>Test steel phase diagram.</p>
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<p>The standard ODF map (ϕ<sub>2</sub> = 45°).</p>
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<p>Microstructure of hot rolled plate. (<b>a</b>) Rolling direction; (<b>b</b>) transverse direction.</p>
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<p>Microstructure of normalized plate. (<b>a</b>) Rolling direction; (<b>b</b>) transverse direction.</p>
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<p>Microstructure of cold-rolled sheet (Red circles represent the shear bands).</p>
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<p>Microstructure of decarburized nitriding sheet.</p>
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<p>Macrostructure of finished product.</p>
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<p>Texture and orientation line at ϕ<sub>2</sub> = 45° section of hot rolled plate. Microstructure texture: (<b>a</b>) surface layer; (<b>b</b>) 1/4 layer; (<b>c</b>) center layer orientation line; (<b>d</b>) {110}; (<b>e</b>) ϕ<sub>1</sub> = 90°; (<b>f</b>) α-fiber.</p>
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<p>Texture and orientation line at ϕ<sub>2</sub> = 45° section of normalized plate orientation: (<b>a</b>) Surface layer; (<b>b</b>) 1/4 layer; (<b>c</b>) 1/2 layer orientation linel (<b>d</b>) {110}; (<b>e</b>) λ-fiber; (<b>f</b>) α-fiber.</p>
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<p>Texture at ϕ<sub>2</sub> = 45° section of cold sheet. (<b>a</b>) ODF map; (<b>b</b>) grain orientation map; (<b>c</b>) α-fiber line.</p>
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<p>Textures of decarburized nitriding sheet and finished product: (<b>a</b>) decarburized nitriding sheet; (<b>b</b>) finished product.</p>
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30 pages, 5801 KiB  
Article
Investigating Scale Effects on Experimental Shear Strength of Earthen Walls (Adobe and Rammed-Earth)
by Daniel M. Ruiz, Juan C. Reyes, Yezid A. Alvarado, Hermes Vacca, Nicola Tarque and Sandra Jerez
Buildings 2025, 15(5), 689; https://doi.org/10.3390/buildings15050689 - 22 Feb 2025
Viewed by 268
Abstract
This study investigates the scale effects on the experimental shear strength of earthen walls, a critical parameter influencing the seismic performance of adobe and rammed-earth (RE) buildings. Recognized for their historical significance and sustainable construction practices, earthen structures require a comprehensive understanding of [...] Read more.
This study investigates the scale effects on the experimental shear strength of earthen walls, a critical parameter influencing the seismic performance of adobe and rammed-earth (RE) buildings. Recognized for their historical significance and sustainable construction practices, earthen structures require a comprehensive understanding of their mechanical behavior under shear loads to ensure effective design and preservation. This research compiles data from over 120 in-plane shear wall tests (adobe and RE), nearly 20 direct shear tests from the scientific and technical literature, and new cyclic direct shear tests performed on large cubic specimens (300 mm side length) made from the same material as a previously tested two-story RE wall. Based on the findings, this study recommends a minimum specimen cross-sectional area of 0.5 m2 for reliable shear strength testing of earthen walls in structural laboratories. This recommendation aims to prevent the unconservative overestimation of shear strength commonly observed in smaller specimens, including direct shear tests. Furthermore, the Mohr–Coulomb failure criterion outlined in the AIS-610 Colombian standard is validated as a conservative lower bound for all compiled shear strength data. Cyclic direct shear tests on nine 300 mm cubic specimens produced a Mohr–Coulomb envelope with an apparent cohesion of 0.0715 MPa and a slope of 0.66, whereas the full-scale two-story wall (5.95 × 6.20 × 0.65 m) constructed with the same material exhibited a much lower cohesion of 0.0139 MPa and a slope of 0.26. The analysis reveals significant scale effects, as small-scale specimens consistently overestimate shear strength due to their inability to capture macro-structural behaviors such as compaction layer interactions, construction joint weaknesses, and stress redistributions. Based on the analysis of the compiled data, the novelty of this study lies in defining a strength reduction factor for direct shear tests (3.4–3.8 for rammed earth, ~3.0 for adobe) to align with full-scale wall behavior, as well as establishing a minimum specimen size (≥0.5 m2) for reliable in-plane shear testing of earthen walls, ensuring accurate structural assessments of shear strength. This study provides a first approach to the shear behavior of unstabilized earth. To expand its application, future research should explore how the scale of specimens with different stabilizers affects their shear strength. Full article
(This article belongs to the Special Issue Seismic Assessment of Unreinforced Masonry Buildings)
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<p>Proposed methodology to assess the shear strength of earthen walls (adobe and RE).</p>
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<p>(<b>a</b>) Overall dimensions of two of the biggest walls with openings found in the references consulted (new schematic illustrations based on previous research [<a href="#B11-buildings-15-00689" class="html-bibr">11</a>,<a href="#B23-buildings-15-00689" class="html-bibr">23</a>]; (<b>b</b>) example of calculation of the axial stress (<math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>)</mo> </mrow> </semantics></math> and shear strength <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>τ</mi> <mo>)</mo> </mrow> </semantics></math> at mid-height of an earthen wall.</p>
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<p>Histogram of frequencies of geometric variables: (<b>a</b>) thickness; (<b>b</b>) slenderness; (<b>c</b>) height; (<b>d</b>) length; (<b>e</b>) area; (<b>f</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>f</mi> </mrow> <mrow> <mi>m</mi> </mrow> <mrow> <mo>′</mo> </mrow> </msubsup> </mrow> </semantics></math>.</p>
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<p>Axial stress vs. shear stress according to the collected data from in-plane shear tests of earthen walls (adobe and RE): (<b>a</b>) adobe; (<b>b</b>) RE; (<b>c</b>) both adobe and RE. The mentioned reference is AIS (2017) [<a href="#B61-buildings-15-00689" class="html-bibr">61</a>].</p>
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<p>Shear strength vs. area of the tested walls according to the collected data of shear in-plane tests of earthen walls (adobe and RE): (<b>a</b>) adobe; (<b>b</b>) RE; (<b>c</b>) both adobe and RE with different axial stresses.</p>
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<p>Shear strength vs. area of the tested walls according to the collected data of shear in-plane tests of earthen walls (adobe and RE): (<b>a</b>) adobe; (<b>b</b>) RE; (<b>c</b>) both adobe and RE with different axial stresses.</p>
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<p>(<b>a</b>) Two-story RE wall; (<b>b</b>) experimental setup for pseudo-static test.</p>
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<p>(<b>a</b>) RE specimens with dimensions of 300 × 300 × 300 mm for direct cyclic shear test; (<b>b</b>) compression and shear specimens during drying process.</p>
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<p>(<b>a</b>) Photograph of the setup for the direct cyclic shear test performed on RE specimens; (<b>b</b>) components of the test setup; (<b>c</b>,<b>d</b>) spheres to support the cubic specimens in order to minimize friction in the test setup.</p>
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<p>(<b>a</b>) Horizontal load protocol (displacement controlled); (<b>b</b>) hysteresis loops for the three axial stresses considered.</p>
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<p>Final state of specimens subjected to cyclic direct shear tests under different axial stress levels.</p>
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<p>Comparative analysis of Mohr–Coulomb failure envelopes derived from direct shear tests (triangles) and shear strength values obtained from full-scale wall tests (diamonds): (<b>a</b>) rammed earth (RE), the mentioned references are Ruiz et al. (2023) [<a href="#B11-buildings-15-00689" class="html-bibr">11</a>] and Reyes et al. (2019) [<a href="#B23-buildings-15-00689" class="html-bibr">23</a>]; (<b>b</b>) adobe, the mentioned references are AIS &amp; CIMOC (2016) [<a href="#B68-buildings-15-00689" class="html-bibr">68</a>] and Reyes et al. (2019) [<a href="#B23-buildings-15-00689" class="html-bibr">23</a>].</p>
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<p>Comparison of shear strength data of earthen walls and Mohr–Coulomb failure envelopes from the scientific and technical literature. The mentioned reference is AIS (2017) [<a href="#B61-buildings-15-00689" class="html-bibr">61</a>].</p>
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17 pages, 4080 KiB  
Article
A Unified Winkler Model for Vertical and Lateral Dynamic Analysis of Tapered Piles in Layered Soils in the Frequency Domain
by Qiangqiang Shua, Huanliang Xu, Wenbo Tu, Mingkang Li and Ningzhuo Shi
Buildings 2025, 15(5), 651; https://doi.org/10.3390/buildings15050651 - 20 Feb 2025
Viewed by 172
Abstract
Tapered piles are a new type of pile foundation known for their simple construction and high bearing capacity, commonly used in railway, highway, or building foundation treatment. This study proposes a unified dynamic Winkler model for vertical and lateral vibration response of tapered [...] Read more.
