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15 pages, 3623 KiB  
Article
Preliminary Exploration on Short-Term Prediction of Local Geomagnetically Induced Currents Using Hybrid Neural Networks
by Yihao Fang, Jin Liu, Haiyang Jiang and Wenhao Chen
Processes 2025, 13(1), 76; https://doi.org/10.3390/pr13010076 - 1 Jan 2025
Viewed by 444
Abstract
During extreme space weather events, transient geomagnetic disturbances initiated by solar eruptive activities can induce geomagnetically induced currents (GICs), which have severe impacts on power grid systems and oil/gas pipelines. Observations indicate that GICs in power grids are characterized by large fluctuation amplitudes, [...] Read more.
During extreme space weather events, transient geomagnetic disturbances initiated by solar eruptive activities can induce geomagnetically induced currents (GICs), which have severe impacts on power grid systems and oil/gas pipelines. Observations indicate that GICs in power grids are characterized by large fluctuation amplitudes, broad frequency ranges, and significant randomness. Their behavior is influenced by several factors, including the sources of space weather disturbance, Earth’s electrical conductivity distribution, the structural integrity and performance of power grid equipment, and so on. This paper presents a hybrid prediction using actual GIC data from power grids and deep learning techniques. We employ various technical methods, including complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms, to investigate short-term prediction methods for local grid GICs. The study uses GIC monitoring samples from 8 November 2004 for model training and testing. The results are evaluated using the coefficient of determination R2, root mean square error (RMSE), and mean absolute error (MAE). Preliminary research suggests that the combined CEEMDAN–CNN–LSTM–attention model significantly improves prediction accuracy and reduces the time delay in GIC prediction during geomagnetic storms compared to using LSTM neural networks alone. Full article
(This article belongs to the Section Energy Systems)
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Figure 1

Figure 1
<p>The structure of the convolutional neural network.</p>
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<p>Structure of long short term memory neural network.</p>
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<p>Structure of attention mechanism.</p>
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<p>Structure of CEEMDAN–CNN–LSTM–attention model.</p>
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<p>Partition of GIC training and testing sets.</p>
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<p>GIC decomposition results based on CEEMDAN.</p>
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<p>CEEMDAN decomposition of the predicted values of each IMF component and R component compared with the original data.</p>
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<p>GIC prediction results based on different models. (<b>a</b>) Prediction results based on LSTM model; (<b>b</b>) Prediction results based on CNN-LSTM-Attention model; (<b>c</b>) Prediction results based on CEEMDAN-LSTM model; (<b>d</b>) Prediction results based on CEEMDAN-CNN-LSTM-Attention model.</p>
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<p>Prediction results of GIC during storms under different models.</p>
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10 pages, 4804 KiB  
Case Report
A Case of Acquired Reactive Perforating Dermatosis with Complete Resolution of Eruptions on Upper and Lower Limbs During the Treatment of Diabetes Mellitus and Peripheral Artery Disease
by Yoshihito Mima, Tsutomu Ohtsuka, Ippei Ebato, Ryosuke Nishie, Satoshi Uesugi, Makoto Sumi, Yoshimasa Nakazato and Yuta Norimatsu
Medicina 2025, 61(1), 36; https://doi.org/10.3390/medicina61010036 - 29 Dec 2024
Viewed by 477
Abstract
Acquired reactive perforating dermatosis (ARPD) is characterized by its onset after the age of 18 years, umbilicated papules or nodules with a central keratotic plug, and the presence of necrotic collagen tissue within an epithelial crater. ARPD is strongly associated with systemic diseases [...] Read more.
Acquired reactive perforating dermatosis (ARPD) is characterized by its onset after the age of 18 years, umbilicated papules or nodules with a central keratotic plug, and the presence of necrotic collagen tissue within an epithelial crater. ARPD is strongly associated with systemic diseases such as diabetes mellitus (DM) and chronic renal failure, which may contribute to ARPD through factors including microcirculatory disturbances and the deposition of metabolic byproducts, including advanced glycation end-products and calcium. Here, we report a case of ARPD that improved following DM treatment and catheter-based interventions for peripheral artery disease (PAD). The eruptions on the upper limbs significantly improved with DM management. On the other hand, lesions on the lower limbs showed marked improvement after the enhancement in arterial blood flow due to catheter surgeries, along with DM treatment. Although a few reports of ARPD improving with DM management exist, our case underscores the importance of adequate DM control in ARPD management. The inability to perform the biopsy of the lesions on the lower limbs is our limitation; however, these lesions, similar to those on the upper limbs, presented with a central keratotic plug and re-epithelialized without forming ulcers or erosions, suggesting they were also related to ARPD. To date, there has been little discussion on the relationship between blood flow impairment in major vessels and ARPD. However, hypertension and venous circulatory dysfunctions are considered to lead to ARPD, raising the possibility that PAD-induced microvascular disturbances might have facilitated lesion formation in the present case. Further accumulation of cases and research is needed to clarify the relationship between blood flow impairment in major vessels and ARPD. Full article
(This article belongs to the Section Dermatology)
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Figure 1

Figure 1
<p>(<b>a</b>,<b>b</b>): Several papules and nodules on upper limbs, ranging from rice to bean size, accompanied by sebaceous plugs in the center (<b>a</b>). Swelling in the lower left leg, a black scab on the left index finger, and similar papules and nodules on the lower limbs (<b>b</b>).</p>
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<p>(<b>a</b>,<b>b</b>): Histopathological examination from upper extremity lesions revealed a cup-shaped defect, which contained keratin with parakeratosis, inflammatory cells, and necrotic material ((<b>a</b>): Hematoxylin and eosin stain ×40). Collagen fibers, vertically extruded from the superficial dermis, were also observed ((<b>b</b>): Elastica van Gieson staining ×100).</p>
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<p>Contrast computed tomography revealed arterial narrowing and ischemia of the left common iliac artery and superficial femoral artery in the left lower limb (yellow arrow).</p>
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<p>(<b>a</b>,<b>b</b>): Papules and nodules on the upper limbs remarkably improved after 2 months of diabetic treatment, whereas lesions on the lower limb remained unchanged.</p>
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<p>(<b>a</b>–<b>c</b>): Catheter angiography revealed the insertion of a stent into the left common iliac artery (yellow arrow) (<b>a</b>). Catheter angiography revealed balloon angioplasty performed on the left superficial femoral artery (yellow arrow) (<b>b</b>). Catheter angiography revealed enhanced blood flow in the left superficial femoral artery after endovascular therapy (yellow arrow) (<b>c</b>).</p>
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<p>(<b>a</b>,<b>b</b>): The eruptions closer to the center of the lower leg showed complete regression 3 months post-endovascular therapy; however, the erythematous area with an internal large crust still left the crust inside (<b>a</b>). Eruptions on the lower limbs became almost flatly pigmented 6 months post-operation (<b>b</b>).</p>
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<p>A table depicting the timeline and progression of the onset of DM, PAD, and skin lesions on the upper and lower limbs.</p>
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16 pages, 10249 KiB  
Article
Early Vegetation Recovery After the 2008–2009 Explosive Eruption of the Chaitén Volcano, Chile
by Ricardo Moreno-Gonzalez, Iván A. Díaz, Duncan A. Christie and Antonio Lara
Diversity 2025, 17(1), 14; https://doi.org/10.3390/d17010014 - 26 Dec 2024
Viewed by 338
Abstract
In May 2008, Chaitén volcano entered an eruptive process, leading to one of the world’s largest eruptions in recent decades. The magnitude of tephra ejected by the eruption left different types of disturbances and caused diverse forms of environmental damage that were heterogeneously [...] Read more.
