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Search Results (170)

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23 pages, 5140 KiB  
Review
Remote Sensing and Geophysical Applications in the Dead Sea Region: Insights, Trends, and Advances
by Damien Closson and Al-Halbouni Djamil
Geosciences 2025, 15(2), 50; https://doi.org/10.3390/geosciences15020050 - 2 Feb 2025
Abstract
The Dead Sea ecosystem, with its hypersaline conditions, base-level fluctuations, and active tectonics, presents a unique challenge for geological studies. Its equilibrium is increasingly unbalanced due to overexploitation of water and mineral resources. Remote sensing, including drone-based photogrammetry and satellite imaging, monitors large-scale [...] Read more.
The Dead Sea ecosystem, with its hypersaline conditions, base-level fluctuations, and active tectonics, presents a unique challenge for geological studies. Its equilibrium is increasingly unbalanced due to overexploitation of water and mineral resources. Remote sensing, including drone-based photogrammetry and satellite imaging, monitors large-scale surface changes, while geophysical methods like electromagnetic and seismic surveys reveal subsurface structures. The integration of these methods has transformed our understanding. Combined studies now monitor hazards such as sinkholes, subsidence, and landslides with greater precision. Advances in artificial intelligence further enhance analysis by processing vast datasets to uncover previously undetectable trends. This synergy between remote sensing, geophysics, and AI offers efficient solutions for studying the disrupted ecosystem. Critical challenges include environmental degradation, rapid water loss, and sinkhole formation, threatening infrastructure, industries, and habitats. Remote sensing has been pivotal in monitoring and mitigating these hazards. Together with geophysics, it provides a robust framework for addressing these extreme conditions. By combining these methods, researchers gain valuable insights into the unique dynamics of the Dead Sea ecosystem, advancing scientific knowledge and supporting sustainable management strategies. Full article
(This article belongs to the Section Hydrogeology)
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Figure 1

Figure 1
<p>(<b>a</b>) Evolution of the Dead Sea Landscape (from 15 September 1972 to 10 January 2025). Over 53 years, the Dead Sea’s base level dropped ~45 m (15-story building), exposing coastal zones, particularly around the Lisan Peninsula. The orange areas, including Ghor Al Haditha (GAH) and Ein Gedi, are impacted by thousands of sinkholes, widespread subsidence, and landslides. The basin, controlled by N24E left-lateral strike-slip faults, experiences rapid geomorphic changes. The southern lake now hosts salt evaporation ponds operated by the Dead Sea Works (DSW) and the Arab Potash Company (APC). Projection UTM 36/WGS84. Source: USGS, Landsat Imagery. (<b>b</b>) Dead Sea Water Level Decline (1976–2024), with Average Decadal Decrease. The steady acceleration of the water level decline is evident, with an expected average decrease for 2020–2030 projected at 12.45 m/decade. The temporary slowing observed in the 1990s resulted from increased flooding triggered by the climatic impacts of the 1991 Mount Pinatubo eruption. It injected massive amounts of sulfur dioxide into the stratosphere, forming aerosols that temporarily cooled the Earth’s surface. This cooling disrupted global weather patterns, including an increase in precipitation in the Near East over several years. These anomalous rainfall events contributed to significant flooding, temporarily mitigating the rate of decline in the Dead Sea’s water level. Source: <a href="http://data.gov.il" target="_blank">data.gov.il</a>.</p>
Full article ">Figure 1 Cont.
<p>(<b>a</b>) Evolution of the Dead Sea Landscape (from 15 September 1972 to 10 January 2025). Over 53 years, the Dead Sea’s base level dropped ~45 m (15-story building), exposing coastal zones, particularly around the Lisan Peninsula. The orange areas, including Ghor Al Haditha (GAH) and Ein Gedi, are impacted by thousands of sinkholes, widespread subsidence, and landslides. The basin, controlled by N24E left-lateral strike-slip faults, experiences rapid geomorphic changes. The southern lake now hosts salt evaporation ponds operated by the Dead Sea Works (DSW) and the Arab Potash Company (APC). Projection UTM 36/WGS84. Source: USGS, Landsat Imagery. (<b>b</b>) Dead Sea Water Level Decline (1976–2024), with Average Decadal Decrease. The steady acceleration of the water level decline is evident, with an expected average decrease for 2020–2030 projected at 12.45 m/decade. The temporary slowing observed in the 1990s resulted from increased flooding triggered by the climatic impacts of the 1991 Mount Pinatubo eruption. It injected massive amounts of sulfur dioxide into the stratosphere, forming aerosols that temporarily cooled the Earth’s surface. This cooling disrupted global weather patterns, including an increase in precipitation in the Near East over several years. These anomalous rainfall events contributed to significant flooding, temporarily mitigating the rate of decline in the Dead Sea’s water level. Source: <a href="http://data.gov.il" target="_blank">data.gov.il</a>.</p>
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<p>(<b>a</b>) Workflow illustrating the integration of field data, remote sensing data, and data processing to produce actionable insights into subsidence and sinkhole evolution in the Dead Sea region. Key components include data collection from time-lapse cameras, hydrometer readings, drone imagery, and borehole studies, complemented by LiDAR and InSAR analysis. (<b>b</b>) Data processing and analysis workflow integrating LiDAR, InSAR, and drone/TLC data. LiDAR facilitated elevation change calculations and sinkhole delineation, while InSAR generated interferograms to trace subsidence lineaments associated with subsurface dissolution channels. Drone and TLC data enabled real-time visualization of flood dynamics and clarified temporal relationships between recharge and discharge events. (<b>c</b>) Results derived from the comprehensive workflow showing quantitative measurements such as floodwater flow velocities, sinkhole expansion rates, and floodwater recharge and discharge timings. The integration of field and remote sensing data reveals subsurface dissolution channels and landscape evolution patterns.</p>
Full article ">Figure 2 Cont.
<p>(<b>a</b>) Workflow illustrating the integration of field data, remote sensing data, and data processing to produce actionable insights into subsidence and sinkhole evolution in the Dead Sea region. Key components include data collection from time-lapse cameras, hydrometer readings, drone imagery, and borehole studies, complemented by LiDAR and InSAR analysis. (<b>b</b>) Data processing and analysis workflow integrating LiDAR, InSAR, and drone/TLC data. LiDAR facilitated elevation change calculations and sinkhole delineation, while InSAR generated interferograms to trace subsidence lineaments associated with subsurface dissolution channels. Drone and TLC data enabled real-time visualization of flood dynamics and clarified temporal relationships between recharge and discharge events. (<b>c</b>) Results derived from the comprehensive workflow showing quantitative measurements such as floodwater flow velocities, sinkhole expansion rates, and floodwater recharge and discharge timings. The integration of field and remote sensing data reveals subsurface dissolution channels and landscape evolution patterns.</p>
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37 pages, 4112 KiB  
Article
Bringing Science to the Periphery Through Distance Learning: Barriers and Opportunities
by Eilat Chen Levy, Gilad Ravid, Yael Shwartz and Noa Avriel-Avni
Educ. Sci. 2025, 15(2), 114; https://doi.org/10.3390/educsci15020114 - 21 Jan 2025
Viewed by 447
Abstract
Students in peripheral areas often score lower in the sciences than those in urban areas. This study explores the feasibility of distance learning in making quality science education accessible everywhere. In 2016, the Israeli government established technological infrastructure in southern peripheral schools, but [...] Read more.
Students in peripheral areas often score lower in the sciences than those in urban areas. This study explores the feasibility of distance learning in making quality science education accessible everywhere. In 2016, the Israeli government established technological infrastructure in southern peripheral schools, but these resources remained largely unused. From 2018 to 2019, we interviewed school principals and education department directors, and surveyed teachers and students about distance learning. The findings showed hesitance among educators to implement distance learning for expanding science subjects, despite their confidence in using it when necessary. After the mandatory shift to distance learning during the COVID-19 pandemic in 2020, attitudes towards technology in remote schools improved. Barriers to implementing distance learning were found to be mainly due to internal factors like preconceived notions, which limited the development of necessary skills among teachers and students. We recommend addressing internal resistance to distance learning in teacher training programs. Full article
(This article belongs to the Section Technology Enhanced Education)
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<p>TPACK Components. Source: <a href="#B41-education-15-00114" class="html-bibr">Koehler and Mishra</a> (<a href="#B41-education-15-00114" class="html-bibr">2009</a>).</p>
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<p>TAM components (source: (<a href="#B14-education-15-00114" class="html-bibr">Davis, 1989</a>)).</p>
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<p>The research area is within the green triangle. The black dots indicate settlements in Israel.</p>
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<p>Perceived usefulness of distance learning.</p>
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<p>Perceived skills and capabilities in using distance learning.</p>
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<p>Perceived ease of use of distance learning.</p>
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<p>Attitudes toward Bagrut and elective courses.</p>
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<p>Barriers to ICT Adoption.</p>
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<p>Perceived usefulness.</p>
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<p>Behavioral intention to use ICT.</p>
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<p>ICT literacy and usage.</p>
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25 pages, 29385 KiB  
Article
Porifera Associated with Deep-Water Stylasterids (Cnidaria, Hydrozoa): New Species and Records from the Ross Sea (Antarctica)
by Barbara Calcinai, Teo Marrocco, Camilla Roveta, Stefania Puce, Paolo Montagna, Claudio Mazzoli, Simonepietro Canese, Carlo Vultaggio and Marco Bertolino
J. Mar. Sci. Eng. 2024, 12(12), 2317; https://doi.org/10.3390/jmse12122317 - 17 Dec 2024
Viewed by 568
Abstract
Stylasterid corals are known to be fundamental habitat-formers in both deep and shallow waters. Their tridimensional structure enhances habitat complexity by creating refuges for a variety of organisms and by acting as basibionts for many other invertebrates, including sponges. Porifera represent crucial components [...] Read more.