Tapered piles are a new type of pile foundation known for their simple construction and high bearing capacity, commonly used in railway, highway, or building foundation treatment. This study proposes a unified dynamic Winkler model for vertical and lateral vibration response of tapered piles in the frequency domain using the impedance function transfer matrix method. The computational expressions are obtained for the different springs and damping of tapered piles with different dimensions using the elastodynamic theoretical of rigid embedded foundations, and the dynamic interaction mechanisms of vertical and lateral vibrations between tapered piles and soil are analyzed. The rationality of the simplified model is validated by comparison with existing literature and finite element simulation results. Finally, an example is provided to discuss the influences of the dimensional parameters of the pile and soil properties on vertical, lateral, and rocking dynamic impedance. The analytical findings demonstrate that the lateral and rocking dynamic impedances of tapered piles undergo a substantially greater enhancement relative to their vertical counterpart as the taper angle is progressively enlarged, assuming the pile volume remains constant. The dynamic impedance of tapered piles under vertical and lateral vibration in upper hard and lower weak soil layers, or upper weak and lower hard soil layers, are both greater than those in a homogeneous foundation. Specifically, the vertical dynamic stiffness of tapered piles in double-layered soils is approximately twice that of homogeneous soil. The rocking dynamic stiffness of the pile is significantly influenced by the soil properties around the pile foundation, whereas the soil properties have little impact on the rocking damping coefficient. Overall, the vertical dynamic characteristics are less influenced by the geometric features of the upper part of the tapered pile, while the lateral dynamic characteristics are significantly affected by these features. The lateral dynamic impedance of the tapered pile increases with the diameter of the upper part of the pile. Furthermore, the vertical, lateral, and rocking dynamic impedance of the pile can be effectively improved by enhancing the soil properties around its upper section. These results can provide theoretical references for the engineering practice. Full article
(This article belongs to the Special Issue Building Vibration and Soil Dynamics—2nd Edition)
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<p>Analytical model for vertical vibration of a tapered pile.</p>
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<p>Analytical model for lateral vibration of a tapered pile.</p>
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<p>Normalized vertical dynamic impedances versus the dimensionless frequency of a single cylindrical pile.</p>
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<p>Normalized lateral dynamic impedances versus the dimensionless frequency of the cylindrical pile.</p>
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<p>Dynamic vertical response of the tapered pile (<span class="html-italic">θ</span> = 1.5°): (<b>a</b>) end-bearing pile; (<b>b</b>) friction pile.</p>
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<p>Finite element mesh of tapered pile (<span class="html-italic">θ</span> = 1.5°).</p>
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<p>Lateral dynamic impedance of tapered pile (<span class="html-italic">θ</span> = 1.5°): (<b>a</b>) dynamic stiffness; (<b>b</b>) damping.</p>
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<p>Effect of taper angle on the vertical dynamic properties: (<b>a</b>) dynamic stiffness; (<b>b</b>) damping.</p>
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<p>Effect of taper angle on lateral dynamic properties: (<b>a</b>) dynamic stiffness (<b>b</b>) damping.</p>
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<p>Effect of taper angle on rocking dynamic properties: (<b>a</b>) dynamic stiffness; (<b>b</b>) damping.</p>
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<p>Illustration of double-layer soil foundations (case HW: upper hard and lower weak layered soil <span class="html-italic">h</span><sub>1</sub>/<span class="html-italic">h</span><sub>2</sub> = 1.0, <span class="html-italic">E<sub>s</sub></span><sub>1</sub>/<span class="html-italic">E<sub>s</sub></span><sub>2</sub> = 4, <span class="html-italic">E<sub>p</sub></span>/<span class="html-italic">E<sub>s</sub></span><sub>2</sub> = 1000; case WH: upper weak and lower hard layered soil <span class="html-italic">h</span><sub>1</sub>/<span class="html-italic">h</span><sub>2</sub> = 1.0, <span class="html-italic">E<sub>s</sub></span><sub>1</sub>/<span class="html-italic">E<sub>s</sub></span><sub>2</sub> = 0.25, <span class="html-italic">E<sub>p</sub></span>/<span class="html-italic">E<sub>s</sub></span><sub>1</sub> = 1000).</p>
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<p>The vertical dynamic impedance of the tapered pile (<span class="html-italic">θ</span> = 1.5°): (<b>a</b>) dynamic stiffness; (<b>b</b>) damping.</p>
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<p>The lateral dynamic impedance of tapered pile in (<span class="html-italic">θ</span> = 1.5°): (<b>a</b>) dynamic stiffness; (<b>b</b>) damping.</p>
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<p>The rocking dynamic impedance of tapered pile (<span class="html-italic">θ</span> = 1.5°): (<b>a</b>) dynamic stiffness; (<b>b</b>) damping.</p>
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16 pages, 4171 KiB  
Article
Study on the Impact of Seepage Filtration Under Wet–Dry Cycles on the Stability of Mudstone Limestone Slopes
by Rui Li, Puyi Wang, Xiang Lu, Wei Zhou, Yihan Guo, Rongbo Lei, Zixiong Zhao, Ziyu Liu and Yu Tian
Water 2025, 17(4), 592; https://doi.org/10.3390/w17040592 - 18 Feb 2025
Viewed by 287
Abstract
Open-pit mining often exposes weak rock layers, the strength of which significantly affects the stability of slopes. If these rock layers are also prone to disintegration and expansion, cyclic rainfall can exacerbate instability. Rainfall-induced changes in the seepage field also indirectly threaten the [...] Read more.
Open-pit mining often exposes weak rock layers, the strength of which significantly affects the stability of slopes. If these rock layers are also prone to disintegration and expansion, cyclic rainfall can exacerbate instability. Rainfall-induced changes in the seepage field also indirectly threaten the stability of slopes. Therefore, investigating the characteristics of mudstone limestone and the impact of the seepage field on slope instability under different wet–dry cycles is of great significance for the safe mining of open-pit mines. This paper takes the mudstone limestone slope of a certain open-pit mine in the southwest as the starting point and conducts experiments on saturated density, water absorption rate, permeability coefficient, compressive strength, and variable angle shear strength. Combined with scanning electron microscopy and phase analysis of X-ray diffraction analysis, the macroscopic and microscopic characteristics of the samples are comprehensively analyzed. FLAC3D software is used to explore the changes in the seepage field and the mechanism of instability. Our research found that for the preparation of mudstone limestone samples, a particle size of less than 1 mm and a drying temperature of 50 °C are optimal, with specific values for initial natural and saturated density, and natural water content. As the number of wet–dry cycles increases, the saturated density of mudstone limestone increases; the water absorption rate first rises sharply and then rises slowly; the permeability coefficient first rises sharply and then stabilizes, finally dropping sharply; the compressive and shear strength decreases slowly, and the internal friction angle changes little; frequent cycles also lead to mudification and seepage filtration. At the microscopic level, pores become larger and more regular, and the distribution is more concentrated; changes in mineral content weaken the strength. Combined with numerical simulation, the changes in the seepage field at the bottom of the slope exceed those at the slope surface and top, the transient saturated area expands, and the overall and local slope stability coefficients gradually decrease. During the third cycle, the local stability is lower than the overall stability, and the landslide trend shifts. In conclusion, wet–dry cycles change the pores and mineral content, affecting the physical and mechanical properties, leading to the deterioration of the transient saturated area, a decrease in matrix suction, and an increase in surface gravity, eventually causing slope instability. Full article
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<p>Diagram of the pilot study programme.</p>
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<p>Flow chart of the test.</p>
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<p>Flowchart of the wet and dry cycle scheme.</p>
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<p>Slope status and model establishment.</p>
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<p>Variation curve of specimen shear strength parameters.</p>
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<p>Effect of the number of wet and dry cycles on porosity and pore diameter.</p>
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<p>X-ray diffraction physical image analysis.</p>
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<p>Changes in seepage field in argillaceous limestone slopes after different rainfall cycles.</p>
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<p>Pore water pressure distribution of argillaceous limestone slope detection line under dry and wet cycle.</p>
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<p>Patterns of change between stability coefficients and depths of infiltration lines.</p>
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27 pages, 2579 KiB  
Article
Assembly Quality Control Technologies in Forced Clamping and Compensation Processes for Large and Integrated Aeronautical Composite Structures
by Feiyan Guo, Qiangwei Bao, Jialiang Liu and Xiliang Sha
Machines 2025, 13(2), 159; https://doi.org/10.3390/machines13020159 - 18 Feb 2025
Viewed by 207
Abstract
For the new type of CFRP (Carbon Fiber Reinforced Plastic) thin-walled components with a large size and weak rigid structure, due to the integration of geometric features and the reduction in the amount of parts, the assembly size transmission chain is short compared [...] Read more.