In May 2008, Chaitén volcano entered an eruptive process, leading to one of the world’s largest eruptions in recent decades. The magnitude of tephra ejected by the eruption left different types of disturbances and caused diverse forms of environmental damage that were heterogeneously distributed across the surrounding area. We went to the field to assess the early vegetation responses a year after the eruption in September 2009. We evaluated the lateral-blast disturbance zone. We distributed a set of plots in three disturbed sites and one in an undisturbed site. In each of these sites, in a rectangular plot of 1000 m2, we marked all standing trees, recording whether they were alive, resprouting, or dead. Additionally, in each site of 80 small plots (~4 m2), we tallied the regenerated plants, their coverage, and the log volume. We described whether the plant regeneration was occurring on a mineral or organic substrate (i.e., ash or leaf litter, respectively). In the blast zone, the eruption created a gradient of disturbance. Close to the crater, we found high levels of devastation marked by no surviving species, scarcely standing-dead trees and logs, and no tree regeneration. At the other extreme end of the disturbance zone, the trees with damaged crowns were resprouting, small plants were regrowing, and seedlings were more dispersed. The main form of regeneration was the resprouting of trunks or buried roots; additionally, a few seedlings were observed in the small plots and elsewhere in disturbed areas. The results suggest that the early stages of succession are shaped by life history traits like dispersion syndrome and regeneration strategy (i.e., vegetative), as was found after other volcanic eruptions. Likewise, the distribution of biological legacies, which is related to disturbance intensity, can cause certain species traits to thrive. For instance, in the blow-down zone, surviving species were chiefly those dispersed by the wind, while in the standing-dead zone, survivors were those dispersed by frugivorous birds. Additionally, we suggest that disturbance intensity variations are related to the elevation gradient. The varying intensities of disturbance further contribute to these ecological dynamics. The early succession in the blast zone of Chaitén volcano is influenced by the interaction between species-specific life history, altitudinal gradient, and biological legacies. Further studies are required to observe the current successional patterns that occur directly in the blast zone and compare these results with those obtained following other volcanic disturbances. Full article
(This article belongs to the Special Issue Plant Succession and Vegetation Dynamics)
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Figure 1
<p>Map of the study site. (<b>Left panel</b>) indicates the position of Chaiten Volcano (red triangle) in relation to the main cities (black dots). (<b>Right panel</b>) shows a close-up of the volcano’s blast zone, the plots’ distribution along the disturbance/elevation gradient (total-destruction plot, blow-down plot, and standing-dead plot), and the undisturbed sector (old-growth plot).</p>
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<p>Photographic records of the study site. (<b>A</b>) showing impressive impacts close to the crater. Comparison of the volcanic dome and vegetation cover before (<b>B</b>) and during the 2008–2009 eruption (<b>C</b>). Red dot indicates the approximate position of the researcher (Dr. Díaz) in panel (<b>A</b>). Images (<b>D</b>–<b>G</b>) represent a disturbance condition where plots were established: (<b>D</b>) Total-destruction, (<b>E</b>) blow-down, (<b>F</b>) standing-dead, and (<b>G</b>) old-growth plots.</p>
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<p>Rarefaction analysis shows the relationship between the number of tree seedling species (<b>A</b>) and small vascular plant species (<b>B</b>) as a function of the number of individuals. In both panels, the shaded area indicates one standard error. All sites affected by the eruption of Chaitén old-growth, standing-dead, and blow-down plots are compared, except for the total-destruction plot, which was not included because of the absence of plants.</p>
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<p>Diameter at the breast height (dbh) distribution for living and standing-dead trees in the study sites. Each panel represents a plot condition influenced by the eruption of Chaitén Volcano.</p>
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<p>Volume of fallen dead trees (logs) in the study sites influenced by the eruption of Chaitén Volcano in the study sites.</p>
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<p>Number of individuals or colonies per relative coverage range at the ground level of the study areas affected by Chaitén Volcano, including vascular and non-vascular species. Panels correspond to the plots distributed in the different disturbance zones.</p>
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26 pages, 22432 KiB  
Article
On the Emergence of the Castellieri Settlements and Possible Effects of Climatic Changes in the 2nd Millennium BC in the Adriatic Region
by Anja Hellmuth Kramberger
Quaternary 2024, 7(4), 56; https://doi.org/10.3390/quat7040056 - 11 Dec 2024
Viewed by 829
Abstract
The fortified hilltop settlement of Monkodonja, located near Rovinj on the west coast of Istria, Croatia, provides insight into Bronze Age occupation and conflict in the Adriatic region. Established around 2000 BC, as evidenced by a series of C14 dates from human and [...] Read more.
The fortified hilltop settlement of Monkodonja, located near Rovinj on the west coast of Istria, Croatia, provides insight into Bronze Age occupation and conflict in the Adriatic region. Established around 2000 BC, as evidenced by a series of C14 dates from human and animal bones, the settlement experienced significant construction phases, particularly in its defensive architecture. Its earliest fortifications, built with limestone blocks using dry-stone wall techniques, date to the 19th century BC, with major expansions in the 16th century BC, where the primary wall was doubled in width and reached over 3 m in thickness. Monkodonja’s architectural complexity, notably the West Gate and Acropolis fortifications, and certain types of artifacts reveal influences from southern regions such the eastern Aegean. However, the settlement appears to have met a violent end around the 15th century BC, suggested by destruction layers, widespread burning, and the presence of weapons such as a lance tip, bronze axe, and slingstones. Monkodonja’s destruction raises questions about broader military conflicts in the Adriatic region during this period. Possible causes could include localized warfare or connections to larger-scale disturbances. Research in Monkodonja is also significant in the context of the debate surrounding the emergence of the so-called Castellieri settlements in Istria at the beginning of the 2nd millennium BC. As early as the beginning of the 20th century, it was proposed that a migration of people to the Istrian peninsula brought this new settlement form and other influences, leading to a significant population increase. The appearance of the Castellieri settlement form coincides with a period marked by documented climatic changes and two major natural disasters in the form of volcanic eruptions. Full article
(This article belongs to the Special Issue Advances in Geoarchaeology and Cultural Heritage)
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Figure 1
<p>The Adriatic region showing the course of sea currents and the location of the Istrian peninsula (graphic: author, data from [<a href="#B1-quaternary-07-00056" class="html-bibr">1</a>] (Figure 14)).</p>
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<p>Aerial view showing the fortified hilltop settlement of Monbrodo, where the concentric walls are covered by the vegetation canopy and clearly stand out in the terrain, Istria, Croatia (Adapted from [<a href="#B13-quaternary-07-00056" class="html-bibr">13</a>] (Figure 3a)).</p>
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<p>Lidar view of Monbrodo (Adapted from [<a href="#B14-quaternary-07-00056" class="html-bibr">14</a>] (Figure 2)).</p>
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<p>Upper: (<b>a</b>,<b>b</b>) Ceramic tripods from Monkodonja, Akropolis (Sonda 3), Istria, Croatia (drawings author), lower: tripods from the Eastern Mediterranean region, (<b>a</b>,<b>b</b>)–Ayia Irini, Kea (Adapted from [<a href="#B31-quaternary-07-00056" class="html-bibr">31</a>]), (<b>c</b>)–Cyprus (Adapted from [<a href="#B32-quaternary-07-00056" class="html-bibr">32</a>] (p. 50)).</p>
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<p>Aerial view of the fortified hilltop settlement of Monkodonja, with the Adriatic coast visible in the background, Istria, Croatia (Adapted from [<a href="#B33-quaternary-07-00056" class="html-bibr">33</a>] (Figure 1)).</p>
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<p>Aerial view showing the excavated area of the main fortification with the west gate of Monkodonja (<b>b</b>) and the various expansion phases of the wall and gateway (<b>a</b>), Istria, Croatia (Adapted from [<a href="#B33-quaternary-07-00056" class="html-bibr">33</a>] (Figure 4)).</p>
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<p>(<b>a</b>,<b>b</b>): Calibrated C14 dates from the 2005 and 2006 measurements of human and animal bone finds from the fortified hilltop settlement of Monkodonja, Istria, Croatia, Leibniz Laboratory for Age Determination and Isotope Research Kiel (Adapted from [<a href="#B49-quaternary-07-00056" class="html-bibr">49</a>] (p. 38)).</p>
Full article ">Figure 7 Cont.