Stylasterid corals are known to be fundamental habitat-formers in both deep and shallow waters. Their tridimensional structure enhances habitat complexity by creating refuges for a variety of organisms and by acting as basibionts for many other invertebrates, including sponges. Porifera represent crucial components of marine benthic assemblages and, in Antarctica, they often dominate benthic communities. Here, we explore the sponge community associated with thanatocoenosis, mostly composed of dead stylasterid skeletons, collected along the Western and Northern edges of the Ross Sea continental shelf. Overall, 37 sponge species were identified from 278 fragments of the stylasterid Inferiolabiata labiata, of which 7 are first records for the Ross Sea, 1 is first record for Antarctic waters and 2 are proposed as new species. Despite the high biodiversity recorded in this and previous studies on Antarctic deep-sea communities, we are still far from capturing the true richness of Antarctic benthic assemblages. Long-term research programs designed to improve the knowledge of the deep-sea fauna inhabiting Antarctic waters are needed to support successful management and conservation plans, especially in this area, considered one of the main marine diversity hotspots worldwide. Full article
(This article belongs to the Section Marine Biology)
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<p>Map of the location of the sampling stations in the Ross Sea continental shelf.</p>
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<p>(<b>A</b>) Bar plots representing the total number of specimens for sponge species with more than 2 samples. (<b>B</b>) Donut charts showing the percentage of sponge species and specimens with an encrusting (Ec) or massive erect (ME) habit, or both (ME/Ec).</p>
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<p>Some sponge specimens with larger sizes: (<b>A</b>) <span class="html-italic">Acanthascus</span> (<span class="html-italic">Rhabdocalyptus</span>) <span class="html-italic">australis</span> (MNA 16005, GCR-02-223 D), (<b>B</b>) <span class="html-italic">Iophon unicorne</span> (MNA 16007, GRC-08-023 DC), (<b>C</b>) <span class="html-italic">Haliclona</span> (<span class="html-italic">Gellius</span>) <span class="html-italic">rudis</span> (MNA 16006, GRC-08-023 DE).</p>
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<p>Bar plots showing (<b>A</b>) the total number of specimens in relation to the area covered on the stylasterid <span class="html-italic">Inferiolabiata labiata</span> and (<b>B</b>) the sponge species with the highest percentage cover on <span class="html-italic">I. labiata</span> (expressed as average ± standard deviation).</p>
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<p>(<b>A</b>) <span class="html-italic">Lissodendoryx</span> (<span class="html-italic">Lissodendoryx</span>) <span class="html-italic">stylosa</span> sp. nov. (holotype MNA 15959, GRC-02-223 O1); (<b>B</b>) <span class="html-italic">L.</span> (<span class="html-italic">L.</span>) <span class="html-italic">styloderma</span> (MNA 13340, GRC-02-223 (1) sp. 3); (<b>C</b>) <span class="html-italic">Crella</span> (<span class="html-italic">Crella</span>) <span class="html-italic">tubifex</span> (MNA 15968, GRC-02-223 BO1); (<b>D</b>) <span class="html-italic">Esperiopsis flagellata</span> sp. nov. (holotype MNA 15962, GRC-02-223 (8) sp. 6); (<b>E</b>) <span class="html-italic">Artemisina plumosa</span> (MNA 15989, GRC-02-223 AN2); (<b>F</b>) <span class="html-italic">Mycale</span> (<span class="html-italic">Anomomycale</span>) cf. <span class="html-italic">titubans</span> (MNA 15994, GRC-02-223 AH1); (<b>G</b>) <span class="html-italic">Tetilla coronida</span> (MNA 15999, GRC-TR17-007 CP1); (<b>H</b>) <span class="html-italic">Poecillastra antarctica</span> comb. nov. (MNA 13302, GRC-02-223 (26) sp. 1). White arrows indicate the position of the sponge specimens on <span class="html-italic">Inferiolabiata labiata</span> fragments.</p>
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<p>SEM pictures of <span class="html-italic">Lissodendoryx</span> (<span class="html-italic">Lissodendoryx</span>) <span class="html-italic">stylosa</span> sp. nov.: (<b>A</b>) style I; (<b>B</b>) style II; (<b>C</b>) arcuate isochelae; (<b>D</b>) sigmas.</p>
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<p>SEM pictures of <span class="html-italic">Lissodendoryx</span> (<span class="html-italic">Lissodendoryx</span>) <span class="html-italic">styloderma</span>: (<b>A</b>) subtylostyle; (<b>B</b>) magnification of the head and the pointed tip of a subtylostyle; (<b>C</b>) tornotes; (<b>D</b>) magnification of a pointed end of a tornote; (<b>E</b>) arcuate isochelae.</p>
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<p>SEM pictures of <span class="html-italic">Crella</span> (<span class="html-italic">Crella</span>) <span class="html-italic">tubifex</span>: (<b>A</b>) acanthostyle; (<b>B</b>) anisostrongyle; (<b>C</b>) acanthostrongyle; (<b>D</b>) magnification of the central portion and extremities of an acanthostrongyle.</p>
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<p>Optical microscope pictures of <span class="html-italic">Esperiopsis flagellata</span> sp. nov.: (<b>A</b>) style; (<b>B</b>) end of a style; (<b>C</b>) isochelae I; (<b>D</b>) isochelae II; (<b>E</b>) C-shaped sigma; (<b>F</b>) flagellated sigma.</p>
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<p>SEM pictures of <span class="html-italic">Artemisina plumosa</span>: (<b>A</b>) style I; (<b>B</b>) style II; (<b>C</b>) tylote; (<b>D</b>) magnification of the spined head of a tylote; (<b>E</b>) isochelae; (<b>F</b>) toxas.</p>
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<p>SEM pictures of <span class="html-italic">Mycale</span> (<span class="html-italic">Anomomycale</span>) cf. <span class="html-italic">titubans</span>: (<b>A</b>) large mycalostyle; (<b>B</b>) thin mycalostyle; (<b>C</b>) anomochelae; (<b>D</b>) sigmas.</p>
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<p><span class="html-italic">Hymeniacidon fragilis</span> (Koltun, 1964) comb. nov.: (<b>A</b>) massively encrusting specimen MNA 13377 (GRC-02-223 (22) sp. 1); (<b>B</b>) laminar specimen MNA 16004 (GRC-02-223 (37) D); (<b>C</b>,<b>D</b>) choanosomal skeleton; (<b>E</b>) ectosomal skeleton; (<b>F</b>) spicules. White arrows indicate the position of the sponge attachment to the stylasterid.</p>
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<p>SEM pictures of <span class="html-italic">Tetilla coronida</span>: (<b>A</b>) oxeas; (<b>B</b>) extremity of a protriaene; (<b>C</b>) extremities of anatrienes; (<b>D</b>) extremities of anamonaenes; (<b>E</b>) sigmaspires.</p>
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<p>Optical microscope pictures of <span class="html-italic">Poecillastra antarctica</span>: (<b>A</b>) oxeas; (<b>B</b>) calthrops and orthotrianes; (<b>C</b>) plesiasters; (<b>D</b>) amphiasters; (<b>E</b>) spirasters.</p>
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13 pages, 2570 KiB  
Article
Phylogeny and Taxonomy of the Naematelia aurantialba Complex in Southwestern China
by Jin-Yan Tang and Zhu-Liang Yang
J. Fungi 2024, 10(12), 845; https://doi.org/10.3390/jof10120845 - 6 Dec 2024
Viewed by 993
Abstract
Naematelia aurantialba and its allies are important edible and medicinal mushrooms in China. They are usually called Jiner (金耳) and have been cultivated on a commercial scale. However, due to the lack of DNA sequences from the holotype of Naematelia aurantialba, the [...] Read more.
Naematelia aurantialba and its allies are important edible and medicinal mushrooms in China. They are usually called Jiner (金耳) and have been cultivated on a commercial scale. However, due to the lack of DNA sequences from the holotype of Naematelia aurantialba, the taxonomic issues of the species complex are unresolved. In this study, the authors successfully generated DNA sequences from the holotype of N. aurantialba by a genome skimming approach and additional allied species by Sanger sequencing. Based on morphological characteristics, molecular phylogenetic data, and geographic distribution patterns, four species, including three new ones, in the complex in southwestern China were uncovered. Naematelia aurantialba occurs at high altitudes (over 3000 m above sea level), with subalpine dead plants as its substrates, and has larger basidiospores, while the commonly cultivated species, described as N. sinensis in this work, is distributed in subtropical areas at altitudes between 1800 m and 2600 m on the dead wood of subtropical plants and has smaller basidiospores. The third species, namely N. nodulosa, has habitats similar to those of N. sinensis but differs from the latter in its basidiomata with an uneven nodulose surface, a loose context with small internal cavities, and numerous conidia. The fourth species, N. pedicellata, is easily distinguished from the others by its basidia, with long basal stalks and broadly ellipsoid basidiospores measuring 10.5–12.5 × 8.0–10.0 μm. All these species are parasitic on Stereum species. This study provides a solid basis for future guidance for the selection of new strains and cultivation practices of these valuable fungi. Full article
(This article belongs to the Section Fungal Evolution, Biodiversity and Systematics)
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Figure 1
<p>Phylogenetic tree of <span class="html-italic">Naematelia</span> with related species based on maximum likelihood analysis from a two-loci (ITS, nrLSU) dataset. ML ≥ 50% and BI ≥ 70% are indicated above the branches.</p>
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<p><span class="html-italic">Naematelia aurantialba</span>: (<b>a</b>,<b>b</b>) fresh basidiomata; note the <span class="html-italic">Stereum</span> basidiomata (arrow); (<b>a</b>) HKAS 89568; (<b>b</b>) HKAS 90938). Bars = 20 mm.</p>
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<p><span class="html-italic">Naematelia aurantialba</span>: (1) basidiospores germinating by budding or by repetition (HKAS89568); (2) basidia at different stages of development; a haustorium attached to a host hypha (arrow) (HKAS89568); (3) basidiospores germinating by budding or by repetition (HKAS19954, holotype). Bars = 10 μm.</p>
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<p><span class="html-italic">Naematelia nodulosa</span> (HKAS141719, holotype): (<b>a</b>) fresh basidioma; (<b>b</b>) vertical sections of basidioma; (<b>c</b>) fresh basidioma and host, namely <span class="html-italic">Stereum hirsutum</span> or its allies (arrow). Bars = 20 mm.</p>
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<p><span class="html-italic">Naematelia nodulosa</span>: (1) parasite hyphae with haustoria, conidiophores and conidia, and unclamped host hypha (arrow) in trama (HKAS 141591); (2) parasite hyphae with haustoria, conidiophore and conidia, and unclamped host hypha (arrow) in trama (HKAS141719, holotype); (3) basidia at different stages of development (HKAS141719, holotype). Bars = 10 m.</p>
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<p><span class="html-italic">Naematelia pedicellata</span> (HKAS 112730, holotype): (<b>a</b>) fresh basidioma; note the <span class="html-italic">Stereum</span> basidiomata on the upper right of the trunk (arrow); (<b>b</b>) vertical sections of basidioma. Bars = 20 mm.</p>
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<p><span class="html-italic">Naematelia pedicellata</span> (HKAS 112730, holotype): 1. hymenium with stalked basidia at different stages of development and hyphidia; 2. cell immediately below a basidium with conidia; 3. basidiospores, germinating by budding or by repetition; 4. parasite hyphae with haustoria, conidiophore and conidia, and unclamped hyphae of host (arrows) in trama. Bars = 10 μm.</p>
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<p><span class="html-italic">Naematelia sinensis</span> (HKAS144460): (<b>a</b>) fresh basidioma; (<b>b</b>) vertical sections of basidioma. Bars = 20 mm.</p>
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<p><span class="html-italic">Naematelia sinensis</span> (HKAS144460): (1) tramal hyphae with conidiophores and conidia; (2) hymenium with basidia at different stages of development, with haustoria attached to unclamped hypha of host (arrow); (3) basidiospores, germinating by repetition. Bars = 10 μm.</p>
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19 pages, 2037 KiB  
Article
Disruption of Biofilm Formation by Dead Sea Soil Extracts: A Novel Approach Against Diabetic Foot Wound Isolates
by Mohammad Zubair, Farha Fatima, Sumbul Rahman, Tariq Alrasheed, Roba Alatawy and M. Ahmed Mesaik
Microbiol. Res. 2024, 15(4), 2535-2553; https://doi.org/10.3390/microbiolres15040169 - 2 Dec 2024
Viewed by 739
Abstract
Bacterial biofilms are closely associated with the rising threat of antimicrobial resistance, which is becoming a global concern. Recently, there has been increased interest in natural extracts as potential antimicrobial agents. One such extract is Dead Sea mud. While there is some evidence [...] Read more.