For the new type of CFRP (Carbon Fiber Reinforced Plastic) thin-walled components with a large size and weak rigid structure, due to the integration of geometric features and the reduction in the amount of parts, the assembly size transmission chain is short compared to traditional metal assembly structures. In addition, the manufacturing errors and layer parameters of large composite parts in different regions are different, and they also have a lower forming accuracy. For the current assembly method that mainly concerns geometric dimensions and tolerances, it is difficult to support precise analysis and accurate geometric error forms for different local and global regions. As a result, in practical engineering, the forced method of applying a local clamping force is inevitably adopted to passively reduce and compensate for assembly errors. However, uneven stress distribution and possible internal damage occur. To avoid the assembly quality problems caused by forced clamping operations, the research status on the optimization of forced clamping process parameters before assembly, the flexible position–force adjustment of fixtures during assembly, and gap compensation and strengthening before assembly completion was analyzed systematically. The relevant key technologies, such as force limit setting, geometric gap reduction, stress/damage evolution prediction, the reverse optimization of clamping process parameters, and precise stress/damage measurement, are proposed and resolved in this paper. With the specific implementation solutions, geometric and mechanical assembly status coupling analysis, active control, and a collaborative guarantee could be achieved. Finally, future research work is proposed, i.e., dynamic evolution behavior modeling and the equalization of the induction and control of physical assembly states. Full article
(This article belongs to the Section Advanced Manufacturing)
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<p>Large-scale and integrated aviation CFRP structure (fuselage panels of Boeing 787 and A350-XWB) [<a href="#B2-machines-13-00159" class="html-bibr">2</a>,<a href="#B3-machines-13-00159" class="html-bibr">3</a>].</p>
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<p>Typical forced positioning and clamping scenarios in practical engineering.</p>
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<p>Position deviation of the panel shape control points in the X, Y, and Z directions before and after correction [<a href="#B22-machines-13-00159" class="html-bibr">22</a>].</p>
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<p>Comparison of effective contacting areas of joining surfaces [<a href="#B43-machines-13-00159" class="html-bibr">43</a>].</p>
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<p>Method for setting the limit value of the forced assembly load.</p>
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<p>Method for eliminating geometric gaps during forced clamping process.</p>
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<p>Prediction method for internal stress and damage evolution during forced clamping process.</p>
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<p>Optimization of process parameters for forced clamping with reverse calculation.</p>
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<p>Precise measurement for internal assembly stress and damage of CFRP structure.</p>
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21 pages, 15426 KiB  
Article
Numerical Simulation on Aerodynamic Noise of (K)TS Control Valves in Natural Gas Transmission and Distribution Stations in Southwest China
by Xiaobo Feng, Lu Yu, Hui Cao, Ling Zhang, Yizhi Pei, Jingchen Wu, Wenhao Yang and Junmin Gao
Energies 2025, 18(4), 968; https://doi.org/10.3390/en18040968 - 17 Feb 2025
Viewed by 264
Abstract
Fluid dynamic noise produced by eddy disturbances and friction along pipe walls poses a significant challenge in natural gas transmission and distribution stations. (K)TS control valves are widely used in natural gas transmission and distribution stations across Southwest China and are among the [...] Read more.
Fluid dynamic noise produced by eddy disturbances and friction along pipe walls poses a significant challenge in natural gas transmission and distribution stations. (K)TS control valves are widely used in natural gas transmission and distribution stations across Southwest China and are among the primary sources of noise in these facilities. In this study, a 3D geometric model of the (K)TS valve was developed, and the gas flow characteristics were simulated to analyze the gas flow field and sound field within the valve under varying pipeline flow velocities, outlet pressures, and valve openings. The results demonstrate that accurate calculations of the 3D valve model can be achieved with a grid cell size of 3.6 mm and a boundary layer set to 3. The noise-generating regions of the valve are concentrated around the throttle port, valve chamber, and valve inlet. The primary factors contributing to the aerodynamic noise include high gas flow velocity gradients, intense turbulence, rapid turbulent energy dissipation, and vortex formation and shedding within the valve. An increase in inlet flow velocity intensifies turbulence and energy dissipation inside the valve, while valve opening primarily influences the size of vortex rings in the valve chamber and throttle outlet. In contrast, outlet pressure exerts a relatively weak effect on the flow field characteristics within the valve. Under varying operating conditions, the noise directivity distribution remains consistent, exhibiting symmetrical patterns along the central axis of the flow channel and forming six-leaf or four-leaf flower shapes. As the distance from the monitoring point to the valve increases, noise propagation becomes more concentrated in the vertical direction of the valve. These findings provide a theoretical basis for understanding the mechanisms of aerodynamic noise generation within (K)TS control valves during natural gas transmission, and can also offer guidance for designing noise reduction solutions for valves. Full article
(This article belongs to the Topic Oil and Gas Pipeline Network for Industrial Applications)
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<p>Valve structure (<b>A</b>) and the positive profile of valve pipeline and flow channel extraction (<b>B</b>).</p>
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<p>Schematic diagram of simulation calculation process.</p>
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<p>The polyhedral grids of flow channel (<b>a</b>, <b>b</b>, <b>c</b>, <b>d</b>, <b>e</b>, <b>f</b>, and <b>g</b> represent grids 1, 2, 3, 4, 5, 6, and 7, respectively).</p>
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<p>The variation of flow rate and the calculated deviation with the number of grids.</p>
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<p>Comparison of the SPL between simulation and actual measurement at a distance of 1 m from the valve.</p>
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<p>(<b>A</b>) Velocity profile of different sections in the flow channel. (<b>B</b>) Static pressure distribution diagram of different sections. (<b>C</b>) Dynamic pressure distribution diagram of different sections.</p>
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<p>(<b>A</b>) The vortex structure in flow channel. (<b>B</b>) The distribution of turbulent kinetic energy and turbulent dissipation rate at section.</p>
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<p>(<b>A</b>) Velocity profile of different sections in the flow channel. (<b>B</b>) Vortex structure under different inlet flow velocities.</p>
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<p>(<b>A</b>) Velocity distribution at section X = 0 under different outlet pressures. (<b>B</b>) Vortex structure under different outlet pressures.</p>
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<p>Changes in velocity (<b>A</b>), turbulence kinetic energy (<b>B</b>), and turbulence dissipation rate (<b>C</b>) on streamlines under different outlet pressure.</p>
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<p>(<b>A</b>) Velocity distribution of different valve positions at section X = 0 and Z = 0. (<b>B</b>) Vortex structure with different valve openings.</p>
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<p>Sound power level distribution at section X = 0 under different operating conditions.</p>
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<p>Sound power levels varying with Z-coordinates under different operating conditions: (<b>A</b>) sound power levels at different pipe flow velocities; (<b>B</b>) sound power levels at different outlet pressures; (<b>C</b>) sound power levels with different valve openings.</p>
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<p>(<b>A</b>) Time-domain fluctuation distribution of sound pressure under different flow velocities. (<b>B</b>) Time-domain fluctuation distribution of sound pressure under different outlet pressures. (<b>C</b>) Time-domain fluctuation distribution of sound pressure with different valve openings.</p>
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<p>Directional analysis of noise under different flow velocities, outlet pressures, and valve openings.</p>
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22 pages, 12382 KiB  
Article
Productivity Evaluation Method for Offshore Thick–Thin Interbedded Reservoirs Based on Graph Attention Multilayer Perceptron
by Bin Jiang, Shiqing Cheng, Yinliang Shi and Ruikai Duan
Processes 2025, 13(2), 525; https://doi.org/10.3390/pr13020525 - 13 Feb 2025
Viewed by 322
Abstract
Offshore multilayer sandstone reservoirs are characterized by complex vertical alternating thick and thin layers, resulting in significant heterogeneity. Traditional productivity evaluation methods often fail to effectively represent the dynamic production patterns of individual wells. This study focuses on the S oilfield offshore (Bohai [...] Read more.