<p>(<b>a</b>,<b>b</b>): Calibrated C14 dates from the 2005 and 2006 measurements of human and animal bone finds from the fortified hilltop settlement of Monkodonja, Istria, Croatia, Leibniz Laboratory for Age Determination and Isotope Research Kiel (Adapted from [<a href="#B49-quaternary-07-00056" class="html-bibr">49</a>] (p. 38)).</p>
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<p>Compilation of all calibrated C14 dates from the burial mounds on Mušego, Istria, Croatia, based on human bones from the Poznan Radiocarbon Laboratory (Poz) and the Leibniz Laboratory for Radiometric Dating and Isotope Research Kiel (KIA) (Adapted from [<a href="#B48-quaternary-07-00056" class="html-bibr">48</a>] (Figure 4)).</p>
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<p>Compilation of all calibrated C14 dates from the burial mounds on Mušego, Istria, Croatia, based on human bones from the Poznan Radiocarbon Laboratory (Poz) and the Leibniz Laboratory for Radiometric Dating and Isotope Research Kiel (KIA) (Adapted from [<a href="#B48-quaternary-07-00056" class="html-bibr">48</a>] (Figure 4)).</p>
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<p>Aerial view showing the excavated area of the acropolis of Monkodonja, Istria, Croatia (Adapted from [<a href="#B33-quaternary-07-00056" class="html-bibr">33</a>] (Figure 5)).</p>
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<p>Bronze Age settlement system showing visual communication around the central settlement of Monkodonja, around the middle of the 2nd millennium BC, Istria, Croatia (graphic by the author, Data from [<a href="#B37-quaternary-07-00056" class="html-bibr">37</a>] (Figure 5) and [<a href="#B1-quaternary-07-00056" class="html-bibr">1</a>] Figure 17)).</p>
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11 pages, 1344 KiB  
Case Report
Eruption Disturbance in Children Receiving Bisphosphonates: Two Case Reports
by Tatsuya Akitomo, Yuko Iwamoto, Mariko Kametani, Ami Kaneki, Taku Nishimura, Chieko Mitsuhata and Ryota Nomura
Pharmaceuticals 2024, 17(11), 1521; https://doi.org/10.3390/ph17111521 - 12 Nov 2024
Viewed by 619
Abstract
Background: Bisphosphonates used for the treatment of osteoporosis, hypercalcemia, or heterotopic calcifications can cause serious adverse dental events such as osteonecrosis of the maxillary and mandibular bones. However, the effects in childhood remain scarcely explored. Case Presentations: We encountered two children who had [...] Read more.
Background: Bisphosphonates used for the treatment of osteoporosis, hypercalcemia, or heterotopic calcifications can cause serious adverse dental events such as osteonecrosis of the maxillary and mandibular bones. However, the effects in childhood remain scarcely explored. Case Presentations: We encountered two children who had started bisphosphonate therapy before completion of the primary dentition. No systemic disease causing congenital delayed tooth eruption was diagnosed. Although the children’s height and weight increased with age, their tooth eruption was significantly delayed compared with the mean. The primary teeth gradually erupted in the follow-up period; however, some teeth did not completely erupt and needed to be extracted to allow for permanent tooth eruption. Conclusions: We report a case of children with early use of bisphosphonates and eruption disturbance, highlighting the need for further investigation into the relationship between these factors. Full article
(This article belongs to the Section Pharmacology)
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Figure 1

Figure 1
<p>Panoramic radiographs of Case 1 revealing the diagnosis as delayed eruption of the maxillary primary second molars, and the location was not changed despite traction treatment. Images taken at (<b>A</b>) 4 years and 6 months, (<b>B</b>) 5 years and 3 months, and (<b>C</b>) 6 years and 6 months.</p>
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<p>Cone-beam computed tomography images of Case 1 obtained at 7 years 0 months showing eruption disturbance of the maxillary left permanent first molar. Sagittal section at maxillary right first molar (<b>A</b>) and left first molar (<b>B</b>). Three-dimensional construction images at right (<b>C</b>) and left (<b>D</b>).</p>
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<p>Images of Case 1 at the age of 12 years and 5 months showing the ankylosis of the mandibular right primary second molar. Intraoral photographs (<b>A</b>) and panoramic radiograph (<b>B</b>).</p>
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<p>Images of Case 2 at the age of 1 year and 2 months showing suspected delayed eruption of primary incisors. Intraoral photographs (<b>A</b>) and periapical radiographs (<b>B</b>).</p>
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<p>Images of Case 2 at the age of 4 years and 1 month revealing eruption disturbance of the primary second molars. Intraoral photographs (<b>A</b>) and panoramic radiograph (<b>B</b>).</p>
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18 pages, 14984 KiB  
Article
The Mother’s Day Solar Storm of 11 May 2024 and Its Effect on Earth’s Radiation Belts
by Viviane Pierrard, Alexandre Winant, Edith Botek and Maximilien Péters de Bonhome
Universe 2024, 10(10), 391; https://doi.org/10.3390/universe10100391 - 10 Oct 2024
Viewed by 1544
Abstract
The month of May 2024 was characterized by solar energetic particles events directed towards the Earth, especially the big event causing a strong terrestrial geomagnetic storm during the night from 10 to 11 May 2024, with auroras observed everywhere in Europe. This was [...] Read more.
The month of May 2024 was characterized by solar energetic particles events directed towards the Earth, especially the big event causing a strong terrestrial geomagnetic storm during the night from 10 to 11 May 2024, with auroras observed everywhere in Europe. This was the strongest storm for the last 20 years with a Disturbed Storm Time index Dst < −400 nT. In the present work, we show with observations of GOES, PROBA-V/EPT and MetOP/MEPED that this exceptional event was associated with the injection of energetic protons in the proton radiation belt, with important consequences for the South part of the South Atlantic Anomaly (SAA). In addition, the geomagnetic storm caused by the solar eruption has had tremendous impacts on the electron radiation belts. Indeed, we show that for 0.3 to 1 MeV electrons, the storm led to a long lasting four belts configuration which was not observed before with EPT launched in 2013, until a smaller geomagnetic storm took place at the end of June 2024. Moreover, for the first time since its launch, observations of the EPT show that ultra-relativistic electrons with E>2 MeV have been injected into the inner belt down to McIlwain parameter L = 2.4, violating the impenetrable barrier previously estimated to be located at L = 2.8. Full article
(This article belongs to the Section Space Science)
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Graphical abstract

Graphical abstract
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<p>Parameters of the solar wind at 1 AU and geomagnetic indices from OMNI from 1 May to 30 June 2024. <b>Top panel</b>: solar wind density (blue) and solar wind speed (red). <b>Second panel</b>: solar wind pressure (blue) and solar wind temperature (red). <b>Third panel</b>: Southward component of the interplanetary magnetic field <math display="inline"><semantics> <msub> <mi>B</mi> <mi>z</mi> </msub> </semantics></math>. <b>Bottom panel</b>: Dst index (blue) and Kp index multiplied by 10 (red).</p>
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<p>GOES observations of proton fluxes with energy &gt;10 MeV (blue), &gt;50 MeV (orange) and &gt;100 MeV (green) at the geostationary orbit from 1 May 2024 to 30 June 2024.</p>
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<p>Proton differential fluxes observed by PROBA-V/EPT from 1 May to 30 June 2024 as a function of the McIlwain parameter L (vertical axis) and time (horizontal axis) in the first 5 EPT proton energy channels. Fluxes are averaged in bins which are 6 h long in time and 0.25 in L. From <b>top</b> to <b>bottom</b>, the energy of each channel increases and they all share the same colorbar.</p>
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<p>Proton differential fluxes observed by MEPED from 1 May to 30 June 2024 as a function of the McIlwain parameter L (vertical axis) and time (horizontal axis) in 5th proton energy channel of MEPED. Fluxes are averaged in bins which are 6 h long in time and 0.25 in L. <b>Top panel</b>: 0° telescope, <b>Bottom panel</b>: 90° telescope.</p>
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<p>Proton fluxes observed in the first EPT proton channel averaged in longitude and latitude bins during four different periods covering most of the period considered in previous figures. The averaging bins here have a width of 10° in longitude and 5° in latitude. Each panel corresponds to a different period: (<b>a</b>) quiet conditions from 1 May to 9 May, (<b>b</b>) storm time and beginning of the recovery from 10 May to 20 May, (<b>c</b>) recovery period from 21 May to 31 May, (<b>d</b>) second proton injection from 1 June to 15 June.</p>
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<p>Observations of neutron monitors at different stations specified in <a href="#universe-10-00391-t001" class="html-table">Table 1</a>, located at all latitudes like Dourbes (Belgium, 50° lat) and SOPO (South Pole latitude <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>90</mn> </mrow> </semantics></math>°). The perturbation during the night of 10 to 11 May 2024 is well visible. The neutron decrease during the storm (Forbush decrease) is immediately followed by a Ground Level Enhancement.</p>
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<p>Electron differential fluxes observed from 1 May to 30 June 2024 as a function of the McIlwain parameter L (vertical axis) and time (horizontal axis) in the 6 EPT electron energy channels. Fluxes are averaged in bins which are 6 h long in time and 0.25 in L. From top to bottom, the energy of the channels increases and they all share the same color bar.</p>
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<p>EPT electron differential fluxes (in <math display="inline"><semantics> <mrow> <msup> <mi>MeV</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi>cm</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi>sr</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) as a function of time from 1 May to 30 June 2024 for all energy channels. Each panel shows the EPT flux at different <span class="html-italic">L</span> values. Plain lines correspond to the fluxes displayed in <a href="#universe-10-00391-f007" class="html-fig">Figure 7</a> and the dashed lines with similar color code correspond to the associated smoothed fluxes by sliding averages with a 4 days width.</p>
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<p>EPT smoothed electron differential fluxes as a function of L and time (color bar) in 3 different energy channels from 3 May to 27 June 2024. Grayed profiles correspond to the period between 8 and 10 May where the smoothed fluxes significantly deviate from the 6 h averaged fluxes.</p>
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<p>Same as <a href="#universe-10-00391-f010" class="html-fig">Figure 10</a> for MEPED 90° telescope for &gt;300 keV integral electron flux.</p>
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<p>Map of electron flux in Channel 1 (500–600 keV) observed from 1 to 10 May 2024 (before the storm, <b>top panel</b>) and from 11 to 20 May 2024 (after the storm, <b>bottom panel</b>). One can see the South Atlantic Anomaly (high fluxes) and its counterpart in the Northern hemisphere (lower fluxes), as well as the penetration of the outer belt at high latitudes.</p>
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<p>Distribution of electrons above 1 MeV as a function of the radial distance in Earth’s radii given by the NASA AE8-MAX model obtained using spenvis.oma.be. Inner and outer belts (in red), as well as the slot (in green), are well visible.</p>
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16 pages, 18068 KiB  
Article
Multi-Wave Structures of Traveling Ionospheric Disturbances Associated with the 2022 Tonga Volcanic Eruptions in the New Zealand and Australia Regions
by Xiaolin Li, Feng Ding, Bo Xiong, Ge Chen, Tian Mao, Qian Song and Changhao Yu
Remote Sens. 2024, 16(14), 2668; https://doi.org/10.3390/rs16142668 - 21 Jul 2024
Viewed by 930
Abstract
Using dense global navigation satellite system data and brightness temperature data across the New Zealand and Australia regions, we tracked the propagation of traveling ionospheric disturbances (TIDs) associated with the 15 January 2022 Tonga volcanic eruptions. We identified two shock wave-related TIDs and [...] Read more.