Bacterial biofilms are closely associated with the rising threat of antimicrobial resistance, which is becoming a global concern. Recently, there has been increased interest in natural extracts as potential antimicrobial agents. One such extract is Dead Sea mud. While there is some evidence of its antimicrobial properties, it has not been extensively studied. Therefore, we designed a study to evaluate the potential of Dead Sea soil as an antimicrobial agent. For this purpose, three bacterial species (Pseudomonas aeruginosa, Escherichia coli, and Staphylococcus aureus) were isolated from the ulcerated foot of a patient in a hospital in Tabuk. P. aeruginosa exhibited significant antibiotic resistance, particularly to Levofloxacin (90%) and Tobramycin (80%), while S. aureus showed 70% resistance to Levofloxacin but no vancomycin resistance. Biofilm activity varied among bacterial strains, with P. aeruginosa showing 30% strong biofilm production. MIC values indicated resistance levels, with P. aeruginosa strain PA8 having the highest MIC at 650 µL/mL. All strains showed significant differences in exopolysaccharide (EPS) production at 0.25 × MIC (p ≤ 0.05) and 0.5 × MIC (p ≤ 0.005). Similarly, alginate production was significantly reduced at 0.25 × MIC (p ≤ 0.05), with even greater inhibition at 0.5 × MIC for combinations such as EC7 + SA5 (p ≤ 0.001). Hydrophobicity significantly changed at 0.25 × MIC (p ≤ 0.05), and combinations revealed highly significant reductions at 0.5 × MIC (p ≤ 0.001). Additionally, significant differences in outer membrane disruption were observed (p ≤ 0.05) with greater effects at 0.5 × MIC (p ≤ 0.005). Swarming motility was notably reduced for SA5 at 0.25 × MIC (p ≤ 0.05) and for PA2 at 0.5 × MIC (p ≤ 0.001). Chitinase activity showed greater reductions at 0.5 × MIC, with EC7 exhibiting the highest decrease. Lastly, sub-MIC concentrations enhanced reactive oxygen species (ROS) production, particularly for strains PA2 and SA5. Our results demonstrate the excellent potential of Dead Sea soil extract as an antimicrobial compound. Future studies should incorporate in vivo models to validate these findings clinically. Full article
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Figure 1
<p>Map of the collection site from Ma’an region of Jordan.</p>
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<p>FTIR of soil extract.</p>
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<p>SEM images of soil at different magnifications.</p>
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<p>EDAX of soil extract.</p>
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<p>Effect of sub-MIC concentrations on exopolysaccharides in tested strains.</p>
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<p>Inhibitory effect of sub-MIC concentrations on alginate in tested strains.</p>
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<p>Inhibitory effect of sub-MICs on hydrophobicity in tested strains.</p>
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<p>Influence of sub-MIC concentrations on outer membrane disruption in tested strains.</p>
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<p>Effect on swarming motility of sub-MIC concentrations in tested strains.</p>
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<p>Effect on chitinase activity of sub-MIC concentrations in tested strains.</p>
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<p>Effect on pre-formed biofilm of sub-MIC concentrations in tested strains.</p>
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<p>Effect of sub-MIC concentrations on ROS in tested strains.</p>
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18 pages, 7050 KiB  
Article
A Numerical Method on Large Roll Motion in Beam Seas Under Intact and Damaged Conditions
by Jiang Lu, Yanjie Zhao, Chao Shi, Taijun Yu and Min Gu
J. Mar. Sci. Eng. 2024, 12(11), 2043; https://doi.org/10.3390/jmse12112043 - 11 Nov 2024
Viewed by 794
Abstract
The second-generation intact stability criteria, including five stability failure modes, were approved by the International Maritime Organization (IMO) in 2020, and it is an urgent task to develop the numerical method for the significant roll motion under dead conditions. Both intact and damaged [...] Read more.
The second-generation intact stability criteria, including five stability failure modes, were approved by the International Maritime Organization (IMO) in 2020, and it is an urgent task to develop the numerical method for the significant roll motion under dead conditions. Both intact and damaged stability focus on the large roll motion in beam seas. A unified numerical method is studied to predict the large roll motion in regular and irregular beam seas under intact and damaged conditions. Firstly, a sway–heave–pitch–roll–yaw coupled equation named 5-DOF and a sway-roll-yaw coupled motion with the roll-righting arm in still water named 3-DOF are used to predict the large roll motion in regular beam seas under the intact and damaged conditions. Secondly, the method is extended for the large roll motion in irregular beam seas, where the diffraction force in the roll direction and the sway and yaw motion under intact and damaged conditions are calculated by the subharmonic superposition method. Thirdly, the roll-righting arm in the calm water, roll-damping coefficients, and the roll natural roll period, under the intact and damaged conditions, are obtained by software and a free roll decay experiment, respectively. Finally, the numerical results of a patrol boat under intact and damaged conditions are compared to the experimental results. The results show that the sway-roll-yaw coupled motion with the roll-righting arm in still water named 3-DOF can predict the large roll motion in regular and irregular beam seas under intact and damaged conditions. Full article
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<p>Coordinate systems.</p>
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<p>The patrol boat lines.</p>
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<p>The damaged position and size.</p>
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<p>Comparison of GZ in calm water under intact and damaged conditions.</p>
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<p>The effect of wavelength on the different methods for calculating <span class="html-italic">GZ</span> in waves with <span class="html-italic">φ</span> = 10 degrees, <span class="html-italic">Fn</span> = 0.0, <span class="html-italic">H/Lpp</span> = 0.02, and <span class="html-italic">χ</span> = 90 degrees.</p>
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<p>Comparison of the restoring variation, including <span class="html-italic">FK</span> and <span class="html-italic">DF</span> components, between three heeling angles with <span class="html-italic">Fn</span> = 0.0, <span class="html-italic">λ/Lpp</span> = 1.243644, <span class="html-italic">H/Lpp</span> = 0.02, and <span class="html-italic">χ</span> = 90 degrees.</p>
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<p>Comparison of the restoring variation in only the FK component between three heeling angles and two methods with <span class="html-italic">Fn</span> = 0.0, <span class="html-italic">λ/Lpp</span> = 1.243644, <span class="html-italic">H/Lpp</span> = 0.02, and <span class="html-italic">χ</span> = 90 degrees.</p>
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<p>The effect of sway and yaw motions on the contribution to restoring variation with <span class="html-italic">φ</span> = 10 degrees, <span class="html-italic">Fn</span> = 0.0, <span class="html-italic">λ/Lpp</span> = 1.243644, <span class="html-italic">H/Lpp</span> = 0.02, and <span class="html-italic">χ</span> = 90 degrees.</p>
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<p>Comparison of the roll amplitudes between the experiment and three numerical methods under the intact condition with <span class="html-italic">Fn</span> = 0.0, 0.272, <span class="html-italic">λ/Lpp</span> = 1.243644, <span class="html-italic">H/Lpp</span> = 0.02, and <span class="html-italic">χ</span> = 90 degrees.</p>
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<p>Comparison of time-domain roll between the experiment and two numerical methods under the intact condition with <span class="html-italic">Fn</span> = 0.0, <span class="html-italic">λ/Lpp</span> = 1.243644, <span class="html-italic">H/Lpp</span> = 0.02, and <span class="html-italic">χ</span> = 90 degrees.</p>
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<p>Comparison of the roll amplitudes between the experiment and three numerical methods under the middle-damaged condition with <span class="html-italic">Fn</span> = 0.0, 0.272, <span class="html-italic">λ/Lpp</span> = 1.243644, <span class="html-italic">H/Lpp</span> = 0.02, and <span class="html-italic">χ</span> = 90 degrees.</p>
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<p>Comparison of time-domain roll between the experiment and two numerical methods under the middle-damaged condition with <span class="html-italic">Fn</span> = 0.0, <span class="html-italic">λ/Lpp</span> = 1.243644, <span class="html-italic">H/Lpp</span> = 0.02, and <span class="html-italic">χ</span> = 90 degrees.</p>
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<p>Comparison of the significant and maximum roll angles between the experiment and three numerical methods under intact conditions with <span class="html-italic">Fn</span> = 0.0, 0.272, <span class="html-italic">T<sub>01</sub></span> = 9.7 m, <span class="html-italic">H<sub>1/3</sub></span> = 4 m, and <span class="html-italic">χ</span> = 90 degrees.</p>
Full article ">Figure 13 Cont.