Offshore multilayer sandstone reservoirs are characterized by complex vertical alternating thick and thin layers, resulting in significant heterogeneity. Traditional productivity evaluation methods often fail to effectively represent the dynamic production patterns of individual wells. This study focuses on the S oilfield offshore (Bohai Bay, China) as a case study. By considering the structural characteristics of thin layers and sand bodies, the reservoir is classified into four types: strong continuous thick layers, weak continuous thick layers, alternating thick–thin layers, and weak continuous thin layers. Based on this classification, a multilayer perceptron classification model based on graph attention neural networks is developed. The model achieves a high classification accuracy of 96.6% by mining the interdependencies between 14 input parameters. Further, by fitting the relationship between interlayer interference coefficients and water cuts for typical wells, a dynamic variation diagnosis plot for interlayer interference coefficients under different reservoir combinations is established. Additionally, a calculation method for the oil productivity index based on reservoir combination patterns is proposed. The method’s effectiveness was validated through field application, where the results significantly improved the correlation between the water-free oil productivity index and flow coefficient, with calculation errors of less than 10% compared to measured values. Full article
(This article belongs to the Section Energy Systems)
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<p>The geographic location of S oilfield.</p>
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<p>Planar distribution pattern map of sandbodies in S oilfield.</p>
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<p>The well-tie profiles of the S oilfield from southwest to northeast.</p>
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<p>Typical interwell profile of multi-stage strong continuous thick layers from southwest to northeast.</p>
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<p>Typical interwell profile of single-stage strong continuous thick layers from southwest to northeast.</p>
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<p>Typical interwell profile of single-stage weak continuous medium–thick layers from southwest to northeast.</p>
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<p>Typical interwell profile of multi-stage weak continuous medium–thick layers from southwest to northeast.</p>
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<p>Typical interwell profile of multi-stage weak continuous thin layers from southwest to northeast.</p>
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<p>Typical interwell profile of single-stage weak continuous thin layers from southwest to northeast.</p>
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<p>Typical wellbore profile of single-phase discontinuous thin layer from southwest to northeast.</p>
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<p>Typical well profiles of the combination modes of thick and thin reservoirs in S oilfield.</p>
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<p>Structure of graph attention-based multilayer perceptron model.</p>
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<p>Results of model experiments with SGD algorithm. (<b>a</b>) The precision and loss value of SGD algorithm with <span class="html-italic">η</span> = 0.1. (<b>b</b>) The precision and loss value of SGD algorithm with <span class="html-italic">η</span> = 0.01. (<b>c</b>) The precision and loss value of SGD algorithm with <span class="html-italic">η</span> = 0.001.</p>
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<p>Results of model experiments with SGD algorithm. (<b>a</b>) The precision and loss value of SGD algorithm with <span class="html-italic">η</span> = 0.1. (<b>b</b>) The precision and loss value of SGD algorithm with <span class="html-italic">η</span> = 0.01. (<b>c</b>) The precision and loss value of SGD algorithm with <span class="html-italic">η</span> = 0.001.</p>
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<p>Results of model experiments with the Adam algorithm. (<b>a</b>) The precision of the Adam algorithm. (<b>b</b>) The loss value of the Adam algorithm.</p>
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<p>The comparison result of graph attentioned multi-layer perceptron model and multi-layer perceptron model. (<b>a</b>) The precision of MLP and GMLLP. (<b>b</b>) The loss value of MLP and GMLLP.</p>
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<p>The matching of changes of interlayer interference coefficient with water content in typical wells with four combination patterns.</p>
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<p>Type curve of interlayer interference coefficient and water cut under different reservoir combinations.</p>
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<p>Diagram of the relationship between oil recovery index with no water production and flow coefficient under different reservoir combinations.</p>
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<p>Comparisons of calculated production index and field-measured production index for infill wells.</p>
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15 pages, 6734 KiB  
Article
Self-Assembled Sandwich-like Mixed Matrix Membrane of Defective Zr-MOF for Efficient Gas Separation
by Yuning Li, Xinya Wang, Weiqiu Huang, Xufei Li, Ping Xia, Xiaochi Xu and Fangrui Feng
Nanomaterials 2025, 15(4), 279; https://doi.org/10.3390/nano15040279 - 12 Feb 2025
Viewed by 497
Abstract
Membrane technology has been widely used in industrial CO2 capturing, gas purification and gas separation, arousing attention due to its advantages of high efficiency, energy saving and environmental protection. In the context of reducing global carbon emissions and combating climate change, it [...] Read more.
Membrane technology has been widely used in industrial CO2 capturing, gas purification and gas separation, arousing attention due to its advantages of high efficiency, energy saving and environmental protection. In the context of reducing global carbon emissions and combating climate change, it is particularly important to capture and separate greenhouse gasses such as CO2. Zr-MOF can be used as a multi-dimensional modification on the polymer membrane to prepare self-assembled MOF-based mixed matrix membranes (MMMs), aiming at the problem of weak adhesion or bonding force between the separation layer and the porous carrier. When defective UiO-66 is applied to PVDF membrane as a functional layer, the CO2 separation performance of the PVDF membrane is significantly improved. TUT-UiO-3-TTN@PVDF has a CO2 permeation flux of 14,294 GPU and a selectivity of 27 for CO2/N2 and 18 for CO2/CH4, respectively. The CO2 permeability and selectivity of the membrane exhibited change after 40 h of continuous operation, significantly improving the gas separation performance and showing exceptional stability for large-scale applications. Full article
(This article belongs to the Special Issue Advances in Polymer Nanofilms)
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<p>A schematic of the TUT-UiO-TTN@PVDF membrane was prepared by the strategies of step assembly, alternate complexation and cooperative coordination.</p>
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<p>SEM images of the (<b>a</b>) surface and (<b>e</b>) section of PVDF, (<b>b</b>,<b>f</b>) of TUT-UiO-TTN@PVDF, (<b>c</b>,<b>g</b>) of TUT-UiO-TTN@SPVDF, (<b>d</b>,<b>h</b>) TUT-UiO-TTN@PP, (<b>i</b>) UiO-66-NH<sub>2</sub> and d-UiO-3; (<b>j</b>) an EDS analysis of the TUT-UiO-3-TTN@PVDF samples.</p>
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<p>(<b>a</b>) Porosity of TUT−UiO−TTN@PVDF, (<b>b</b>) N<sub>2</sub> adsorption−desorption isotherm, and (<b>c</b>) pore size distribution.</p>
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<p>XRD images of (<b>a</b>) d−UiO−4 and UiO−66 and (<b>b</b>) d−UiO−4@TUT−PVDF; (<b>c</b>) FT−IR images of d−UiO−4, UiO−66−NH<sub>2</sub> and TUT−UiO−4−TTN@PVDF.</p>
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<p>XPS of (<b>a</b>) the TUT-UiO-4-TTN@PVDF membrane for (<b>b</b>) C1s, (<b>c</b>) N1s, (<b>d</b>) O1s, (<b>e</b>) Zr3d, (<b>f</b>) F1s and (<b>g</b>) Ti2p.</p>
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<p>Contact angle of the (<b>a</b>) TUT-UiO-TTN@PVDF, (<b>b</b>) tensile strength and elongation, mechanical strength of (<b>c</b>) TUT-UiO-4-TTN@PVDF membrane, (<b>d</b>) TUT-UiO-4-TTN@SPVDF and (<b>e</b>) TUT-UiO-4-TTN@PPwith original membrane.</p>
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<p>TGA images of (<b>a</b>) a TUT-UiO-4-TTN@PVDF membrane, (<b>b</b>) a TUT-UiO-4-TTN@SPVDF membrane and (<b>c</b>) a TUT-UiO-4-TTN@PP membrane.</p>
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<p>(<b>a</b>) The adsorption curves of CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub> of d-UiO-3; (<b>b</b>) the permeation flux and selectivity of TUT-UiO-3-TTN@PVDF for H<sub>2</sub>, CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>; (<b>c</b>) the permeability and (<b>d</b>) selectivity of TUT-PVDF with different defect degrees; comparison of the (<b>e</b>) CO<sub>2</sub>/N<sub>2</sub> and (<b>f</b>) CO<sub>2</sub>/CH<sub>4</sub> selectivity of TUT-UiO-4-TTN@PVDF with other MOF-containing MMMs.</p>
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<p>(<b>a</b>) The permeability of the TUT-UiO-4-TTN@PVDF membrane at different temperatures; (<b>b</b>) the selectivity, osmotic activation energy of the membrane for (<b>c</b>) CO<sub>2</sub>, (<b>d</b>) N<sub>2</sub> and (<b>e</b>) CH<sub>4</sub>; and (<b>f</b>) the long-term stability of the TUT-UiO-TTN@PVDF membrane.</p>
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<p>(<b>a</b>) The permeability and selectivity of the TUT-UiO-4-TTN@PVDF membrane for the mixture, (<b>b</b>) d-UiO crystal model, (<b>c</b>) d-UiO adsorption simulation of CO<sub>2</sub>/N<sub>2</sub>, (<b>d</b>) d-UiO adsorption simulation of CO<sub>2</sub>/CH<sub>4</sub>, (<b>e</b>) UiO-66-NH<sub>2</sub> crystal model, (<b>f</b>) adsorption simulation of CO<sub>2</sub>/N<sub>2</sub> by UiO-66-NH<sub>2</sub>, (<b>g</b>) adsorption simulation of CO<sub>2</sub>/CH<sub>4</sub> by UiO-66-NH<sub>2</sub>, (<b>h</b>) schematic diagram of selective gas adsorption by d-UiO, (<b>i</b>) interaction energy of gas adsorption by d-UiO and UiO-66-NH<sub>2</sub>.</p>
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25 pages, 13698 KiB  
Article
Self-Supervised Foundation Model for Template Matching
by Anton Hristov, Dimo Dimov and Maria Nisheva-Pavlova
Big Data Cogn. Comput. 2025, 9(2), 38; https://doi.org/10.3390/bdcc9020038 - 11 Feb 2025
Viewed by 442
Abstract
Finding a template location in a query image is a fundamental problem in many computer vision applications, such as localization of known objects, image registration, image matching, and object tracking. Currently available methods fail when insufficient training data are available or big variations [...] Read more.