Using dense global navigation satellite system data and brightness temperature data across the New Zealand and Australia regions, we tracked the propagation of traveling ionospheric disturbances (TIDs) associated with the 15 January 2022 Tonga volcanic eruptions. We identified two shock wave-related TIDs and two Lamb wave-related TIDs following the eruptions. The two shock wave-related TIDs, propagating with velocities of 724–750 and 445–471 m/s, respectively, were observed around New Zealand and Australia within a distance of 3500–6500 km from the eruptive center. These shock wave-related TIDs suffered severe attenuation during the propagation and disappeared more than 6500 km from the eruptive center. Based on the TEC data from the nearest ground-based receivers, we estimated the onset times of two main volcanic explosions at 04:20:54 UT ± 116 s and 04:24:37 UT ± 141 s, respectively. The two shock wave-related TIDs were most likely generated by these two main volcanic eruptions. The two Lamb wave-related TIDs propagated with velocities of 300–370 and 250 m/s in the near-field region. The Lamb wave-related TIDs experienced minimal attenuation during their long-distance propagation, with only a 0.17% decrease observed in the relative amplitudes of the Lamb wave-related TIDs from the near-field to far-field regions. Full article
(This article belongs to the Special Issue Application of GNSS Remote Sensing in Ionosphere Monitoring)
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<p>GNSS receivers (blue and red dots) and observational slices (black lines) across the New Zealand and Australia regions. The solid regions along the slices (a–f) were selected to calculate the DTEC and brightness temperature keograms in this study. Red dots denote the GNSS stations used to estimate the onset times of the eruptions and capital letters correspond to their names. The green line represents the trajectory of IPPs. The blue and red solid triangles represent the IPPs when the TIDs arrived.</p>
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<p>Two-dimensional DTEC maps of the Australia and New Zealand regions. The presented times are in UT on 15 January 2022. DTEC data are expressed in TEC units (TECUs). Subfigures (<b>a</b>–<b>l</b>) show the snapshots of the DTEC map from 05:30 UT–11:30 UT.</p>
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<p>Relative DTEC keograms along the slices (<b>a</b>–<b>c</b>) and (<b>e</b>) solid black lines in <a href="#remotesensing-16-02668-f001" class="html-fig">Figure 1</a>. Relative DTEC data are expressed in percentages (%).</p>
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<p>Differential brightness temperature keograms along the slices (<b>c</b>–<b>f</b>) solid black lines in <a href="#remotesensing-16-02668-f001" class="html-fig">Figure 1</a>. Differential brightness temperature data are expressed in Kelvin (K).</p>
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<p>TEC series observed at GNSS stations SAMO, FTNA, USP1, and RAUL using satellites G10 (blue) and R21 (red) during the Tonga volcano eruptions. Red and blue dots denote the timings of the two major eruption events in the time series.</p>
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<p>Observed TEC series from satellites G18, G24, and R20 during the Tonga volcano eruptions. The responses at GNSS stations SAMO (blue) and FTNA (red) are shown for each satellite. Red and blue dots denote the timings of the two major eruption events in the time series.</p>
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<p>Relative DTEC keograms along the slices (<b>a</b>,<b>b</b>) in Figure 1 of Li et al. [<a href="#B18-remotesensing-16-02668" class="html-bibr">18</a>]. Dashed black lines represent the speeds of 350 and 250 m/s. Relative DTEC data are expressed in percentages (%).</p>
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29 pages, 9386 KiB  
Article
Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery
by Stefan Peters, Jixue Liu, Gunnar Keppel, Anna Wendleder and Peiliang Xu
Remote Sens. 2024, 16(10), 1722; https://doi.org/10.3390/rs16101722 - 13 May 2024
Cited by 4 | Viewed by 2284
Abstract
Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of [...] Read more.
Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management. Full article
(This article belongs to the Special Issue Natural Hazard Mapping with Google Earth Engine)
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Graphical abstract

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<p>Distribution of landslides recorded in the landslide inventories used as case studies in (<b>a</b>) Japan, (<b>b</b>) Haiti, (<b>c</b>) Papua New Guinea, and (<b>d</b>) New Zealand, and study areas implemented in this study. The distribution of coseismic (old and new) landslides based on available landslide inventory data is indicated.</p>
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<p>Conceptual overview of the ML-LaDeCORsat approach. The approach involves two principal steps: pre-processing (<b>top box</b>) and model training and evaluation (<b>bottom box</b>).</p>
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<p>Schematic of a landslide event that illustrates source and flow areas. Adapted from [<a href="#B1-remotesensing-16-01722" class="html-bibr">1</a>], figure 17.</p>
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<p>Effect of adding a buffer to slope filtering for the Japan case study. When mapping slope filtering (grey) on top of the landslide inventory (red) with (<b>a</b>): slope threshold &gt;10 degrees and (<b>b</b>) slope threshold &gt;10 degrees plus 100 m buffer, 24% and 99% of inventoried landslides are covered, respectively.</p>
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<p>Example of the training and validation approach implemented for the machine learning model. (<b>a</b>): Sampling areas with 10 degrees slope and 100 m buffer filter applied were identified as “landslide” inside (P<sub>area</sub>), “non-landslide” inside (N<sub>area</sub>), and another “non-landslide” inside N-B<sub>area</sub>. (<b>b</b>): randomly placed training and validation points within each sampling area were assigned at a ratio of 4:1.</p>
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<p>Comparison of accuracy assessments for ML-LaDeCORsat using various GEE ML classifiers: Classification and Regression Trees (GTB), Gradient Tree Boost (GTB), CART, Random Forest (RF), and Scalable Vector Machine (SVM).</p>
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<p>Comparison of GTB-based ML-LaDeCORsat (new) with existing landslide detection methods (M1–M15 as listed in <a href="#remotesensing-16-01722-t001" class="html-table">Table 1</a>) applied to all 4 case studies using balanced accuracy (<b>top</b>) and kappa (<b>bottom</b>) as performance indicators. M6: Unsupervised classification for Haiti, Papua New Guinea, and New Zealand reached computational limits in GEE; M12: P2 bands not available for Haiti, Papua New Guinea, and New Zealand.</p>
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<p>Landslide inventories, predicted landslides using ML-LaDeCORsat with the best-performing classifier, and error maps for Japan (<b>a</b>–<b>c</b>), Haiti (<b>d</b>–<b>f</b>), Papua New Guinea (<b>g</b>–<b>i</b>), and New Zealand (<b>j</b>–<b>l</b>).</p>
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<p>Importance of various landslide conditioning bands for ML-LaDeCORsat accuracy based on normalized sorted band importance for each study site using either (<b>a</b>) GTB, (<b>b</b>) CART, and (<b>c</b>) RF. Classifiers are sorted by their sum over all case studies and high values indicate high-importance factors.</p>
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<p>Across sites transfer learning and accuracies using the best-performing classifier (GTB) for ML-LaDeCORsat and “all pixel” validation. OA, BA, and recall metrics of accuracy are plotted against different training options: using all training samples from the target site (first position in each graph), all sites (second), all other sites without target site (third), and the “best performing” other sites plus a subset (50/40/30/20/10/5/1%) of the target site (all other positions). The “best performing” other sites have been identified by comparing detection accuracies for a target site using all possible combinations of across-domain training sites (e.g., training option for JPN target site: HTI only; PNG only; NZL only; HTI and PNG; PNG and NZL; HTI and NZL; all three).</p>
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10 pages, 2002 KiB  
Review
The Impact of the Hunga Tonga–Hunga Ha’apai Volcanic Eruption on the Stratospheric Environment
by Qian Sun, Taojun Lu, Dan Li and Jingyuan Xu
Atmosphere 2024, 15(4), 483; https://doi.org/10.3390/atmos15040483 - 13 Apr 2024
Cited by 1 | Viewed by 1887
Abstract
In this study, an overview of two years of research findings concerning the 2022 Hunga Tonga–Hunga Ha’apai (HTHH) volcanic eruption in the stratospheric environment is provided, focusing on water vapor, aerosols, and ozone. Additionally, the potential impacts of these changes on aviation equipment [...] Read more.