<p>Comparison of the significant and maximum roll angles between the experiment and three numerical methods under intact conditions with <span class="html-italic">Fn</span> = 0.0, 0.272, <span class="html-italic">T<sub>01</sub></span> = 9.7 m, <span class="html-italic">H<sub>1/3</sub></span> = 4 m, and <span class="html-italic">χ</span> = 90 degrees.</p>
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<p>Comparison of the significant and maximum roll angles between the experiment and one numerical method under three damaged conditions with <span class="html-italic">Fn</span> = 0.0, <span class="html-italic">T<sub>01</sub></span> = 9.7 s, <span class="html-italic">H<sub>1/3</sub></span> = 4 m, and <span class="html-italic">χ</span> = 90 degrees.</p>
Full article ">Figure 14 Cont.
<p>Comparison of the significant and maximum roll angles between the experiment and one numerical method under three damaged conditions with <span class="html-italic">Fn</span> = 0.0, <span class="html-italic">T<sub>01</sub></span> = 9.7 s, <span class="html-italic">H<sub>1/3</sub></span> = 4 m, and <span class="html-italic">χ</span> = 90 degrees.</p>
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<p>Comparison of the significant and maximum roll angles between the experiment and one numerical method under three damaged conditions with <span class="html-italic">Fn</span> = 0.272, <span class="html-italic">T<sub>01</sub></span> = 9.7 s, <span class="html-italic">H<sub>1/3</sub></span> = 4 m, and <span class="html-italic">χ</span> = 90 degrees.</p>
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20 pages, 25566 KiB  
Article
Reassortants of the Highly Pathogenic Influenza Virus A/H5N1 Causing Mass Swan Mortality in Kazakhstan from 2023 to 2024
by Kulyaisan T. Sultankulova, Takhmina U. Argimbayeva, Nurdos A. Aubakir, Arailym Bopi, Zamira D. Omarova, Aibarys M. Melisbek, Kobey Karamendin, Aidyn Kydyrmanov, Olga V. Chervyakova, Aslan A. Kerimbayev, Yerbol D. Burashev, Yermukhanmet T. Kasymbekov and Mukhit B. Orynbayev
Animals 2024, 14(22), 3211; https://doi.org/10.3390/ani14223211 - 8 Nov 2024
Viewed by 1106
Abstract
In the winter of 2023/2024, the mass death of swans was observed on Lake Karakol on the eastern coast of the Caspian Sea. From 21 December 2023 to 25 January 2024, 1132 swan corpses (Cygnus olor, Cygnus cygnus) were collected [...] Read more.
In the winter of 2023/2024, the mass death of swans was observed on Lake Karakol on the eastern coast of the Caspian Sea. From 21 December 2023 to 25 January 2024, 1132 swan corpses (Cygnus olor, Cygnus cygnus) were collected and disposed of on the coast by veterinary services and ecologists. Biological samples were collected from 18 birds for analysis at different dates of the epizootic. It was found that the influenza outbreak was associated with a high concentration of migrating birds at Lake Karakol as a result of a sharp cold snap in the northern regions. At different dates of the epizootic, three avian influenza A/H5N1 viruses of clade 2.3.4.4.b were isolated from dead birds and identified as highly pathogenic viruses (HPAIs) based on the amino acid sequence of the hemagglutinin multi-base proteolytic cleavage site (PLREKRRRKR/G). A phylogenetic analysis showed that the viruses isolated from the swans had reassortations in the PB2, PB1, and NP genes between highly pathogenic (HP) and low-pathogenic (LP) avian influenza viruses. Avian influenza viruses A/Cygnus cygnus/Karakol lake/01/2024(H5N1) and A/Mute swan/Karakol lake/02/2024(H5N1) isolated on 10 January 2024 received PB2, PB1, and NP from LPAIV, while A/Mute swan/Mangystau/9809/2023(H5N1) isolated on 26 December 2023 received PB1 and NP from LPAIV, indicating that the H5N1 viruses in this study are new reassortants. All viruses showed amino acid substitutions in the PB2, PB1, NP, and NS1 segments, which are critical for enhanced virulence or adaptation in mammals. An analysis of the genomes of the isolated viruses showed that bird deaths during different periods of the epizootic were caused by different reassortant viruses. Kazakhstan is located at the crossroads of several migratory routes of migratory birds, and the possible participation of wild birds in the introduction of various pathogens into the regions of Kazakhstan requires further study. Full article
(This article belongs to the Special Issue Interdisciplinary Perspectives on Wildlife Disease Ecology)
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Figure 1

Figure 1
<p>Wild bird death site in the winter of 2023/2024.</p>
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<p>Dynamics of swan mortality on Lake Karakol in the winter of 2023/2024 (according to data from the veterinary service of the Mangystau region).</p>
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<p>Remains of a swan’s corpse.</p>
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<p>Swan corpse with signs of diarrhea and without a right leg (with a gnawed leg).</p>
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<p>Stray dog on the shore of Lake Karakol.</p>
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<p>Whooper swan (<span class="html-italic">Cygnus cygnus</span>) (an adult).</p>
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<p>Whooper swan (<span class="html-italic">Cygnus olor</span>) (a cygnet).</p>
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<p>Sick bird (a cygnet).</p>
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<p>Heart. Haemorrhages in the myocard.</p>
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<p>Haemorrhages in the liver.</p>
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<p>Lung edema.</p>
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<p>Phylogenetic trees, including complete PB2 (<b>A</b>), PB1 (<b>B</b>), PA (<b>C</b>), HA (<b>D</b>), NP (<b>E</b>), NA (<b>F</b>), M (<b>G</b>), and NS (<b>H</b>) genes, of Kazakhstani HPAIV H5N1 strains isolated from swans on the coast of Lake Karakol, located on the eastern shore of the Kazakhstani part of the Caspian Sea from 2023 to 2024 and publicly available sequences (GenBank). The strains investigated in this study are marked with triangles, squares, and circles: <span class="html-fig-inline" id="animals-14-03211-i001"><img alt="Animals 14 03211 i001" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i001.png"/></span>—A/<span class="html-italic">Mute swan</span>/Mangystau/9809/2023(H5N1); <span class="html-fig-inline" id="animals-14-03211-i002"><img alt="Animals 14 03211 i002" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i002.png"/></span>—A/<span class="html-italic">Cygnus cygnus</span>/Karakol lake/01/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i003"><img alt="Animals 14 03211 i003" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i003.png"/></span>—A/Mute swan/Karakol lake/02/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i004"><img alt="Animals 14 03211 i004" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i004.png"/></span>—A/mute swan/Mangystau/1-S24R-2/2024(H5N1) (virus isolated at NVRC and KazNARU by Tabynov K et al. in 2024 [<a href="#B27-animals-14-03211" class="html-bibr">27</a>]).</p>
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<p>Phylogenetic trees, including complete PB2 (<b>A</b>), PB1 (<b>B</b>), PA (<b>C</b>), HA (<b>D</b>), NP (<b>E</b>), NA (<b>F</b>), M (<b>G</b>), and NS (<b>H</b>) genes, of Kazakhstani HPAIV H5N1 strains isolated from swans on the coast of Lake Karakol, located on the eastern shore of the Kazakhstani part of the Caspian Sea from 2023 to 2024 and publicly available sequences (GenBank). The strains investigated in this study are marked with triangles, squares, and circles: <span class="html-fig-inline" id="animals-14-03211-i001"><img alt="Animals 14 03211 i001" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i001.png"/></span>—A/<span class="html-italic">Mute swan</span>/Mangystau/9809/2023(H5N1); <span class="html-fig-inline" id="animals-14-03211-i002"><img alt="Animals 14 03211 i002" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i002.png"/></span>—A/<span class="html-italic">Cygnus cygnus</span>/Karakol lake/01/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i003"><img alt="Animals 14 03211 i003" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i003.png"/></span>—A/Mute swan/Karakol lake/02/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i004"><img alt="Animals 14 03211 i004" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i004.png"/></span>—A/mute swan/Mangystau/1-S24R-2/2024(H5N1) (virus isolated at NVRC and KazNARU by Tabynov K et al. in 2024 [<a href="#B27-animals-14-03211" class="html-bibr">27</a>]).</p>
Full article ">Figure 12 Cont.
<p>Phylogenetic trees, including complete PB2 (<b>A</b>), PB1 (<b>B</b>), PA (<b>C</b>), HA (<b>D</b>), NP (<b>E</b>), NA (<b>F</b>), M (<b>G</b>), and NS (<b>H</b>) genes, of Kazakhstani HPAIV H5N1 strains isolated from swans on the coast of Lake Karakol, located on the eastern shore of the Kazakhstani part of the Caspian Sea from 2023 to 2024 and publicly available sequences (GenBank). The strains investigated in this study are marked with triangles, squares, and circles: <span class="html-fig-inline" id="animals-14-03211-i001"><img alt="Animals 14 03211 i001" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i001.png"/></span>—A/<span class="html-italic">Mute swan</span>/Mangystau/9809/2023(H5N1); <span class="html-fig-inline" id="animals-14-03211-i002"><img alt="Animals 14 03211 i002" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i002.png"/></span>—A/<span class="html-italic">Cygnus cygnus</span>/Karakol lake/01/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i003"><img alt="Animals 14 03211 i003" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i003.png"/></span>—A/Mute swan/Karakol lake/02/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i004"><img alt="Animals 14 03211 i004" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i004.png"/></span>—A/mute swan/Mangystau/1-S24R-2/2024(H5N1) (virus isolated at NVRC and KazNARU by Tabynov K et al. in 2024 [<a href="#B27-animals-14-03211" class="html-bibr">27</a>]).</p>
Full article ">Figure 12 Cont.