Finding a template location in a query image is a fundamental problem in many computer vision applications, such as localization of known objects, image registration, image matching, and object tracking. Currently available methods fail when insufficient training data are available or big variations in the textures, different modalities, and weak visual features exist in the images, leading to limited applications on real-world tasks. We introduce Self-Supervised Foundation Model for Template Matching (Self-TM), a novel end-to-end approach to self-supervised learning template matching. The idea behind Self-TM is to learn hierarchical features incorporating localization properties from images without any annotations. As going deeper in the convolutional neural network (CNN) layers, their filters begin to react to more complex structures and their receptive fields increase. This leads to loss of localization information in contrast to the early layers. The hierarchical propagation of the last layers back to the first layer results in precise template localization. Due to its zero-shot generalization capabilities on tasks such as image retrieval, dense template matching, and sparse image matching, our pre-trained model can be classified as a foundation one. Full article
(This article belongs to the Special Issue Perception and Detection of Intelligent Vision)
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<p>Illustration of Self-TM.</p>
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<p>Illustration of a receptive field, <math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>F</mi> <mrow> <mi>p</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> <mo>_</mo> <msub> <mi>p</mi> <mi>N</mi> </msub> </mrow> </msub> </mrow> </semantics></math>, in layer <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> (in orange) of a detected maximum value, <math display="inline"><semantics> <mrow> <mi>p</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> <mo>_</mo> <msub> <mi>p</mi> <mi>N</mi> </msub> </mrow> </semantics></math>, in layer <math display="inline"><semantics> <mi>N</mi> </semantics></math> (in red).</p>
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<p>Visual representation of results on Hpatches (values, excluding those for Self-TM, are taken from Twin-Net [<a href="#B61-BDCC-09-00038" class="html-bibr">61</a>]): (<b>a</b>) patch verification task; (<b>b</b>) image matching task; (<b>c</b>) patch retrieval task. The methods are grouped into the following groups: “handcrafted”, which were manually created by their authors; “supervised”, which used annotated data for their training; “self-supervised”, which did not use any annotations. A plus (+) denotes Self-TM models that are finetuned on the Hpatches dataset, and similarly (*) denotes variations of Tfear models.</p>
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<p>Visual representation of results on Hpatches (values, excluding those for Self-TM, are taken from Twin-Net [<a href="#B61-BDCC-09-00038" class="html-bibr">61</a>]): (<b>a</b>) patch verification task; (<b>b</b>) image matching task; (<b>c</b>) patch retrieval task. The methods are grouped into the following groups: “handcrafted”, which were manually created by their authors; “supervised”, which used annotated data for their training; “self-supervised”, which did not use any annotations. A plus (+) denotes Self-TM models that are finetuned on the Hpatches dataset, and similarly (*) denotes variations of Tfear models.</p>
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<p>Comparison of OmniGlue [<a href="#B34-BDCC-09-00038" class="html-bibr">34</a>] (<b>a</b>) and OmniGlue + Self-TM Base (<b>b</b>) in finding keypoint matches in an image with out-of-training-domain modality. For the purpose of visualization, matches with high “confidence” are not visualized to make the errors visible. The correct matches are shown in green color, respectively the incorrect matches in red color.</p>
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21 pages, 18532 KiB  
Article
Cesium-137 Distribution Patterns in Bottom Sediments of Beaver Ponds in Small Rivers in the North of the Volga Upland, European Russia
by Artyom V. Gusarov, Aidar G. Sharifullin, Achim A. Beylich and Fedor N. Lisetskii
Water 2025, 17(4), 503; https://doi.org/10.3390/w17040503 - 11 Feb 2025
Viewed by 517
Abstract
This paper presents the results of the analysis of the redistribution of cesium-137 (137Cs) in the bottom sediments of beaver ponds in two small rivers in the forest-steppe north of the Volga Upland, which is one of the most contaminated areas [...] Read more.