In this study, an overview of two years of research findings concerning the 2022 Hunga Tonga–Hunga Ha’apai (HTHH) volcanic eruption in the stratospheric environment is provided, focusing on water vapor, aerosols, and ozone. Additionally, the potential impacts of these changes on aviation equipment materials are discussed. The HTHH volcanic eruption released a large amount of particles (e.g., ash and ice) and gases (e.g., H2O, SO2, and HCl), significantly affecting the redistribution of stratospheric water vapor and aerosols. Stratospheric water vapor increased by approximately 140–150 Tg (8–10%), with a concentration peak observed in the height range of 22.2–27 km (38–17 hPa). Satellite measurements indicate that the HTHH volcano injected approximately 0.2–0.5 Tg of sulfur dioxide into the stratosphere, which was partially converted into sulfate aerosols. In-situ observations revealed that the volcanic aerosols exhibit hygroscopic characteristics, with particle sizes ranging from 0.22–0.42 μm under background conditions to 0.42–1.27 μm. The moist stratospheric conditions increased the aerosol surface area, inducing heterogeneous chlorine chemical reactions on the aerosol surface, resulting in stratospheric ozone depletion in the HTHH plume within one week. In addition, atmospheric disturbances and ionospheric disruptions triggered by volcanic eruptions may adversely affect aircraft and communication systems. Further research is required to understand the evolution of volcanic aerosols and the impact of volcanic activity on aviation equipment materials. Full article
(This article belongs to the Section Meteorology)
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<p>(<b>left</b>) MLS/Aura time series of mean (30° S–5° N) stratospheric water vapor anomalies (ppmv) from 100 to 1 hPa after the HTHH eruption (15 January 2022), from January–April 2022. The anomalies are calculated by subtracting the water vapor averaged 0–10 days prior to the eruption of the HTHH from the daily water vapor data. (<b>middle</b>) Zonal mean water vapor anomalies (ppmv) as a function of latitude and time at 38–17 hPa levels. The locations of the HTHH volcano and Lijiang are marked with a black plus symbol and a triangle, respectively. (<b>right</b>) Vertical profiles of water vapor at Lijiang on 9 April 2022, derived from in-situ observations (blue), and MLS remote sensing observations (black). The gray line shows the mean April water vapor with standard deviations derived from MLS during 2005–2020 (adapted from Xu et al., [<a href="#B19-atmosphere-15-00483" class="html-bibr">19</a>]).</p>
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<p>(<b>a</b>) Vertical profiles of the backscatter ratio (blue for 455 nm, red for 940 nm) data derived from the COBALD sonde and the effective radius (green) from the POPS measurements obtained in Lijiang on 9 April 2022. (<b>b</b>) Vertical profiles of the aerosol number density for bins of eight sizes from the POPS measurements obtained on 9 April 2022. (<b>c</b>) Profiles of water vapor on different days in Lijiang. (<b>d</b>) The European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) streamline data obtained at 30 hPa on 9 April 2022. The yellow dots mark the CALIPSO footprints when volcanic plumes (with altitudes of 22–25 km) were detected by an expedited level 2 vertical feature mask (VFM) algorithm on 7, 8, and 9 April. The asterisk marks the Lijiang site (from Figure 2 of Bian et al. [<a href="#B18-atmosphere-15-00483" class="html-bibr">18</a>]).</p>
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<p>Panels (top, bottom) show zonal and latitude band averages as a function of time for the CALIOP 532 nm scattering ratio (color) and MLS water vapor (contours, ppmv) for the 15–5° S and 25–15° S latitude bands, respectively (adapted from Legras et al. [<a href="#B23-atmosphere-15-00483" class="html-bibr">23</a>]).</p>
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<p>After the HTHH eruption, a balloon campaign took place at Reunion Island. Plume dynamics showcase the volcanic injection of H<sub>2</sub>O vapor, sulfur dioxide (SO<sub>2</sub>), and HCl, promoting rapid chlorine activation in hydrated volcanic aerosols and O3 depletion in the stratosphere. The 22 January 2022 O<sub>3</sub> profile (black line) contrasts with Reunion’s climatology (red line), displaying a notable decline (from the first figure of Evan et al. [<a href="#B34-atmosphere-15-00483" class="html-bibr">34</a>]).</p>
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13 pages, 3774 KiB  
Article
Morphometric Comparison and Prognostic Analysis of Permanent Maxillary Central Incisors with Eruption Disturbances—A Cross-Sectional Study
by Yuri Jeong, Jonghyun Shin, Soyoung Park, Taesung Jeong and Eungyung Lee
Children 2024, 11(3), 307; https://doi.org/10.3390/children11030307 - 5 Mar 2024
Viewed by 1074
Abstract
Aims: The aim of this study was to retrospectively compare the morphometrics of permanent maxillary central incisors with and without eruption disturbances, while simultaneously evaluating prognosis based on different factors. Materials and Methods: Seventy patients with unilateral permanent maxillary central incisor eruption disturbances [...] Read more.
Aims: The aim of this study was to retrospectively compare the morphometrics of permanent maxillary central incisors with and without eruption disturbances, while simultaneously evaluating prognosis based on different factors. Materials and Methods: Seventy patients with unilateral permanent maxillary central incisor eruption disturbances were included. Within a group of 70 subjects, measurements were taken for both normally erupted central incisors and central incisors with eruption disturbances to determine the length of the roots and the volume of the teeth. Various factors, such as angulation of impaction, and vertical height of impaction, were assessed to investigate their correlation with surgical intervention. Results: Both the root length and tooth volume were significantly smaller in the eruption disturbance incisors than in the normally erupted incisors (p ≤ 0.001). Moreover, there was a statistically significant increase in surgical intervention among cases with no clear physical barrier (primary retention) (p < 0.05) or when adjacent normally erupted central incisors exhibited more than 2/3 of root development (p < 0.05). Conclusions: The results of this study numerically demonstrated the delayed tooth development of the permanent maxillary central incisors with unilateral eruption disturbances compared to appropriately erupted incisors by measuring root length and tooth volume. The absence of obstacles and the degree of root development in adjacent erupted incisors might serve as factors for clinicians to determine the necessity and timing of surgical intervention. Full article
(This article belongs to the Special Issue 10th Anniversary of Children: Pediatric Dentistry Progress)
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<p>Demirjian’s stage assessment of N group. (<b>A</b>) Stage E (<b>B</b>) Stage F (<b>C</b>) Stage G (<b>D</b>) Stage H.</p>
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<p>Panoramic radiographs analysis. (<b>A</b>) Criteria for evaluating impaction height; coronal, middle, and apical were classified with reference to the contralateral erupted central incisor. (<b>B</b>,<b>C</b>) Criteria for evaluating impaction angle (mesial, distal); using the vertical midline passing through the ANS (anterior nasal spine) as a reference, the distance from this line to the midpoint of the incisal edge (b) was subtracted from the distance from this line to the midpoint of the root apex (a) of the impacted central incisor. A positive value indicated a mesial impaction, while a negative value indicated a distal impaction.</p>
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<p>Analysis of root length and tooth volume using Cone Beam Computed Tomography (CBCT). (<b>A</b>) Measurement of root length; the shortest perpendicular distance between the line passing through the cementoenamel junction (CEJ) of the tooth and the line connecting the most apical point of the root. (<b>B</b>) Measurement of tooth volume; N group and ED group were each segmented and their volumes were measured.</p>
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<p>Flow chart showing process of selecting subjects included in the study.</p>
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13 pages, 2984 KiB  
Communication
Mini-Implant Insertion Using a Guide Manufactured with Computer-Aided Design and Computer-Aided Manufacturing in an Adolescent Patient Suffering from Tooth Eruption Disturbance
by Christina Weismann, Kathrin Heise, Maite Aretxabaleta, Marcel Cetindis, Bernd Koos and Matthias C. Schulz
Bioengineering 2024, 11(1), 91; https://doi.org/10.3390/bioengineering11010091 - 18 Jan 2024
Cited by 3 | Viewed by 1686
Abstract
Due to dental diseases, anatomical restrictions, and mixed dentition, the reduction in the number of teeth and the displacement of tooth germs pose challenges in orthodontic treatment, limiting anchorage options. The presented case demonstrates an advanced treatment solution using digital CAD/CAM-technologies and medical [...] Read more.