<p>Phylogenetic trees, including complete PB2 (<b>A</b>), PB1 (<b>B</b>), PA (<b>C</b>), HA (<b>D</b>), NP (<b>E</b>), NA (<b>F</b>), M (<b>G</b>), and NS (<b>H</b>) genes, of Kazakhstani HPAIV H5N1 strains isolated from swans on the coast of Lake Karakol, located on the eastern shore of the Kazakhstani part of the Caspian Sea from 2023 to 2024 and publicly available sequences (GenBank). The strains investigated in this study are marked with triangles, squares, and circles: <span class="html-fig-inline" id="animals-14-03211-i001"><img alt="Animals 14 03211 i001" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i001.png"/></span>—A/<span class="html-italic">Mute swan</span>/Mangystau/9809/2023(H5N1); <span class="html-fig-inline" id="animals-14-03211-i002"><img alt="Animals 14 03211 i002" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i002.png"/></span>—A/<span class="html-italic">Cygnus cygnus</span>/Karakol lake/01/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i003"><img alt="Animals 14 03211 i003" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i003.png"/></span>—A/Mute swan/Karakol lake/02/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i004"><img alt="Animals 14 03211 i004" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i004.png"/></span>—A/mute swan/Mangystau/1-S24R-2/2024(H5N1) (virus isolated at NVRC and KazNARU by Tabynov K et al. in 2024 [<a href="#B27-animals-14-03211" class="html-bibr">27</a>]).</p>
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<p>Hypothetical reassortment events of the A/<span class="html-italic">Cygnus cygnus</span>/Karakol lake/01/2024(H5N1) viruses. The eight genes are shown in <a href="#animals-14-03211-t001" class="html-table">Table 1</a> and are as follows: PB2, PB1, PA, HA, NP, NA, M, and NS. The colors of the bars indicate the different sources of the gene segments.</p>
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<p>Hypothetical reassortment events of the A/<span class="html-italic">mute swan</span>/Mangystau/1-S24R-2/2024(H5N1) viruses.</p>
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<p>Hypothetical reassortment events of the A/<span class="html-italic">Mute swan</span>/Mangystau/9809/2023(H5N1) viruses.</p>
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11 pages, 5907 KiB  
Article
Mass Mortality in Terns and Gulls Associated with Highly Pathogenic Avian Influenza Viruses in Caspian Sea, Kazakhstan
by Aidyn Kydyrmanov, Kobey Karamendin, Yermukhammet Kassymbekov, Klara Daulbayeva, Temirlan Sabyrzhan, Yelizaveta Khan, Sardor Nuralibekov, Barshagul Baikara and Sasan Fereidouni
Viruses 2024, 16(11), 1661; https://doi.org/10.3390/v16111661 - 24 Oct 2024
Viewed by 1798
Abstract
Mass mortality in Caspian terns (Hydroprogne caspia), Pallas’s gulls (Ichthyaetus ichthyaetus), and Caspian gulls (Larus cachinnans) was recorded on the northeastern shores of the Caspian Sea in June 2022. More than 5000 gulls and terns died due [...] Read more.
Mass mortality in Caspian terns (Hydroprogne caspia), Pallas’s gulls (Ichthyaetus ichthyaetus), and Caspian gulls (Larus cachinnans) was recorded on the northeastern shores of the Caspian Sea in June 2022. More than 5000 gulls and terns died due to the outbreak. The outbreak was investigated in the field, and representative numbers of samples were collected and analyzed using pathological, virological, and molecular methods. Highly pathogenic avian influenza A (H5N1) viruses were detected and isolated from samples collected from dead birds. Genetic and phylogenetic analyses indicated that the hemagglutinin (HA) genes belonged to the clade 2.3.4.4.b of the H5Nx HPAI viruses, B2 sub-lineage, and were closely related to the highly pathogenic influenza viruses, caused an outbreak in wild birds with a high mortality rate in the western part of the Caspian Sea. Full article
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Figure 1

Figure 1
<p>Satellite map of the northern Caspian Sea illustrates the main die-off sites. The red dots indicate the locations where bird die-offs were registered, and the lime-colored dots represent the isles where avian colonies have remained uninfected (the satellite map was modified after A. Kenzhegaliev et al. [<a href="#B19-viruses-16-01661" class="html-bibr">19</a>]).</p>
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<p>Mortality among the tern and gull populations on the DC-04 (<b>a</b>) and Zuid-West (<b>b</b>) islands.</p>
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<p>The livers, hearts, and air sacs of two dead Caspian gulls exhibited evidence of hepatitis, hepatic hemorrhage, pericarditis, and airsacculitis, respectively. The black arrows indicate the presence of pathological lesions on the organs.</p>
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<p>The phylogenetic tree of the full-length coding sequence of the <span class="html-italic">HA</span> gene of the HPAIV H5N1 subtype A/Caspian tern/Atyrau/9184/2022 (marked with red dot) and other related viruses isolated mainly in 2021, 2022, and 2023 from wild birds (GenBank and GISAID databases). The numbers at the nodes indicate the maximum likelihood bootstrap values of 500 replicates under the specified model. Only bootstrap values &gt;70 are depicted. The bar represents a substitution rate of 0.02 nucleotides per site.</p>
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21 pages, 3075 KiB  
Article
Investigations on the Health Status and Infection Risk of Harbour Seals (Phoca vitulina) from Waters of the Lower Saxon Wadden Sea, Germany
by Ursula Siebert, Jan Lakemeyer, Martin Runge, Peter Lienau, Silke Braune, Edda Bartelt, Miguel L. Grilo and Ralf Pund
Animals 2024, 14(20), 2920; https://doi.org/10.3390/ani14202920 - 10 Oct 2024
Cited by 1 | Viewed by 1330
Abstract
Harbour seals (Phoca vitulina) are the most common pinniped species in the Wadden Sea of Schleswig-Holstein, Hamburg and Lower Saxony, Germany. Their numbers have recovered after significant depletion due to viral outbreaks and effects of anthropogenic activities like pollution and habitat [...] Read more.
Harbour seals (Phoca vitulina) are the most common pinniped species in the Wadden Sea of Schleswig-Holstein, Hamburg and Lower Saxony, Germany. Their numbers have recovered after significant depletion due to viral outbreaks and effects of anthropogenic activities like pollution and habitat disturbance. Within the Wadden Sea National Park of Lower Saxony the harbour seal is protected. As a top predator in the Wadden Sea ecosystem, the harbour seal is a sentinel species for the state of the environment. Between 2015 and 2017, a total of 80 stranded dead harbour seals were collected along the coastline of Lower Saxony and submitted for pathological investigations. Of these, 70 seals were born in the same year (0–7 months, age group 1) and eight in the previous year (8–19 months, age group 2), due to high mortality rates in these age groups. However, two perennial animals were also available for examination during this period, one of which was in good nutritional condition. Many of the seals that had been mercy-killed and found dead were in poor nutritional status. Histopathological, microbiological, parasitological and virological examinations were conducted on 69 individuals (86% (69/80)) in a suitable state of preservation. Respiratory tract parasitosis, cachexia, and bronchopneumonia were the most common causes of death or disease. Overall, there was no evidence of a relapse of a viral disease outbreak. Macrowaste, such as plastic waste or fishery-related debris, were not found in any gastrointestinal tract of the animals examined. There was also no evidence of grey seal predation. Weakness and cachexia were prominent causes of disease and death in harbour seals found within a few weeks after birth, but bronchopneumonia and septicaemia also developed in slightly older animals. Frequently found microbial pathogens in seals from Lower Saxony were similar to those found in other studies on seals from the Wadden Sea region in Schleswig-Holstein, for example streptococci and Escherichia coli/v. haemolytica, Brucella spp. and Erysipelothrix rhusiopathiae, potentially human pathogenic germs. The results of the examinations of dead harbour seals from Lower Saxony show that pathological investigations on a representative number of animals deliver urgently needed information on the health status of the population. The results represent an important contribution to the state of the top predators of the Wadden Sea as part of the obligations within the Trilateral Wadden Sea Agreement, Oslo and Paris Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) and the Marine Framework Directive. The investigations should be continued as a matter of urgency and the stranding network should be expanded. Full article
(This article belongs to the Special Issue Wildlife Diseases: Pathology and Diagnostic Investigation)
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Figure 1
<p>Locations of 80 harbour seals found on the Wadden Sea coast between 2015 and 2017 in the state of Lower Saxony, Germany. Europe map created with mapchart.net (accessed 1 January 2024). Lower Saxony map created with Microsoft Excel 2019 (©Microsoft, Albuquerque, NM, USA).</p>
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<p>Reference points of the length measurements obtained in the post-mortem analysis of dead seals.</p>
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<p>Reference points of the blubber thickness measurements (fatty tissue of the subcutis, blubber) in seals.</p>
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<p>Preservation status of the seals included in this study (<span class="html-italic">n</span> = 80): 1—fresh, 2—first signs of autolysis, 3—moderate autolysis, 4—advanced autolysis, 5—macerated or advanced autolysis. Graph produced using GraphPad Prism1 (GraphPad Software, San Diego, CA, USA, version 5.01).</p>
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<p>Severe lungworm infection with complete obstruction of the right main bronchus by pulmonary nematodes (arrow).</p>
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<p>Roundworms (<span class="html-italic">Otostrongylus circumlitus</span>) isolated from the trachea and fixed in 70% ethanol.</p>
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<p>Harbour seal, lung: severe pulmonary endoparasitosis with nematodes in the parenchyma (arrows) associated with lobular atelectasis (A). Additionally, there is a moderate interstitial oedema (arrowheads) and an alveolar emphysema in the adjacent pulmonary tissue (asterisks). HE (4×).</p>
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<p>Harbour seal, lung: Bronchiolus with intraluminal nematodes and mild chronic peribronchitis (arrows). HE (20×).</p>
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<p>Harbour seal, lung: severe pulmonary endoparasitosis with nematodes in a bronchus (arrowhead) and in the parenchyma (arrows) with associated atelectasis. Adjacent pulmonary tissue displays alveolar emphysema (asterisks). HE (4×).</p>
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<p>Severe gastric parasitic infection. Several roundworms (arrow) were found in the lumen of the stomach. The gastric mucosa is hypertrophied, forming broad increased folds and shows signs of petechiation (pin-point sized haemorrhages).</p>
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<p>Seal, ventral view. Perianal area contaminated with diarrhoea (black arrows). Posterior right flipper showing an open fracture involving the tarsal and metatarsal bones, with partial shattering, bone dislocation and dislocated fracture ends. In the fracture area, there is severe skin and subcutaneous tissue loss (10 × 8 cm). One of the metatarsal bones (white arrowheads) protrudes from the open wound, while parts of the tarsal bones (white arrow) can also be observed.</p>
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<p>Seal, suppurative inflammation of the navel; when cut, a yellowish-grey and slightly viscous mass protruded (umbilical abscess, arrow).</p>
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<p>One seal showed superficial to 0.5–2 cm lesions on different parts of the skin (polytrauma); in the plantar metatarsal region of the right posterior flipper, there are clearly visible, fresh, up to 10 euro cent-sized, round-ovoid, bleeding, 0.2–0.5 cm deep skin wounds (arrows).</p>
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12 pages, 2555 KiB  
Article
Plastics at an Offshore Fish Farm on the South Coast of Madeira Island (Portugal): A Preliminary Evaluation of Their Origin, Type, and Impact on Farmed Fish
by Mariana Martins, Ana Pombo, Susana Mendes and Carlos A. P. Andrade
Environments 2024, 11(9), 202; https://doi.org/10.3390/environments11090202 - 14 Sep 2024
Viewed by 1159
Abstract
Plastic pollution is a global problem affecting all ecosystems, and it represents most of the marine litter. Offshore aquaculture is a sector particularly vulnerable to this issue. To investigate this concern, the present study employed videography to monitor macroplastics at an offshore fish [...] Read more.