This paper presents the results of the analysis of the redistribution of cesium-137 (137Cs) in the bottom sediments of beaver ponds in two small rivers in the forest-steppe north of the Volga Upland, which is one of the most contaminated areas of the Middle Volga region (European Russia) with artificial radionuclides. This study is based on fieldwork materials, laboratory analyses of the specific radioactivity of 137Cs in soil and bottom sediment samples, their granulometric composition, and the content of organic matter in them. The obtained results indicate a significant decrease in the specific activity of 137Cs in the direction from near-water-divide surface soils (on average, 54 Bq/kg) to the bottom sediments of beaver ponds of the studied rivers (on average, no more than 6 Bq/kg). A weak (statistically insignificant) tendency towards a decrease in the specific activity of 137Cs in the bottom sediments of beaver ponds downstream of rivers was also revealed. With this detected trend, no statistically significant relationship was found between changes in 137Cs and changes in the granulometric composition of bottom sediments. However, a relatively good relationship was identified with changes in the content of total organic matter. The stage-by-stage accumulation of sediment thickness in one of the beaver ponds was revealed, with the highest concentration of 137Cs in the layer with the highest content of finely dispersed fractions and organic matter. The obtained results indicate that for a correct quantitative assessment of the migration of pollutants (including radioactive ones) in floodplain-channel systems, it is necessary to consider beaver structures (primarily ponds), which act as zones of their intensive accumulation. Full article
(This article belongs to the Special Issue Hydrodynamics and Sediment Transport in the Coastal Zone)
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<p>Contamination of Eastern Europe (<b>left</b>) and the Republic of Tatarstan (<b>right</b>) with radioactive cesium-137 immediately after its fallout in 1986 [<a href="#B10-water-17-00503" class="html-bibr">10</a>]. <span class="html-italic">1</span>—location of the Republic of Tatarstan, which is part of the Volga Federal District; <span class="html-italic">2</span>—location of the Chernobyl Nuclear Power Plant; <span class="html-italic">3</span>—the study area, <span class="html-italic">4</span>—the total cesium-137 deposition (in kBq/m<sup>2</sup>): <span class="html-italic">a</span>—2–4, <span class="html-italic">b</span>—4–10, <span class="html-italic">c</span>—10–20, <span class="html-italic">d</span>—20–40, <span class="html-italic">e</span>—40–100.</p>
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<p>(<b>A</b>) Location of the studied objects, (<b>B</b>) soil and bottom sediment sampling sites, and (<b>C</b>) <sup>137</sup>Cs’s specific activity in them. <span class="html-italic">1</span>—location of the studied objects, <span class="html-italic">2</span>—boundaries of the studied river basins, <span class="html-italic">3</span>—water bodies (ponds and reservoirs), <span class="html-italic">4</span>—river network, <span class="html-italic">5</span>—contours (drawn every 20 m), <span class="html-italic">6</span>—locations of sampling points (a—riverbed and low floodplain flooded by ponds, b—drained high floodplain, c—lowest floodplain terrace, d—river valley slope, e—near-water-divide surface); <span class="html-italic">7</span>—specific activity of <sup>137</sup>Cs, Bq/kg (a—0–10, b—10–50, c—50 and more).</p>
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<p>Processes of sampling bottom sediments and soils in beaver ponds of the Morkvashinka and Morkvashka rivers and their interfluves.</p>
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<p>Changes in the specific activity of <sup>137</sup>Cs (<span class="html-italic">A</span>) in the bottom sediments of beaver ponds along the Morkvashinka and Morkvashka rivers (Pre-Volga Region of the Republic of Tatarstan). <span class="html-italic">L</span> is the horizontal distance; <span class="html-italic">h</span> is the absolute elevation; <span class="html-italic">R</span>(…) and <span class="html-italic">L</span>(…) are the right and left tributaries of the rivers, respectively, with their numbering (<span class="html-italic">p</span> and <span class="html-italic">t</span> are permanent and temporary watercourses); <span class="html-italic">α</span> is the average slope of the channel; AP is an anthropogenic pond; <span class="html-italic">1</span> is the location of beaver dams; <span class="html-italic">2</span> is the location of the mouths of the tributaries; <span class="html-italic">3a</span> is the sediments in the mouths of the Morkvashinka and Morkvashka rivers in the backwater zone of the Kuybyshev Reservoir (for comparison); <span class="html-italic">3b</span> is the sediments in the ponds (mostly drained) without dams, presumably of beaver origin; <span class="html-italic">3c</span> is the sediments in the flooded ponds.</p>
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<p>Changes in the specific activity of <sup>137</sup>Cs (<span class="html-italic">A</span>), total organic matter content (<span class="html-italic">O</span>), and granulometric composition (<span class="html-italic">P</span>) in bottom sediments (near-surface layers) of beaver ponds along the Morkvashinka River. <span class="html-italic">L</span> is the distance along the river from its mouth (see <a href="#water-17-00503-f004" class="html-fig">Figure 4</a>); <span class="html-italic">R</span><sup>2</sup> is the linear trend approximation coefficient (dashed line).</p>
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<p>Changes in water discharges (<span class="html-italic">Q</span>, L/s) and suspended sediment loads (<span class="html-italic">W</span>, mg/s) along the Morkvashka (18 April 2023) and Morkvashinka (15 April 2023) rivers. <span class="html-italic">L</span> is the distance along the river from its mouth (see <a href="#water-17-00503-f004" class="html-fig">Figure 4</a>); <span class="html-italic">R</span><sup>2</sup> is the linear trend approximation coefficient (dashed line).</p>
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<p>Changes in the average specific activity of <sup>137</sup>Cs, particle size distribution, and total organic content in three morphologically distinct layers of bottom sediments in one of the beaver ponds (dried up at the fieldwork) in the lower reaches of the Morkvashinka River. <span class="html-italic">Note:</span> layer III lies directly on the surface of the river’s carbonate rubble alluvium.</p>
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<p>Changes in some parameters of flood water discharge and suspended (+bedload) sediment load of the Myosha River at Pestretsy (Western Pre-Kama Region, Republic of Tatarstan) in 2008–2022. <span class="html-italic">Q</span> is the water discharge (<span class="html-italic">Q</span><sub>s</sub> is the highest monthly average of the year; <span class="html-italic">Q</span><sub>max</sub> is the annual maximum); <span class="html-italic">W</span> is the suspended (+bedload) sediment load (<span class="html-italic">W</span><sub>s</sub> is the highest monthly average of the year; <span class="html-italic">W</span><sub>dec</sub> is the highest ten-day average in the month with the highest sediment load of the year; <span class="html-italic">W</span><sub>max</sub> is the annual maximum); <span class="html-italic">R</span><sup>2</sup> is the approximation coefficient of the 5-degree polynomial trend line (<span class="html-italic">Tr</span>).</p>
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<p>An example of a section of the dried-up riverbed of the lower reaches of the Morkvashinka River, composed of weakly rounded rubble alluvium, during the low-water period (July–September) in 2022.</p>
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<p>One of the dried-up beaver ponds with characteristic fine-grained bottom sediments in the lower reaches of the Morkvashinka River during the low-water period (July–September) in 2022.</p>
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15 pages, 9164 KiB  
Article
Image Quality in Adaptive Optics Optical Coherence Tomography of Diabetic Patients
by Elisabeth Brunner, Laura Kunze, Wolfgang Drexler, Andreas Pollreisz and Michael Pircher
Diagnostics 2025, 15(4), 429; https://doi.org/10.3390/diagnostics15040429 - 10 Feb 2025
Viewed by 315
Abstract
Background/Objectives: An assessment of the retinal image quality in adaptive optics optical coherence tomography (AO-OCT) is challenging. Many factors influence AO-OCT imaging performance, leading to greatly varying imaging results, even in the same subject. The aim of this study is to introduce [...] Read more.
Background/Objectives: An assessment of the retinal image quality in adaptive optics optical coherence tomography (AO-OCT) is challenging. Many factors influence AO-OCT imaging performance, leading to greatly varying imaging results, even in the same subject. The aim of this study is to introduce quantitative means for an assessment of AO-OCT image quality and to compare these with parameters retrieved from the pyramid wavefront sensor of the system. Methods: We used a spectral domain AO-OCT instrument to repetitively image six patients suffering from diabetic retinopathy over a time span of one year. The data evaluation consists of two volume acquisitions with a focus on the photoreceptor layer, each at five different retinal locations per visit; 7–8 visits per patient are included in this data analysis, resulting in a total of ~420 volumes. Results: A large variability in AO-OCT image quality is observed between subjects and between visits of the same subject. On average, the image quality does not depend on the measurement location. The data show a moderate correlation between the axial position of the volume recording and image quality. The correlation between pupil size and AO-OCT image quality is not linear. A weak correlation is found between the signal-to-noise ratio of the wavefront sensor image and the image quality. Conclusions: The introduced AO-OCT image quality metric gives useful insights into the performance of such a system. A longitudinal assessment of this metric, together with wavefront sensor data, is essential to identify factors influencing image quality and, in the next step, to optimize the performance of AO-OCT systems. Full article