Due to dental diseases, anatomical restrictions, and mixed dentition, the reduction in the number of teeth and the displacement of tooth germs pose challenges in orthodontic treatment, limiting anchorage options. The presented case demonstrates an advanced treatment solution using digital CAD/CAM-technologies and medical imaging for the creation of a mini-implant template. A 12-year-old male patient experiencing delayed tooth eruption, multiple impacted germs, and maxillary constriction underwent intraoral scanning and CBCT. Utilizing coDiagnostiXTM Version 10.2 software, the acquired data were merged to determine the mini-implant placement and to design the template. The template was then manufactured through stereolithography using surgical-guide material. Mini-implants were inserted using the produced appliance, enabling safe insertion by avoiding vital structures. Surgically exposed displaced teeth were aligned using a Hyrax screw appliance anchored on the mini-implants for rapid palatal expansion (RPE) and subsequently used as fixed orthodontics to align impacted teeth. The screw was activated daily for 10 weeks, resulting in a 7 mm posterior and 5 mm anterior maxillary transversal increase. Skeletal anchorage facilitated simultaneous RPE and tooth alignment, ensuring accuracy, patient safety, and appliance stability. The presented case shows a scenario in which computer-aided navigation for mini-implant positioning can enhance precision and versatility in challenging anatomical cases. Full article
(This article belongs to the Special Issue Recent Advances in the Treatment of Dental Diseases)
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<p>The images show a twelve-year-old patient showing a delayed tooth eruption of the permanent teeth, a disturbance in the eruption of the first molars, a displacement of multiple tooth germs, and a crowding, certainly of the canines and premolars in the maxilla. (<b>A</b>) Lateral, frontal, and top views of the maxillary and mandibular dental plaster casts of the patient at the time of initial presentation; (<b>B</b>) Panoramic radiograph revealing multiple dislocated tooth germs; (<b>C</b>) Lateral cephalogram depicting the skeletal growth pattern.</p>
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<p>Screenshot of the planning of the two palatal mini-implants using the coDiagnostiX<sup>TM</sup> (Dental Wings GmbH, Chemnitz, Germany). (<b>A</b>) The planned implant position in the coronal plane showing the vertical bone dimension of the hard palate. (<b>B</b>) Sagittal plane of the right implant ensuring a sufficient distance to the root of the inclined lateral incisor. (<b>C</b>) Transversal plane of the maxilla displaying the multiple retained tooth germs restricting the region for safe mini-implant insertion. (<b>D</b>) Three-dimensional depiction of the bone (brown), the intraoral maxillary soft tissue scan (green), and the final design of the drilling template (white). (<b>E</b>) Panoramic radiograph-like view of the case displaying the planned mini-implant positions.</p>
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<p>Placement of the customized drilling template on Preform software (Formlabs), considering the frontal (<b>A</b>) and posterior (<b>B</b>) views. The support structures were not placed in areas of higher-accuracy interest, such as the negative of the maxillary scan and holes of the implants.</p>
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<p>CAD/CAM-based implant drilling template procedure. (<b>A</b>) CAD/CAM manufactured tooth-borne template. (<b>B</b>) Three-dimensional-printed maxillary model. (<b>C</b>) In vitro check of the fit of the custom-made template on maxillary model. (<b>D</b>) Intra-operative placement of the template in the maxilla. The incisal fenestration on teeth 11 and 21 enables an intra-operative fit check of the template. (<b>E</b>) Intra-oral view following successful guided placement of the mini-implants.</p>
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<p>Intraoral pictures depicting the increase in the transversal maxillary dimension by the combination of the mini-implants and the Hyrax screw at different time points: (<b>A</b>) before activation, (<b>B</b>) after four weeks of the activation period, and (<b>C</b>) after ten weeks of activation.</p>
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<p>Intraoral images depicting the modification of the Hyrax appliance for skeletal anchorage to align the impacted teeth: (<b>A</b>) immediately after the surgical exposure of the teeth (16, 13, 23, 26) with the Hyrax modification and sutures still in situ; (<b>B</b>) alignment of tooth 23 into the dental arch using a fixed orthodontic segment appliance; (<b>C</b>) continued alignment of the dental arch; (<b>D</b>) alignment of tooth 13 into the dental arch and the use of open coils in regions 15 and 25 attached to the fixed orthodontic appliance; (<b>E</b>) after the third surgical exposure and alignment into the dental arch using cantilevers of the teeth 15 and 25 with the Hyrax device as skeletal anchorage.</p>
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<p>The follow-up panoramic radiograph at the age of 13 years reveals the displaced germs of teeth 15 and 25, partially superimposed by the cantilever structure in the palatal area. Both mini-implants show no sign of peri-implant osteolysis.</p>
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15 pages, 1536 KiB  
Article
A Partial Eruption of a Sigmoid Filament in the Small Dipole Active Region 12734
by Jihong Liu, Yin Zhang, Yuhong Zheng, Yu Liu and Jie Chen
Universe 2024, 10(1), 42; https://doi.org/10.3390/universe10010042 - 16 Jan 2024
Viewed by 1406
Abstract
We present a detailed analysis of a partial eruption of a sigmoid filament lying along the polarity inversion line (PIL) of the small active region (AR) NOAA 12734 (with an area of 1.44 ×103 square megameters). The active filament was rooted [...] Read more.