Plastic pollution is a global problem affecting all ecosystems, and it represents most of the marine litter. Offshore aquaculture is a sector particularly vulnerable to this issue. To investigate this concern, the present study employed videography to monitor macroplastics at an offshore fish farm on Madeira Island (Portugal) and analysis of fish gut content to evaluate macroplastic ingestion by farmed sea bream Sparus aurata. Our analysis revealed that the majority of identified plastic debris originated from domestic use (66.66%) and fisheries/aquaculture activities (24.99%). While the number of dead fish suitable for sampling was limited (1.05% of the total mortality), macroplastic debris ingestion was identified in 5.15% of the total mortalities and reported for the first time in species in offshore farming conditions. Fish ingested fragmented plastic sheets, with the amount positively correlated with fish weight (r = 0.621, p = 0.031, n = 12). Notably, the stretched length of these fragments exceeded 50% of the standard length of most fish. Inconsistencies were observed in the number of samples collected per cage and per week. To ensure robust results, these discrepancies should be rectified in future studies. Additionally, extending the sampling period to encompass all seasons would be beneficial for a more comprehensive understanding of seasonal variations in plastic occurrence. Full article
(This article belongs to the Special Issue Plastics Pollution in Aquatic Environments)
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<p>Marismar’s fish farm location and its offshore cages. (<b>a</b>) Madeira Island’s map; (<b>b</b>) location of offshore cages; (<b>c</b>) fish farm concession area; (<b>d</b>) display of fish farm cages.</p>
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<p>The main ocean current directions (<b>A</b>) at the fish farm and the number of plastics found at each cage and quadrant (fish farm cages identified from 1 to 10) and (<b>B</b>) Diagram of frequency (%) of ocean current directions observed at the fish farm.</p>
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<p>Average (and standard deviation) of the condition factor (K) of individuals that died of natural/unknown causes (dead fish) with and without plastic and of individuals that were sampled monthly (fish captured alive) from cages 4, 5, 6, and 9 of Marismar’s fish farm. Symbols * and # represent significant differences (i.e., whenever <span class="html-italic">p</span>-value &lt; 0.05).</p>
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<p>Sea bream <span class="html-italic">Sparus aurata</span> sampled from Marismar’s fish farm, (<b>A</b>,<b>B</b>). In the foreground, plastic removed from the gastrointestinal tract from each fish.</p>
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<p>Correlation between weight of fish and amount (weight) of macroplastics found in the digestive tract (n = 12; r = 0.621; <span class="html-italic">p</span> = 0.031).</p>
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21 pages, 1416 KiB  
Article
A Novel Medium Access Policy Based on Reinforcement Learning in Energy-Harvesting Underwater Sensor Networks
by Çiğdem Eriş, Ömer Melih Gül and Pınar Sarısaray Bölük
Sensors 2024, 24(17), 5791; https://doi.org/10.3390/s24175791 - 6 Sep 2024
Cited by 2 | Viewed by 1095
Abstract
Underwater acoustic sensor networks (UASNs) are fundamental assets to enable discovery and utilization of sub-sea environments and have attracted both academia and industry to execute long-term underwater missions. Given the heightened significance of battery dependency in underwater wireless sensor networks, our objective is [...] Read more.
Underwater acoustic sensor networks (UASNs) are fundamental assets to enable discovery and utilization of sub-sea environments and have attracted both academia and industry to execute long-term underwater missions. Given the heightened significance of battery dependency in underwater wireless sensor networks, our objective is to maximize the amount of harvested energy underwater by adopting the TDMA time slot scheduling approach to prolong the operational lifetime of the sensors. In this study, we considered the spatial uncertainty of underwater ambient resources to improve the utilization of available energy and examine a stochastic model for piezoelectric energy harvesting. Considering a realistic channel and environment condition, a novel multi-agent reinforcement learning algorithm is proposed. Nodes observe and learn from their choice of transmission slots based on the available energy in the underwater medium and autonomously adapt their communication slots to their energy harvesting conditions instead of relying on the cluster head. In the numerical results, we present the impact of piezoelectric energy harvesting and harvesting awareness on three lifetime metrics. We observe that energy harvesting contributes to 4% improvement in first node dead (FND), 14% improvement in half node dead (HND), and 22% improvement in last node dead (LND). Additionally, the harvesting-aware TDMA-RL method further increases HND by 17% and LND by 38%. Our results show that the proposed method improves in-cluster communication time interval utilization and outperforms traditional time slot allocation methods in terms of throughput and energy harvesting efficiency. Full article
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<p>3D Sample network topology showing gateway and sub-sea node placements.</p>
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<p>Training paradigm of multi-agent reinforcement learning system.</p>
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<p>Example self scheduling of transmission slots representing high/low harvesting conditions.</p>
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<p>Number of data signals received at the gateway per energy.</p>
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<p>Number of data signals received at the gateway over time.</p>
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<p>Number of alive nodes per data items received.</p>
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<p>Total harvested energy in system in rounds.</p>
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<p>Number of alive nodes over simulation time.</p>
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<p>Number of alive nodes over simulation rounds.</p>
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23 pages, 16575 KiB  
Article
Remote Sensing of Floodwater-Induced Subsurface Halite Dissolution in a Salt Karst System, with Implications for Landscape Evolution: The Western Shores of the Dead Sea
by Gidon Baer, Ittai Gavrieli, Iyad Swaed and Ran N. Nof
Remote Sens. 2024, 16(17), 3294; https://doi.org/10.3390/rs16173294 - 4 Sep 2024
Cited by 1 | Viewed by 1503
Abstract
We study the interrelations between salt karst and landscape evolution at the Ze’elim and Hever alluvial fans, Dead Sea (DS), Israel, in an attempt to characterize the ongoing surface and subsurface processes and identify future trends. Using light detection and ranging, interferometric synthetic [...] Read more.
We study the interrelations between salt karst and landscape evolution at the Ze’elim and Hever alluvial fans, Dead Sea (DS), Israel, in an attempt to characterize the ongoing surface and subsurface processes and identify future trends. Using light detection and ranging, interferometric synthetic aperture radar, drone photography, time-lapse cameras, and direct measurements of floodwater levels, we document floodwater recharge through riverbed sinkholes, subsurface salt dissolution, groundwater flow, and brine discharge at shoreline sinkholes during the years 2011–2023. At the Ze’elim fan, most of the surface floodwater drains into streambed sinkholes and discharges at shoreline sinkholes, whereas at the Hever fan, only a small fraction of the floodwater drains into sinkholes, while the majority flows downstream to the DS. This difference is attributed to the low-gradient stream profiles in Ze’elim, which enable water accumulation and recharge in sinkholes and their surrounding depressions, in contrast with the higher-gradient Hever profiles, which yield high-energy floods capable of carrying coarse gravel that eventually fill the sinkholes. The rapid drainage of floodwater into sinkholes also involves slope failure due to pore-pressure drop and cohesion loss within hours after each drainage event. Surface subsidence lineaments detected by InSAR indicate the presence of subsurface dissolution channels between recharge and discharge sites in the two fans and in the nearby Lynch straits. Subsidence and streambed sinkholes occur in most other fans and streams that flow to the DS; however, with the exception of Ze’elim, all other streams show only minor or no recharge along their course. This is due to either the high-gradient profiles, the gravelly sediments, the limited floods, or the lack of conditions for sinkhole development in the other streambeds. Thus, understanding the factors that govern the flood-related karst formation is of great importance for predicting landscape evolution in the DS region and elsewhere and for sinkhole hazard assessment. Full article
(This article belongs to the Special Issue Remote Sensing of the Dead Sea Region)
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<p>LiDAR topography of the two study areas draped upon hill−shaded DSMs. (<b>a</b>) Ze’elim fan. Gully numbers (in white) are after [<a href="#B3-remotesensing-16-03294" class="html-bibr">3</a>]. (<b>b</b>) Hever fan. (<b>c</b>) Location maps of the study areas.</p>
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<p>Elevation profiles along Ze’elim and Hever riverbeds, May 2020. Note the low gradients (1−3%) and fine-grained composition of the Ze’elim riverbeds (blue-green profiles, for location, see <a href="#remotesensing-16-03294-f001" class="html-fig">Figure 1</a>a), in contrast with the high gradients (3−4.5%) and coarse gravel sediments of the Hever riverbeds (red-brown profiles; for location, see <a href="#remotesensing-16-03294-f001" class="html-fig">Figure 1</a>b), which decrease to 1−1.5% only at their easternmost parts.</p>
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<p>(<b>a</b>) Time-lapse camera and drone, overlooking gully 14 recharge sinkhole. The blue arrow marks the flow direction from west to east. (<b>b</b>) View south at the DSW canal as floodwater crosses the overpasses. Photo courtesy of DSW. (<b>c</b>) Locations of hydrometers (marked by white arrows) that are installed at an overpass. E and W mark eastern and western hydrometers.</p>
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<p>Photos of recharge sinkholes at Ze’elim fan streambeds. Blue arrows mark the flow direction. Ab—abandoned gullies, overhanging downstream of the recharge sinkholes. For location, see <a href="#remotesensing-16-03294-f001" class="html-fig">Figure 1</a>a. (<b>a</b>) Gully 6. (<b>b</b>) Gully 7. (<b>c</b>) Gully 13. (<b>d</b>) Gully 14. Drone picture was taken by Liran Ben Moshe.</p>
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<p>(<b>a</b>) Floodwater recharge (red circles) and discharge (blue rectangles) sites at the Ze’elim fan. Red and yellow triangles mark locations and operation intervals of the TLCs. Yellow numbers denote gully numbers (after [<a href="#B3-remotesensing-16-03294" class="html-bibr">3</a>]). (<b>b</b>) Drone photograph, 2 January 2020, showing the discharge sinkholes (rectangles) and TLCs (triangles) within and around the shoreline sinkholes of gully 10. Note that not all TLCs operated simultaneously.</p>
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<p>Discharge sinkhole 10a (see location in <a href="#remotesensing-16-03294-f005" class="html-fig">Figure 5</a>b). (<b>a</b>) View east, February 2024. (<b>b</b>) TLC picture showing water discharge following the 25 March 2019 flood. (<b>c</b>) The nested sinkhole at the northern wall of the major sinkhole, February 2024, exposing the “Sinkhole Salt” layer (white layers with small cavities), the dissolution channel openings, and groundwater flow.</p>