(This article belongs to the Special Issue High-Resolution Retinal Imaging: Hot Topics and Recent Developments)
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<p>Color fundus images of diabetic retinopathy patients indicating the AO-OCT imaging locations.</p>
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<p>Image data of a healthy volunteer and processing steps for estimating the image quality in AO-OCT. (<b>a</b>) Recorded and motion-corrected volume data (green dashed line indicates the position of the B-scan shown in (<b>c</b>), arrows indicate the integration depth to generate the en-face scan shown in (<b>b</b>)). (<b>b</b>) Extracted en-face image of the photoreceptors where <span class="html-italic">CoV</span> and mean are calculated. (<b>c</b>) Extracted B-scan (green arrows indicate the depth integration range for the en-face scan, the red arrow indicates the direction of the <span class="html-italic">CoV</span> evaluation). (<b>d</b>) Depth profile of <span class="html-italic">CoV</span> values. (<b>e</b>) Depth profile of mean values. ILM: inner limiting membrane, ELM: external limiting membrane, IS/OS: junction between inner and outer segments of photoreceptors, COST: cone outer segment tips, ROST: rod outer segment tips, RPE: retinal pigment epithelium.</p>
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<p>AO-OCT image quality in healthy subjects. (<b>a</b>) Maximum <span class="html-italic">CoV</span> value along the imaging depth with dependence on the imaging location (black line: average over 3 healthy subjects, grey lines: standard deviation, red line: average over all imaging locations). (<b>b</b>) Maximum <span class="html-italic">CoV</span> value normalized to the average over all healthy volunteers and locations, in percent.</p>
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<p>Pyramid wavefront sensor data retrieved in a healthy volunteer after AO loop convergence. (<b>a</b>) Full pyramid wavefront sensor image indicating the region of interest (white square) that was used for further evaluation. Regions that were used to calculate the signal-to-noise ratio of the pyramid wavefront sensor images are indicated by red squares. (<b>b</b>) Region of interest, indicating the central pupil position and pupil size of the system in white and the measured central pupil position and size in red. d<sub>c</sub> denotes the deviation of the pupil from the central position. D<sub>x</sub> and D<sub>y</sub> denote the diameter of the pupil in the x and y directions, respectively. (<b>c</b>) Pixels above a certain intensity threshold (after pixel binning) that are used to calculate the wavefront slopes. White: detected pixels of in vivo measurement, grey: detected pixels in the model eye corresponding to the system pupil size. (<b>d</b>) Retrieved wavefront slopes in the x and y directions, respectively.</p>
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<p>Dependence of AO-OCT image quality on the imaging day for all patients, estimated by normalized <span class="html-italic">Max.CoV</span> values.</p>
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<p>Dependence of AO-OCT image quality on the imaging location estimated by normalized <span class="html-italic">Max.CoV</span> values. (<b>a</b>) <span class="html-italic">Max.CoV</span> values for each patient averaged over all visits. (<b>b</b>) Mean (black line) and standard deviation (grey lines) of <span class="html-italic">Max.CoV</span> values for all patients for volume 1 and (<b>c</b>) volume 2.</p>
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<p>Representative pupil images recorded at location 2 for all patients and all visits.</p>
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<p>Dependence of pupil diameter (<b>a</b>) and pupil displacement (<b>b</b>) on the imaging day for all patients.</p>
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<p>Dependence of SNR of the pupil images (<b>a</b>) and AO-image quality (<b>b</b>) on the imaging day for all patients.</p>
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<p>(<b>a</b>) Correlation plot between axial position of the outer segments of cones (OS) with respect to the zero delay of the system and the normalized <span class="html-italic">CoV</span> values. A Pearson coefficient of −0.41 (<span class="html-italic">p</span> &lt; 0.01) was found, and the black line indicates a linear fit of the data. (<b>b</b>) Distribution of axial positions of the OS for all volume recordings.</p>
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<p>Correlation plots of patient data between <span class="html-italic">Max.CoV</span> values and (<b>a</b>) AO-correction quality, (<b>b</b>) SNR of the pyramid wavefront sensor, (<b>c</b>) pupil diameter and (<b>d</b>) pupil displacement from the central position. The Pearson coefficient was measured to be 0.11 (<span class="html-italic">p</span> = 0.12), 0.24 (<span class="html-italic">p</span> &lt; 0.01), −0.06 (<span class="html-italic">p</span> = 0.43) and 0.2 (<span class="html-italic">p</span> &lt; 0.01), respectively.</p>
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27 pages, 15613 KiB  
Article
Mineralogical and Geochemical Characterization of Argillaceous Rocks in the Upper Wuerhe Formation in the Mahu 1 Well Block of the Junggar Basin, NW China
by Hao Fu, Yongjun Li, Jianhua Qin, Fenghao Duan, Xueyi Xu, Nanhe Peng, Gaoxue Yang, Kai Liu, Xin Wang and Jing Zhang
Minerals 2025, 15(2), 157; https://doi.org/10.3390/min15020157 - 7 Feb 2025
Viewed by 514
Abstract
The Mahu Sag, where the Mahu 1 well block is located, is one of the most important hydrocarbon-rich depressions in the Junggar Basin, NW China. The Permian Upper Wuerhe Formation (UWF) constitutes the primary layer of the unconventional tight oil reservoir in the [...] Read more.
The Mahu Sag, where the Mahu 1 well block is located, is one of the most important hydrocarbon-rich depressions in the Junggar Basin, NW China. The Permian Upper Wuerhe Formation (UWF) constitutes the primary layer of the unconventional tight oil reservoir in the Mahu Oilfield. To explore the provenance and sedimentary environment during the deposition of the UWF in the study area, we determined the clay mineralogy and whole-rock geochemical composition of argillaceous rocks. The results show that the primary minerals in argillaceous rock are feldspar, clay minerals, quartz, and a minor amount of hematite. The clay minerals identified included illite, smectite, kaolinite, chlorite, and illite/smectite mixed layers. The tectonic setting of the provenance area for the UWF is a continental island arc, associated with a cutting magmatic arc. The main provenance area is related to the Baogutu tectonic belt (the Zhayier Mountain and the Hala’alate Mountain). The bedrock primarily consists of acidic igneous rocks, with minor occurrences of intermediate–basic igneous and sedimentary rock. The chemical index of alteration (CIA) shows that the parent rocks of the argillaceous rocks have experienced moderate–strong chemical weathering. Combining the Sr/Cu and ΣLREE/ΣHREE ratios, δEu values, and clay mineral characteristics, we determined that the paleoclimate during the deposition of the UWF was generally warm and humid, with occasional short-term dry and cold periods. The UWF gradually changes, according to the relative humidity and enhanced chemical weathering from the bottom to the top. An analysis of trace elements, paleosalinity, and paleowater depth indicate that the studied argillaceous rocks were deposited in a shallow-water oxidation environment of continental fresh water with weak hydrodynamic conditions. Full article
(This article belongs to the Section Clays and Engineered Mineral Materials)
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<p>(<b>a</b>) Structural location of the Junggar Basin [<a href="#B30-minerals-15-00157" class="html-bibr">30</a>,<a href="#B44-minerals-15-00157" class="html-bibr">44</a>]; (<b>b</b>) division of tectonic units in the Junggar Basin (modified from Xinjiang Oilfield Company); (<b>c</b>) geographical location and tectonic division map of the Mahu 1 well block showing the studied well’s location information [<a href="#B41-minerals-15-00157" class="html-bibr">41</a>]; (<b>d</b>) stratigraphic column of the study area [<a href="#B41-minerals-15-00157" class="html-bibr">41</a>,<a href="#B42-minerals-15-00157" class="html-bibr">42</a>].</p>
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<p>Stratigraphic histogram of UWF and sampling location of argillaceous rock samples.</p>
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<p>Characteristics of argillaceous rocks in the UWF. (<b>a</b>) MH027, 3358.2 m, P<sub>3</sub><span class="html-italic">w</span><sup>1</sup>, dark gray mudstone; (<b>b</b>) K206, 3608.2 m, P<sub>3</sub><span class="html-italic">w</span><sup>2</sup>, gray sandy mudstone; (<b>c</b>) MH11, 3369.7 m, P<sub>3</sub><span class="html-italic">w</span><sup>1</sup>, grayish black silty mudstone; (<b>d</b>) MH11, 3468.2 m, P<sub>3</sub><span class="html-italic">w</span><sup>1</sup>, gray silty mudstone; (<b>e</b>,<b>f</b>) yellow–brown clay minerals of dark gray mudstone in the MH027; (<b>g</b>,<b>h</b>) quartz and plagioclase of gray sandy mudstone in the K206, muddy structure; (<b>i</b>,<b>j</b>) quartz and feldspar of black silty mudstone, silty argillaceous structure; (<b>k</b>,<b>l</b>) quartz of gray silty mudstone in MH11, silty argillaceous structure. Mineral code: clay, clay minerals; Qz, quartz; Pl, plagioclase; Fsp, feldspar.</p>