We present a detailed analysis of a partial eruption of a sigmoid filament lying along the polarity inversion line (PIL) of the small active region (AR) NOAA 12734 (with an area of 1.44 ×103 square megameters). The active filament was rooted in a dipole sunspot of the AR. The eruption was associated with a C1.3 flare and subsequent large-scale coronal disturbances. During its solar disk passage before the flare, the AR had the following characteristics: (1) Most of the time, the magnetic field lines in the AR showed a sigmoidal structure (‘L1’) in the low corona and arc-shaped loops (i.e., ‘L2’) in the upper atmosphere. (2) An ‘X’-shaped structure was formed between the original ‘S’-shaped magnetic loop (‘L1’) and the newly rising one (‘L3’) between the main positive and negative magnetic polarities of the sunspots, and the intersection point of flux ropes ‘L1’ and ‘L3’ corresponds well with the area where the initial extreme-ultraviolet (EUV) 1600 Å brightening of the flare occurred. (3) The AR disobeyed the hemispherical helicity rule and had magnetic twist and writhe of the same signs, i.e., its magnetic helicity/current helicity were positive in the northern hemisphere. (4) Sustained magnetic emergence and cancellation occurred before the flare. Therefore, the magnetic reconnection of highly twisted helical flux ropes under the confinement of the overlying magnetic fields is probably responsible for the partial eruption of the filament. Full article
(This article belongs to the Special Issue Small-Scale Eruptions on the Sun)
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<p>Overview of AR 12734 at 03:00−03:05 UT, right before the GOES flare started. (<b>a</b>,<b>b</b>) HMI intensity gram and light-of-sight (LOS) magnetogram, respectively. (<b>c</b>,<b>d</b>) AIA 304 and 171 Å images, respectively. The red letters ‘P1’, ‘P2’, and ‘P3’ mark the main positive polarities, and the blue letters ‘N1’, ‘N2’, and ‘N3’ mark the negative ones. The green letters ‘L1’, ‘L2’, and ‘L3’ in panel (<b>d</b>) mark the magnetic loops between ‘N1’ and ‘P1’ (along the sigmoidal structure), ‘N1’ and ‘P3’, and ‘N1’ and ‘P1’ (along a straight line), separately. The red/blue contours in panel (<b>d</b>) mark the LOS field of 320 Gauss. A small 304/171 image is placed at the bottom-left corner in panels (<b>c</b>)/(<b>d</b>), with the field of view (FOV) as 150″ × 120″. The well-sigmoid-shaped filament in the small 304 image was taken at 03:00 UT, and the red/purple crosses in the small 171 image mark the loop ‘L1’/‘L3’.</p>
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<p>The temporal evolution of AR 12734 as shown in the AIA 171 Å and 131 Å images taken on 8 March 2019. The red/blue contours mark the LOS field of 320 Gauss. The green/yellow letters ‘L1’, ‘L2’, and ‘L3’ in panels (<b>a</b>)/(<b>e</b>) mark the magnetic loops similar to <a href="#universe-10-00042-f001" class="html-fig">Figure 1</a>. ‘Lf1’/‘Lf2’ in panels (<b>b</b>,<b>f</b>)/(<b>c</b>,<b>g</b>) mark the backflows of the partially erupted internal filament. Lf1 and Lf2 occurred 1 and 4 min after the two flaring peaks, separately. ‘Se’ in panel (<b>d</b>) marks the newly formed S-shaped filament channel about 4 h after the eruptive events. A similar sigmoidal filament can be seen in the AIA 304 Å image shown in panel (<b>h</b>). It is less twisted than before. ‘R1’ and ‘R2’ in panels (<b>b</b>,<b>d</b>) mark the region where the line of force rotates clockwise.</p>
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<p><b>Top</b>: The vector magnetic field of AR 12734. The blue and red arrows indicate the positive and negative transverse magnetic fields, respectively. The yellow solid contours mark the flaring polarity inversion line (FPIL). The green contours outline the area where the initial EUV 1600 Å brightening occurred for the flare. <b>Bottom</b>: The velocity distribution calculated using DAVE4VM overlaid on the grayscale map of the LOS magnetic field distribution. The FOV is 171″ × 98″.</p>
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<p>The dynamic evolution of the LOS magnetic field of AR 12734 in the core region, which is indicated by the dashed white box in <a href="#universe-10-00042-f003" class="html-fig">Figure 3</a>, top panel. The FOV is 61″ × 41″. The red/blue contours mark the LOS field of 1 Gauss and can represent the PIL. The white rectangles mark the areas with obvious magnetic emergence before the flare. Furthermore, apparent magnetic cancellation appears on both sides of the rectangles along the PIL, as indicated by the green arrows.</p>
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<p>The average alpha density, unsigned flux, writhe, and helicity accumulation as a function of time for AR 12734. The dashed line marks the first GOES flare maximum.</p>
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<p>NLFFF extrapolation from 8 March 2019 at 03:00 UT (7 min before the flare initiation). The red, purple, blue lines outline the low-lying magnetic flux ropes cospatial with ‘L1’, ‘L2’, and ‘L3’ marked in <a href="#universe-10-00042-f001" class="html-fig">Figure 1</a>d and <a href="#universe-10-00042-f002" class="html-fig">Figure 2</a>a. The yellow lines mark the large-scale overlying loops. The white letter ‘C’ marks the intersection point of ‘L1’ and ‘L3’ where the filament is lifted.</p>
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14 pages, 4987 KiB  
Article
GNSS/AQUA Fusion Study of Atmospheric Response Characteristics and Interaction Mechanisms during the 2022 Tonga Volcanic Eruption
by Lulu Ming, Fuyang Ke, Xiangxiang Hu, Wanganyin Cui and Pan Zhao
Atmosphere 2023, 14(11), 1619; https://doi.org/10.3390/atmos14111619 - 28 Oct 2023
Cited by 1 | Viewed by 1321
Abstract
A large-scale underwater volcanic eruption occurred at the volcano of Hunga Tonga-Hunga Ha’apai (HTHH) on 15 January 2022. At present, there is no consensus on the ionospheric response characteristics and interaction mechanism during volcanic eruptions. Based on the Global Navigation Satellite System (GNSS), [...] Read more.
A large-scale underwater volcanic eruption occurred at the volcano of Hunga Tonga-Hunga Ha’apai (HTHH) on 15 January 2022. At present, there is no consensus on the ionospheric response characteristics and interaction mechanism during volcanic eruptions. Based on the Global Navigation Satellite System (GNSS), AQUA satellite’s Atmospheric Infrared Sounder (AIRS), the experiment studies the response characteristics of the ionosphere and gravity waves during the eruption of the volcano and their interaction mechanisms and the International Real-Time Geomagnetic Observation Network (INTERMAGNET). First, a geomagnetic anomaly was detected before the eruption, which caused variations in the ionospheric VTEC (Vertical Total Electron Content) by about 15 TECU. Based on the IGS (International GNSS Service) observations, the VTEC distribution between 60° north and south latitudes was retrieved. The results show that before and after the eruption of Tonga Volcano, significant ionospheric anomalies were observed to the south, northwest and southwest of the volcano, with a maximum anomaly of 15 TECU. The study indicates that the geomagnetic anomaly disturbance is one of the precursors of volcanic eruption and has a certain degree of impact on the ionosphere. A correlation between geomagnetic anomalies and ionospheric anomalies was found to exist. The vast impact from the volcanic eruption excites gravity waves over the surface, which then propagate longitudinally, further perturbing the ionosphere. It is also detected that the ionospheric anomaly perturbation has a high coincidence effect with the gravity wave anomaly. Therefore, the gravity waves generated by atmospheric variations are used to explain the ionospheric perturbation phenomenon caused by volcanic eruptions. Full article
(This article belongs to the Special Issue Coupling between Plasmasphere and Upper Atmosphere)
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<p>The solar index F10.7, the Dst and Ap indexes of the geomagnetic activity from 1 to 31 January 2022.</p>
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<p>Global Positioning System (GPS) stations of IGS and Location of HTHH and API.</p>
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<p>The Variations of Geomagnetic Signal.</p>
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<p>Geomagnetic Variations anomalies with the total background geomagnetic intensity removed.</p>
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<p>The Variations of SO<sub>2</sub> on 15 January 2022.</p>
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<p>Brightness Temperature Variance at 4.3 Micron (Blue Line) and 15 Micron (High Altitudes) (Red Line) on 15 January 2022.</p>
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<p>Brightness Temperature Variance at 15 Micron (High Altitudes) on 14 January 2022 (<b>a</b>), 15 January 2022 (<b>b</b>) and 16 January 2022 (<b>c</b>).</p>
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<p>The <span class="html-italic">VTEC</span> in Three Different Cases.</p>
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<p>The Abnormal <span class="html-italic">VTEC</span> between 60 Degrees North and 60 Degrees South on 14 January 2022 and the black triangle represents the location of the volcano.</p>
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<p>The Abnormal <span class="html-italic">VTEC</span> between 60 Degrees North and 60 Degrees South on 15 January 2022 and the black triangle represents the location of the volcano.</p>
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<p>The Abnormal <span class="html-italic">VTEC</span> between 60 Degrees North and 60 Degrees South on 16 January 2022 and the black triangle represents the location of the volcano.</p>
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18 pages, 14118 KiB  
Article
Analysis of Ionospheric Anomalies before the Tonga Volcanic Eruption on 15 January 2022
by Jiandi Feng, Yunbin Yuan, Ting Zhang, Zhihao Zhang and Di Meng
Remote Sens. 2023, 15(19), 4879; https://doi.org/10.3390/rs15194879 - 9 Oct 2023
Cited by 11 | Viewed by 2391
Abstract
In this paper, GNSS stations’ observational data, global ionospheric maps (GIM) and the electron density of FORMOSAT-7/COSMIC-2 occultation are used to study ionospheric anomalies before the submarine volcanic eruption of Hunga Tonga–Hunga Ha’apai on 15 January 2022. (i) We detect the negative total [...] Read more.