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<p>Sinkholes and subsidence along the course of gully 3. (<b>a</b>) LiDAR DSM, July 2023 (see location in <a href="#remotesensing-16-03294-f001" class="html-fig">Figure 1</a>). The location of profile A−A’ (panel <b>d</b>) is shown in a dashed white line. The area around the recharge sinkhole is marked by a circle. (<b>b</b>) Drone photograph of the recharge area (sinkhole marked by white circle), January 2024, taken by Liran Ben Moshe. (<b>c</b>) Streambed sinkhole recharging floodwater after the 15.2.2024 flood. (<b>d</b>) Elevation profile A−A’ along the gully, September 2023 (location shown in panel <b>a</b>). Note that this recharge sinkhole does not appear in 2020 (see Ze’elim 3 profile in <a href="#remotesensing-16-03294-f002" class="html-fig">Figure 2</a>).</p>
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<p>Water levels at five overpasses during the 21–22 November 2021 flood in Ze’elim. See inset for location. The overpasses are marked by white numbers, and streams are marked by yellow numbers. E and W stand for eastern and western hydrographs, respectively (<a href="#remotesensing-16-03294-f003" class="html-fig">Figure 3</a>c).</p>
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<p>An interferogram of the Ze’elim fan spanning 44 days in early 2024, showing subsidence lineaments that are interpreted as surface manifestations of subsurface dissolution channels. The two acquisition times are 13 January 2024 and 26 February 2024. WZSL, EZSL, and NZSL stand for western, eastern, and northern Ze’elim subsidence lineaments, shown by white, orange, and yellow arrows, respectively. Gully numbers are marked in white (after [<a href="#B3-remotesensing-16-03294" class="html-bibr">3</a>]).</p>
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<p>Surface (dashed white lines) and proposed subsurface water pathways (dashed yellow lines) in Ze’elim: a southern pathway from gully 14 to the western side of sinkhole 10 (10a in <a href="#remotesensing-16-03294-f005" class="html-fig">Figure 5</a>b), and central pathways from gullies 5, 6, and 7 to the eastern side of sinkhole 10 (10f in <a href="#remotesensing-16-03294-f005" class="html-fig">Figure 5</a>b). Gully numbers are after [<a href="#B3-remotesensing-16-03294" class="html-bibr">3</a>].</p>
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<p>(<b>a</b>,<b>b</b>) Annual surface elevation changes in the Hever fan, draped upon LiDAR DSMs. River incision, riverbank collapse, subsidence, and sinkholes are displayed by negative (blue) values. Aggradation of alluvial material along the streambeds and within sinkholes is displayed by positive (red) values and by white arrows. White ellipses mark subsidence around sinkhole clusters. The black arrow in (<b>a</b>) points at a meandering subsidence lineament, interpreted as the surface manifestation of a subsurface dissolution channel. (<b>c</b>) Interferogram showing sinkhole-related subsidence (semi-circular fringe colors) and a meandering subsidence lineament (marked by white arrows) that is interpreted as the surface manifestation of a subsurface dissolution channel between the western cluster of recharging sinkholes and the eastern subsidence zone (similar to the lineament in panel <b>a</b>). The acquisition times of the two images are 7 June 2018 and 18 June 2018.</p>
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<p>(<b>a</b>–<b>c</b>) TLC photos showing recharge of floodwater at sinkholes in the northern branch of Hever fan during the 20 February 2015 flood. Blue arrows mark the braided streambed flow direction. Note the water overflow and the filling of the sinkhole with gravel at the final hours of the flood (panels (<b>b</b>) and (<b>c</b>), respectively). (<b>d</b>) Drone picture of 7 February 2019 floodwater drained into recharge sinkholes along the northern Hever branch with overflowing water continuing downstream.</p>
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<p>(<b>a</b>) Discharge sinkholes at the lower part of the southern Hever branch. (<b>b</b>) Offshore discharge sites at the Hever shoreline (white arrows). (<b>c</b>) A small salt chimney 2 m offshore in Hever. (<b>d</b>) <span class="html-italic">Anabasis setifera</span> vegetation at the lower Hever southern streambed. For location, see the black arrow in <a href="#remotesensing-16-03294-f011" class="html-fig">Figure 11</a>a.</p>
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<p>Linear and meandering subsidence patterns at the Lynch straits (for location, see <a href="#remotesensing-16-03294-f001" class="html-fig">Figure 1</a>c). (<b>a</b>) Subtraction map of LiDAR DSMs between 2023 and 2014. (<b>b</b>) Interferogram between 2 and 13 April 2018. The white arrows point to subsidence lineaments that are interpreted to form above subsurface salt dissolution channels.</p>
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<p>Stream gradients along the western shoreline of the DS. Color legend distinguishes between low-gradient, mud-dominated streams (green); high-gradient, gravel-dominated streams (red); and intermediate-gradient mixed mud-gravel streams (yellow).</p>
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17 pages, 4134 KiB  
Article
Direct and Remote Sensing Monitoring of Plant Salinity Stress in a Coastal Back-Barrier Environment: Mediterranean Pine Forest Stress and Mortality as a Case Study
by Luigi Alessandrino, Elisabetta Giuditta, Salvatore Faugno, Nicolò Colombani and Micòl Mastrocicco
Remote Sens. 2024, 16(17), 3150; https://doi.org/10.3390/rs16173150 - 26 Aug 2024
Viewed by 799
Abstract
The increase in atmospheric and soil temperatures in recent decades has led to unfavorable conditions for plants in many Mediterranean coastal environments. A typical example can be found along the coast of the Campania region in Italy, within the “Volturno Licola Falciano Natural [...] Read more.
The increase in atmospheric and soil temperatures in recent decades has led to unfavorable conditions for plants in many Mediterranean coastal environments. A typical example can be found along the coast of the Campania region in Italy, within the “Volturno Licola Falciano Natural Reserve”, where a pine forest suffered a dramatic loss of trees in 2021. New pines were planted in 2023 to replace the dead ones, with a larger tree layout and interspersed with Mediterranean bushes to replace the dead pine forest. A direct (in situ) monitoring program was planned to analyze the determinants of the pine salinity stress, coupled with Sentinel-2 L2A data; in particular, multispectral indices NDVI and NDMI were provided by the EU Copernicus service for plant status and water stress level information. Both the vadose zone and shallow groundwater were monitored with continuous logging probes. Vadose zone monitoring indicated that salinity peaked at a 30 cm soil depth, with values up to 1.9 g/L. These harsh conditions, combined with air temperatures reaching peaks of more than 40 °C, created severe difficulties for pine growth. The results of the shallow groundwater monitoring showed that the groundwater salinity was low (0.35–0.4 g/L) near the shoreline since the dune environment allowed rapid rainwater infiltration, preventing seawater intrusion. Meanwhile, salinity increased inland, reaching a peak at the end of the summer, with values up to 2.8 g/L. In November 2023, salts from storm-borne aerosols (“sea spray”) deposited on the soil caused the sea-facing portion of the newly planted pines to dry out. Differently, the pioneer vegetation of the Mediterranean dunes, directly facing the sea, was not affected by the massive deposition of sea spray. The NDMI and NDVI data were useful in distinguishing the old pine trees suffering from increasing stress and final death but were not accurate in detecting the stress conditions of newly planted, still rather short pine trees because their spectral reflectance largely interfered with the adjacent shrub growth. The proposed coupling of direct and remote sensing monitoring was successful and could be applied to detect the main drivers of plant stress in many other Mediterranean coastal environments. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Coastline Monitoring)
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<p>(<b>A</b>) Sentinel-2 true color mosaic image of the study area showing the boundaries (yellow line) of the “Volturno Licola Falciano Natural Reserve” and the location (red point) of the experimental field; (<b>B</b>) Google Earth image of the experimental field showing piezometer locations (P1, P2, and P3), soil profile locations (SA, SB, SC), and soil sample locations (1, 2, 3, 4, 5, and 6); (<b>C</b>) cross-sectional diagram indicating the experimental area zonation and the piezometer locations (P1, P2, and P3); (<b>D</b>) DTM of the experimental field.</p>
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<p>(<b>A</b>) Annual Sentinel-2 L2A NDVI images of each October of the old pine forest in the experimental field from 2016 to 2024; (<b>B</b>) Air T<sub>max</sub> trend (red line) from 2016 to 2024. The black vertical line indicates the new pines’ plantation date; (<b>C</b>) NDVI trend (light green line) and NDVI LOESS line (dark green line) from 2016 to 2024. The horizontal dashed black lines indicate the forest/shrubland interface and the shrubland/grassland interface on the basis of NDVI values, while the vertical black line indicates the new pines’ plantation date; (<b>D</b>) NDMI trend (blue line), NDVI LOESS line (dark blue line), and daily precipitation pattern (gray line) from 2016 to 2024. The horizontal dashed black lines indicate the level of water stress intervals, while the vertical black line indicates the new pines’ plantation date.</p>
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<p>(<b>A</b>) NDVI trend (green line) from March 2023 to January 2024 and daily T<sub>max</sub> trend (red line). The dashed black lines indicate the forest/shrubland interface and the shrubland/grassland interface on the basis of NDVI values; (<b>B</b>) NDMI trend (blue line) from March 2023 to January 2024 and daily precipitation pattern (gray line). The dashed black lines indicate the level of water stress intervals.</p>
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<p>(<b>A</b>) Volumetric water content (VWC) of the vadose zone from March to September 2023 at different depths (−10; −20; −30; −40 cm) and daily precipitation (gray line); (<b>B</b>) TDS of the vadose zone from March to September 2023 at different depths (−10; −20; −30; −40 cm) and daily precipitation (gray line); (<b>C</b>) average daily temperature of the vadose zone at different depths (0.0; −10; −20; −30; −40 cm) from March to September 2023 and daily precipitation (gray line); (<b>D</b>) daily oscillations of the vadose zone temperature at different depths (−10; −20; −30; −40 cm) and the air temperatures (gray line) from June to August 2023.</p>
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<p>(<b>A</b>) Water table level from March to December 2023 in the 3 piezometers P1, P2, and P3, and daily precipitation (gray line); (<b>B</b>) groundwater TDS from March to December 2023 in the 3 piezometers P1, P2, and P3, and daily precipitation (gray line).</p>
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<p>Na<sup>+</sup> (blue line), Cl<sup>−</sup> (red line), and SO<sub>4</sub><sup>2−</sup> (green line) wet deposition fluxes from March to December 2023. The dashed black line represents the critical level of airborne salinity for Cl<sup>−</sup> following the international standard ISO 9223:2012.</p>
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<p>(<b>A</b>) NDMI and NDVI images and pixel frequency histograms of NDMI and NDVI pre-storm (10/2023); (<b>B</b>) NDMI and NDVI images and pixel frequency histograms of NDMI and NDVI post-storm (11/2023).</p>
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18 pages, 18205 KiB  
Article
Interpreting Soft-Sediment Deformation Structures: Insights into Earthquake History and Depositional Processes in the Dead Sea, Jordan
by Bety S. Al-Saqarat, Mahmoud Abbas, Mu’ayyad Al Hseinat, Tala Amer Qutishat, Duha Shammar and Ehab AlShamaileh
Geosciences 2024, 14(8), 217; https://doi.org/10.3390/geosciences14080217 - 16 Aug 2024
Cited by 1 | Viewed by 1915
Abstract
Soft-sediment deformation structures (SSDSs) typically form in unconsolidated sedimentary deposits before lithification. Understanding these structures involves evaluating their characteristics, genesis timing, and the dynamics of sediment deformation. SSDSs are essential for deciphering ancient environments, reconstructing depositional processes, and discerning past prevailing conditions. In [...] Read more.