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<p>XRD patterns of argillaceous rocks in the UWF.</p>
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<p>XRD patterns of clay minerals in argillaceous rocks in the UWF. (<b>a</b>) K206-15-23x, 3698.1 m, P<sub>3</sub><span class="html-italic">w</span><sup>1</sup>, silty mudstone; (<b>b</b>) MH027-4-4x, 3352.2 m, P<sub>3</sub><span class="html-italic">w</span><sup>1</sup>, silty mudstone; (<b>c</b>) K044-2-1x, 3219.4 m, P<sub>3</sub><span class="html-italic">w</span><sup>2</sup>, silty mudstone; (<b>d</b>) K206-9-1x, 3607.2 m, P<sub>3</sub><span class="html-italic">w</span><sup>2</sup>, silty mudstone; (<b>e</b>) MH11-1-14x, 3236 m, P<sub>3</sub><span class="html-italic">w</span><sup>3</sup>, silty mudstone; (<b>f</b>) MH013-3-21x, 3540.2 m, P<sub>3</sub><span class="html-italic">w</span><sup>3</sup>, sandy silty mudstone. Mineral code: Q, quartz; Ab, albite; Cc, calcite; S, smectite; K, kaolinite; C, chlorite; I, illite; I/S, illite/smectite mixed layers.</p>
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<p>SEM images of clay minerals in argillaceous rocks. (<b>a</b>) Flaky clay mineral with broken appearance and bedding structure, MH032, 3479.2 m; (<b>b</b>) irregular plate-shaped kaolinite (red square), MH31, 3187.4 m; (<b>c</b>) leaf-like chlorite (red square), MH032, 3479.2 m; (<b>d</b>) honeycomb I/S minerals and pore filamentous illite (red square), MH11, 3236.2 m.</p>
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<p>(<b>a</b>) Trace element spider diagram and (<b>b</b>) REE distribution pattern for the argillaceous rocks (normalization values are from [<a href="#B58-minerals-15-00157" class="html-bibr">58</a>]).</p>
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<p>Correlation diagrams of argillaceous rock samples from UWF. (<b>a</b>) δCeߝδEu correlation diagram; (<b>b</b>) ΣREEߝδCe correlation diagram; (<b>c</b>) (Dy/Sm)<sub>N</sub>ߝδCe correlation diagram.</p>
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<p>Th/ScߝZr/Sc diagram of argillaceous rocks in the UWF (base map modified from [<a href="#B91-minerals-15-00157" class="html-bibr">91</a>,<a href="#B92-minerals-15-00157" class="html-bibr">92</a>]).</p>
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<p>(<b>a</b>) SrߝBa (base map modified from [<a href="#B111-minerals-15-00157" class="html-bibr">111</a>]) and (<b>b</b>) VߝBa (base map modified from [<a href="#B5-minerals-15-00157" class="html-bibr">5</a>]) diagrams of argillaceous rocks in the UWF.</p>
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<p>Ni/CoߝU/Th diagram of argillaceous rocks in the UWF (base map modified from [<a href="#B118-minerals-15-00157" class="html-bibr">118</a>]).</p>
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<p>(<b>a</b>) SiO<sub>2</sub>ߝK<sub>2</sub>O/Na<sub>2</sub>O (base map modified from [<a href="#B5-minerals-15-00157" class="html-bibr">5</a>]) and (<b>b</b>) La/ScߝTi/Zr (base map modified from [<a href="#B119-minerals-15-00157" class="html-bibr">119</a>]) diagrams of the samples.</p>
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<p>(<b>a</b>) La/ThߝHf (base map modified from [<a href="#B122-minerals-15-00157" class="html-bibr">122</a>]) and (<b>b</b>) La/Yb−ΣREE (base map modified from [<a href="#B123-minerals-15-00157" class="html-bibr">123</a>]) diagrams of the samples.</p>
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23 pages, 7096 KiB  
Article
Establishing Benchmark Properties for 3D-Printed Concrete: A Study of Printability, Strength, and Durability
by Alise Sapata, Māris Šinka, Genādijs Šahmenko, Lidija Korat Bensa, Lucija Hanžič, Katarina Šter, Sandris Ručevskis, Diāna Bajāre and Freek P. Bos
J. Compos. Sci. 2025, 9(2), 74; https://doi.org/10.3390/jcs9020074 - 7 Feb 2025
Viewed by 822
Abstract
This study investigates the fresh state and hardened state mechanical and durability properties of 3D-printed concrete. The mechanical tests focused on its anisotropic behavior in response to different load orientations. Compressive, flexural, and splitting tensile strengths were evaluated relative to the print layers [...] Read more.
This study investigates the fresh state and hardened state mechanical and durability properties of 3D-printed concrete. The mechanical tests focused on its anisotropic behavior in response to different load orientations. Compressive, flexural, and splitting tensile strengths were evaluated relative to the print layers orientation. Results showed that compressive strength varied significantly, achieving 85% of cast sample strength when the load was applied parallel to the print layers ([u] direction), 71% when the load was applied perpendicular to the print object’s side plane ([v] direction), while only reaching 59% when applied perpendicular to the top plane ([w] direction). Similar trends were observed for flexural strength, with average values reaching 75% of cast sample strength when the load was applied perpendicular to the print layers ([v.u] and [w.u] directions), but decreasing to 53% when the load was applied parallel to print layers ([u.w] direction), underscoring the weaknesses at interlayer interfaces. The splitting tensile strength remained relatively consistent across print orientations, reaching 90% of the cast sample strength. Durability assessment tests revealed that 3D-printed concrete exhibits reduced resistance to environmental factors, particularly at the layer interfaces where the cold joint was formed, which are prone to moisture penetration and crack formation. These findings contribute valuable insights into the mechanical and durability properties of 3D-printed concrete, emphasizing the importance of print orientation and interlayer bonding in its performance. This understanding helps guide the optimal use of 3D-printed elements in real-life applications by aligning load or exposure to environmental factors with the material’s strength and durability characteristics. Full article
(This article belongs to the Special Issue Sustainable Composite Construction Materials, Volume II)
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<p>Custom-made laboratory concrete printer at RTU: (<b>a</b>) printer setup with the aluminum frame and print area; (<b>b</b>) gantry system closeup: (1) motor; (2) hopper; (3) inlet; (4) pipe; (5) nozzle.</p>
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<p>(<b>a</b>) Rheometer working protocol. The protocol consists of two parts with varying resting intervals: an initial resting phase of 6 min, followed by 1 min working time. After 25 min, the resting time extends to 11 min, followed by 1 min working time. (<b>b</b>) Rheological chart: time vs. torque.</p>
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<p>Specimen preparation for mechanical tests: (<b>a</b>) print object geometry for mechanical tests; (<b>b</b>) markings on specimens before cutting; (<b>c</b>) circular saw table: (1) circular saw blade; (2) metal guide fixed perpendicular to the saw blade; (3) metal guide fixed parallel to the saw blade.</p>
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<p>Test setups for compressive strength tests in various directions: (<b>a</b>) direction [u]; (<b>b</b>) direction [v]; (<b>c</b>) direction [w].</p>
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<p>Test setups for splitting strength tests in various directions: (<b>a</b>) direction [w/u]; (<b>b</b>) direction [v/w]; (<b>c</b>) direction [u/v].</p>
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<p>Test setups for flexural strength tests in various directions: (<b>a</b>) direction [u.w]; (<b>b</b>) direction [v.u]; (<b>c</b>) direction [w.u].</p>
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<p>Print object geometry for durability tests. Two printed objects: the bottom part was printed first for the object with the cold joint (T<sub>SET</sub>); the printed object without the cold joint (T<sub>0</sub>).</p>
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<p>Static and dynamic yield stress values obtained via rheometer and slugs tests.</p>
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<p>Direct buildability test: (<b>a</b>) mixture right before plastic collapse; (<b>b</b>) plastic collapse.</p>
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<p>Compressive strength test results.</p>
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<p>Comparison between compressive strength results of samples taken from different locations.</p>
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<p>Flexural strength test results.</p>
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<p>The fracture pattern of flexural strength test specimens: (<b>a</b>) direction [u.w]; (<b>b</b>) direction [v.u]; (<b>c</b>) direction [w.u].</p>
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<p>Splitting strength test results.</p>
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<p>Water absorption test results.</p>
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<p>Carbonation depth of the developed mixture after 7, 28, 56, and 90 days for (<b>a</b>) cast samples; (<b>b</b>) printed samples without the cold joint (T<sub>0</sub>); (<b>c</b>) printed samples with the cold joint (T<sub>SET</sub>).</p>
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<p>3D-printed carbonation samples after testing: (<b>a</b>) T<sub>0</sub> after 7 days; (<b>b</b>) T<sub>0</sub> after 28 days; (<b>c</b>) T<sub>0</sub> after 56 days; (<b>d</b>) T<sub>0</sub> after 90 days; (<b>e</b>) T<sub>SET</sub> after 7 days; (<b>f</b>) T<sub>SET</sub> after 28 days; (<b>g</b>) T<sub>SET</sub> after 56 days; (<b>h</b>) T<sub>SET</sub> after 90 days.</p>
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