In this paper, GNSS stations’ observational data, global ionospheric maps (GIM) and the electron density of FORMOSAT-7/COSMIC-2 occultation are used to study ionospheric anomalies before the submarine volcanic eruption of Hunga Tonga–Hunga Ha’apai on 15 January 2022. (i) We detect the negative total electron content (TEC) anomalies by three GNSS stations on 5 January before the volcanic eruption after excluding the influence of solar and geomagnetic disturbances and lower atmospheric forcing. The GIMs also detect the negative anomaly in the global ionospheric TEC only near the epicenter of the eruption on 5 January, with a maximum outlier exceeding 6 TECU. (ii) From 1 to 3 January (local time), the equatorial ionization anomaly (EIA) peak shifts significantly towards the Antarctic from afternoon to night. The equatorial ionization anomaly double peak decreases from 4 January, and the EIA double peak disappears and merges into a single peak on 7 January. Meanwhile, the diurnal maxima of TEC at TONG station decrease by nearly 10 TECU and only one diurnal maximum occurred on 4 January (i.e., 5 January of UT), but the significant ionospheric diurnal double-maxima (DDM) are observed on other dates. (iii) We find a maximum value exceeding NmF2 at an altitude of 100~130 km above the volcanic eruption on 5 January (i.e., a sporadic E layer), with an electron density of 7.5 × 105 el/cm3. Full article
(This article belongs to the Special Issue Ionosphere Monitoring with Remote Sensing II)
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<p>Location of volcanic eruption in Tonga (red pentagram) and distribution of GNSS stations (black triangle).</p>
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<p>Geomagnetic and solar activity before and after the volcanic eruption from 1 to 16 January 2022. The red vertical line indicates the time of volcanic eruption (same below).</p>
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<p>Variations in ionospheric TEC over TONG, LAUT and SAMO stations from 1 to 16 January 2022.</p>
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<p>Ionospheric TEC anomalies detected using the sliding interquartile range method over TONG, LAUT and SAMO stations from 1 to 16 January 2022.</p>
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<p>Ionospheric TEC variations (GPS-TEC and NeuralProphet-TEC) over TONG, LAUT and SAMO stations from 1 to 16 January 2022.</p>
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<p>Ionospheric TEC anomalies detected using NeuralProphet over TONG, LAUT and SAMO stations on 1 to 16 January 2022.</p>
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<p>Cross-wavelet transform of TEC time series and spatial weather parameters from 1 to 16 January 2022 at TONG station. The closed area of the thick black line passes the standard red noise test at 95% confidence level, indicating the significance of the period; the cone of influence (COI) area below the thin black solid line is the area of wavelet transform data with large edge effects, and the thick red line indicates the moment of volcanic eruption.</p>
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<p>Wavelet coherence spectrum of TEC time series with space weather parameters at TONG station from 1 to 16 January 2022.</p>
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<p>Distribution of the global ionospheric TEC anomaly on 5 January, from 02:00 to 07:00 UT. The red pentagram representing the location of the eruption center and the black solid line indicating the magnetic equator (same below).</p>
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<p>Distribution of global ionospheric TEC on 5 January, from 00:00 to 05:00 UT.</p>
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<p>Latitude–time–TEC variations extracted along the 175°W longitude line. The red line is the latitudinal position of the eruption.</p>
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<p>TEC time series of KOKB, TONG and CHTI stations from 1 to 8 January (local time), with the 1st peaks, 2nd peaks and valleys of ionospheric DDM indicated by red, magenta and blue dots, respectively.</p>
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<p>(<b>a</b>) Ionospheric electron density profile near the volcanic eruption detected by FOR-MOSAT-7/COSMIC-2 1st satellite on 5 January. The red pentagram is the location of the eruption, and the line segment in (<b>b</b>) is the tangent point trajectory of the satellite with GNSS satellite.</p>
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<p>(<b>a</b>) Ionospheric electron density profile near the volcanic eruption detected by FOR-MOSAT-7/COSMIC-2 2nd satellite on 5 January. The red pentagram is the location of the eruption, and the line segment in (<b>b</b>) is the tangent point trajectory of the satellite with GNSS satellite. Ionospheric electron density profile from 90 to 150 km is shown in (<b>c</b>).</p>
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<p>(<b>a</b>) Ionospheric electron density profile near the volcanic eruption detected by FOR-MOSAT-7/COSMIC-2 4th satellite on 5 January. The red pentagram is the location of the eruption, and the line segment in (<b>b</b>) is the tangent point trajectory of the satellite with GNSS satellite. Ionospheric electron density profile from 90 to 150 km is shown in (<b>c</b>).</p>
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<p>Neutral wind variations from 50 to 100 km above eruption simulated by the HWM14 model during 5–10 January 2022.</p>
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<p>The global distribution of Global Ultraviolet Imager (GUVI)-measured O/N2 ratio during 5–10 January 2022.</p>
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12 pages, 3145 KiB  
Article
Macroinvertebrate Response to Internal Nutrient Loading Increases in Shallow Eutrophic Lakes
by Kai Peng, Rui Dong, Boqiang Qin, Yongjiu Cai, Jianming Deng and Zhijun Gong
Biology 2023, 12(9), 1247; https://doi.org/10.3390/biology12091247 - 18 Sep 2023
Cited by 1 | Viewed by 2248
Abstract
In eutrophic lakes, even if external loading is controlled, internal nutrient loading delays the recovery of lake eutrophication. When the input of external pollutants is reduced, the dissolved oxygen environment at the sediment interface improves in a season without algal blooms. As an [...] Read more.
In eutrophic lakes, even if external loading is controlled, internal nutrient loading delays the recovery of lake eutrophication. When the input of external pollutants is reduced, the dissolved oxygen environment at the sediment interface improves in a season without algal blooms. As an important part of lake ecosystems, macroinvertebrates are sensitive to hypoxia caused by eutrophication; however, how this change affects macroinvertebrates is still unknown. In this study, we analysed the monitoring data of northern Lake Taihu from 2007 to 2019. After 2007, the external loading of Lake Taihu was relatively stable, but eutrophication began to intensify after 2013, and the nutrients in the sediments also began to decline, which was related to the efficient use of nutrients by algal blooms. The community structure and population density of macroinvertebrates showed different responses in different stages. In particular, the density of oligochaetes and the Shannon–Wiener index showed significant differences in their response to different stages, and their sensitivity to eutrophication was significantly reduced. Under eutrophication conditions dominated by internal loading, frequent hypoxia occurs at the sediment interface only when an algal bloom erupts. When there is no bloom, the probability of sediment hypoxia is significantly reduced under the disturbance of wind. Our results indicate that the current method for evaluating lake eutrophication based on oligochaetes and the Shannon–Wiener diversity index may lose its sensitivity. Full article
(This article belongs to the Section Ecology)
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<p>Study area and sampling sites in Lake Taihu.</p>
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<p>Changes in nutrient concentrations in the water column and sediment and chlorophyll concentrations in Lake Taihu from 2007 to 2019. (The data are converted to a power of 0.25, and the green range represents the 95% confidence interval. TN: total nitrogen, TP: total phosphorus Chla: chlorophyll, LOI: loss on ignition, TNs: sediment total nitrogen, TPs: sediment total phosphorus, TLI: trophic level index, % silt: percentage of silt).</p>
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<p>Changes in the abundance of macroinvertebrates for different classes in North Taihu from 2007 to 2019. (The data represents the average values from 9 sampling sites for each season and converted to a power of 0.25, and the green range represents the 95% confidence interval. (<b>a</b>–<b>f</b>) respectively represent the density change of Oligochaetes, Bivalvia, Gastropoda, Polychaeta, Crustacean, Chironomidae).</p>
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<p>Changes in the community structure of macroinvertebrates in Taihu in two stages and significant environmental factors affecting their changes. The line represents the annual average change in the sample site score in NMDS analyses. (Stage 1: 2007–2013, Stage 2: 2014–2019. TN: total nitrogen, TP: total phosphorus, LOI: loss on ignition, TNs: sediment total nitrogen, TPs: sediment total phosphorus, % silt: percentage of silt, WS: wind speed).</p>
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<p>Changes in the benthic diversity index of Taihu in different seasons from 2007 to 2019 and the number of times that the DO% was lower than 50% in the hourly DO high-frequency monitoring results. (No bottom dissolved oxygen value was less than 50% in February and November. The grey range represents the 95% confidence interval).</p>
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<p>The relationship between the density of all oligochaetes, the Shannon–Wiener index, and the TLI index in northern Lake Taihu in different periods. (The Oligochaeta density is converted to the fourth power of the root sign, and the grey range represents the 95% confidence interval).</p>
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<p>The dredging area and dredging quantity in Taihu from 2007 to 2018 (North Taihu Lake and East Taihu Lake are the main areas for ecological dredging).</p>
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<p>Differences in the response of macroinvertebrates to lake eutrophication dominated by external loading and internal loading.</p>
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