Soft-sediment deformation structures (SSDSs) typically form in unconsolidated sedimentary deposits before lithification. Understanding these structures involves evaluating their characteristics, genesis timing, and the dynamics of sediment deformation. SSDSs are essential for deciphering ancient environments, reconstructing depositional processes, and discerning past prevailing conditions. In the Dead Sea region, SSDSs are abundant and well preserved due to unique geological and environmental factors, including rapid sedimentation rates and seismic activity. Influenced by the Dead Sea Transform Fault, the area offers insights into tectonic activity and historical earthquakes predating modern instrumentation. This study extensively examines SSDSs along the Dead Sea area in Jordan, focusing on sediments near the Lisan Peninsula, where the prominent Lisan Formation (71–12 ka) exposes numerous deformations. Mineralogical and geochemical analyses using X-ray diffraction (XRD) and X-ray fluorescence (XRF) were applied on deformed and undeformed layers to test the potential trigger of seismite formation in the Dead Sea area. The XRD and XRF results reveal Aragonite and Halite as the predominant compounds. Field observations, coupled with mineralogical and geochemical data, suggest tectonic activity as the primary driver of SSDSs formation in the Dead Sea region. Other contributing factors, such as high salinity, arid climate, and depositional settings, may also have influenced their formation. These structures offer valuable insights into the region’s geological history, environmental conditions, and tectonic evolution. Full article
(This article belongs to the Section Sedimentology, Stratigraphy and Palaeontology)
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<p>(<b>A</b>) Location map of Jordan (dashed red line) exhibits the major structural elements, including the DSTF segments (based on [<a href="#B31-geosciences-14-00217" class="html-bibr">31</a>,<a href="#B32-geosciences-14-00217" class="html-bibr">32</a>]). The plate tectonic configuration of the region is modified from Stern and Johnson [<a href="#B33-geosciences-14-00217" class="html-bibr">33</a>]. DSF: Dead Sea Fault; JVF: Jordan Valley Fault; WAF: Wadi Araba Fault [<a href="#B31-geosciences-14-00217" class="html-bibr">31</a>,<a href="#B32-geosciences-14-00217" class="html-bibr">32</a>,<a href="#B33-geosciences-14-00217" class="html-bibr">33</a>]. (<b>B</b>) Satellite image showing the locations of the nineteen studied outcrops of SSDSs in the Dead Sea area.</p>
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<p>Stratigraphic column of the Lisan Formation in central Jordan (modified after Abed and Yagan [<a href="#B57-geosciences-14-00217" class="html-bibr">57</a>].</p>
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<p>(<b>A</b>) Two deformed beds with southward verging slumps of variable size (31°14′6.1″ N, 35°31′12.20″ E; site 15). (<b>B</b>) One of the deformed layers with westward verging slumps (31°14′6.10″ N, 35°31′12.20″ E; site 11). (<b>C</b>) Deformed layer between two undeformed layers (31°13′38.70″ N, 35°31′6.80″ E; site 15). (<b>D</b>) Two deformed layers separated by undeformed layer (31°14′5.03″ N, 35°31′14.97″ E; site 13). (<b>E</b>) Four deformed beds with slumps and with different thicknesses and wavelengths (31°14′5.03″ N, 35°31′14.97″ E; site 13). The length of the hammer is 41 cm, the pen is 11 cm, and the shovel is 22 cm.</p>
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<p>(<b>A</b>) Mixed layer contains diapir fragments of marl mixed with sandy layer (31°6′1.60″ N, 35°31′36.10″ E; site 4). (<b>B</b>) Mixed layer showing fragments of course- and fine-grained sediments (31°6′1.60″ N, 35°31′36.10″ E; site 4). The length of the hammer is 41 cm and of the Silva compass is 17 cm.</p>
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<p>(<b>A</b>) Neptunian dyke: the arrow indicates the direction of sediments flow from the top to the bottom as evidenced by decreased width of the dyke with depth (31°13′34.60″ N, 35°31′15.40″ E; site 10). (<b>B</b>) Injection dyke: the arrow indicates the direction of the flow of liquified sediments from bottom to the top of the stratigraphic section (31°14′6.10″ N, 35°31′12.20″ E; site 15). The length of the hammer is 33 cm.</p>
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<p>(<b>A</b>) Load and flame structures (31°6′4.10″ N, 35°31′37.80″ E; site 3). (<b>B</b>) Load structure (31°6′4.10″ N, 35°31′37.80″ E; site 3). The length of the hammer is 41 cm and of the pen is 14 cm.</p>
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<p>Folded laminated marl that incorporates a cap layer of fine to medium-grained sandstone (31°13′34.60″ N, 35°31′15.40″ E; site 10). The length of the pen is 14 cm.</p>
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<p>(<b>A</b>) XRD results for the major minerals in the undeformed layer (DST1). (<b>B</b>) XRD results for the major minerals in the deformed layer (DST2).</p>
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<p>(<b>A</b>) Thin section reveals 10× zoom to aragonite crystals in undeformed layer. (<b>B</b>) Thin section 10× zoom of aragonite crystals in deformed layer.</p>
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<p>(<b>A</b>) A transtensional fault system comprising several normal faults that intersect the Quaternary deposits (the Lisan Formation), forming a negative-flower structure. (<b>B</b>–<b>D</b>) (31°5′44.20″ N, 35°31′31.00″ E; site 1) and (31°13′35.10″ N, 35°31′28.00″ E; site 8).</p>
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<p>(<b>A</b>) Rose diagram showing the faults measurements and indicating the correspondence with the DSTF and the Karak Wadi Al Fayha Fault. (<b>B</b>) Cyclographs and poles (dots) of the faults (dots).</p>
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14 pages, 14651 KiB  
Article
Effects of Fallen Posidonia Oceanica Seagrass Leaves on Wave Energy at Sandy Beaches
by Ogan Sevim and Emre N. Otay
Water 2024, 16(16), 2261; https://doi.org/10.3390/w16162261 - 11 Aug 2024
Cited by 1 | Viewed by 946
Abstract
Posidonia Oceanica (PO) is an endemic marine plant in the Mediterranean Sea. In an experimental study conducted in the Eastern Mediterranean, the effects of natural PO leaves on reducing the height of incident waves impacting a beach were measured. The transmission coefficient ( [...] Read more.
Posidonia Oceanica (PO) is an endemic marine plant in the Mediterranean Sea. In an experimental study conducted in the Eastern Mediterranean, the effects of natural PO leaves on reducing the height of incident waves impacting a beach were measured. The transmission coefficient (Kt) was found to vary between 0.73 and 0.94, which is equivalent to a wave height decay of 6–27%. The results show that in their natural environment, free-floating dead PO leaves dissipate incoming wave energy and have the capacity to protect beaches against erosion. Further analysis in separate frequency bands showed that waves with periods between 4.5–6.2 s were more sensitive to PO leaves in terms of energy dissipation. The transmission coefficient for medium-period waves, calculated using the medium-frequency part of the wave spectrum, delivered a maximum transmission coefficient of 0.5, corresponding to a 50% decay in wave height due to PO leaves. Full article
(This article belongs to the Special Issue Hydrodynamics and Sediment Transport in Ocean Engineering)
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Figure 1

Figure 1
<p>(<b>a</b>) Free-floating dead PO leaves in a wave breaking at Fugla Beach (photo taken in May 2022). (<b>b</b>) Dead PO leaves deposited along Damlatas Beach (photo taken in February 2024).</p>
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<p>Site location and measurement device locations.</p>
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<p>Schematic of test setup.</p>
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<p>(<b>a</b>) ADCP deployment at the sea bottom. (<b>b</b>) Test setup and device locations.</p>
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<p>(<b>a</b>) Bathymetric plan and (<b>b</b>) cross-section of the testing zone.</p>
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<p>Source of PO leaves and testing location.</p>
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<p>Aerial image of the testing site after completion of the tests.</p>
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<p>Measurement of wave periods using video frames of drone imaging. Red and blue lines indicate the crests of two consecutive waves.</p>
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<p>Wave crest distortion due to PO leaves. Blue line indicates undistorted wave crest, whereas the arrow indicates the direction of the wave.</p>
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<p>Comparison of wave spectra measured at three gauges during initial and final stages of PO release: (<b>a</b>) 12:09 PM; (<b>b</b>) 12:31 PM; (<b>c</b>) 15:09 PM; (<b>d</b>) 15:31 PM.</p>
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<p>(<b>a</b>) Significant wave height and mean sea level variations at different times and stations. (<b>b</b>) Peak and mean wave period variations.</p>
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<p>Transmission coefficients between gauges.</p>
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<p>Transmission coefficients with respect to the amount of PO leaves within the test area.</p>
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<p>Transmission coefficients of different wave periods: (<b>a</b>) long-period waves (6.2–9.8 s); (<b>b</b>) medium-period waves (4.5–6.2 s); (<b>c</b>) short-period waves (3.4–4.5 s).</p>
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<p>Transmission coefficients between PG2 and PG1 for different wave periods.</p>
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