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15 pages, 286 KiB  
Article
Validity and Concordance of a Linear Position Transducer (Vitruve) for Measuring Movement Velocity during Resistance Training
by Jaime González-Galán, José Carlos Herrera-Bermudo, Juan José González-Badillo and David Rodríguez-Rosell
Sensors 2024, 24(19), 6444; https://doi.org/10.3390/s24196444 - 5 Oct 2024
Viewed by 294
Abstract
This study aimed to analyze the intra-device agreement of a new linear position transducer (Vitruve, VT) and the inter-device agreement with a previously validated linear velocity transducer (T-Force System, TF) in different range of velocities. A group of 50 healthy, physically active men [...] Read more.
This study aimed to analyze the intra-device agreement of a new linear position transducer (Vitruve, VT) and the inter-device agreement with a previously validated linear velocity transducer (T-Force System, TF) in different range of velocities. A group of 50 healthy, physically active men performed a progressive loading test during a bench press (BP) and full-squat (SQ) exercise with a simultaneous recording of two VT and one TF devices. The mean propulsive velocity (MPV) and peak of velocity (PV) were recorded for subsequent analysis. A set of statistics was used to determine the degree of agreement (Intraclass correlation coefficient [ICC], Lin’s concordance correlation coefficient [CCC], mean square deviation [MSD], and variance of the difference between measurements [VMD]) and the error magnitude (standard error of measurement [SEM], smallest detectable change [SDC], and maximum errors [ME]) between devices. The established velocity ranges were as follows: >1.20 m·s−1; 1.20–0.95 m·s−1; 0.95–0.70 m·s−1; 0.70–0.45 m·s−1; ≤0.45 m·s−1 for BP; and >1.50 m·s−1; 1.50–1.25 m·s−1; 1.25–1.00 m·s−1; 1.00–0.75 m·s−1; and ≤0.75 m·s−1 for SQ. For the MPV, the VT system showed high intra- and inter-device agreement and moderate error magnitude with pooled data in both exercises. However, the level of agreement decreased (ICC: 0.790–0.996; CCC: 0.663–0.992) and the error increased (ME: 2.8–13.4% 1RM; SEM: 0.035–0.01 m·s−1) as the velocity range increased. For the PV, the magnitude of error was very high in both exercises. In conclusion, our results suggest that the VT system should only be used at MPVs below 0.45 m·s−1 for BP and 0.75 m·s−1 for SQ in order to obtain an accurate and reliable measurement, preferably using the MPV variable instead of the PV. Therefore, it appears that the VT system may not be appropriate for objectively monitoring resistance training and assessing strength performance along the entire spectrum of load-velocity curve. Full article
(This article belongs to the Section Physical Sensors)
15 pages, 6939 KiB  
Article
Evaluation of Spatial–Temporal Variations in Ecological Environment Quality in the Red Soil Region of Southern China: A Case Study of Changting County
by Junming Chen, Guangfa Lin and Zhibiao Chen
Appl. Sci. 2024, 14(19), 8641; https://doi.org/10.3390/app14198641 - 25 Sep 2024
Viewed by 297
Abstract
The evaluation of ecological environment quality (EEQ) is an important method to measure the quality of ecosystem services. Therefore, the EEQ of Changting County, located in the red soil region of southern China, was assessed by using the remote sensing ecological index (RSEI) [...] Read more.
The evaluation of ecological environment quality (EEQ) is an important method to measure the quality of ecosystem services. Therefore, the EEQ of Changting County, located in the red soil region of southern China, was assessed by using the remote sensing ecological index (RSEI) based on Landsat images from 1995 to 2019, and its spatiotemporal variability was identified by using the Global Moran’s I index, standard deviational ellipse, and kernel density estimation. The results showed that, firstly, the EEQ degraded from 1995 to 2000, then improved from 2000 to 2019; secondly, the spatial distribution of the RSEI for each study year was not random and had a strong positive correlation; thirdly, the directional distributions of the RSEI for all the grades were almost in the direction of southwest to northeast, and the spatial discrete characteristics of the moderate- and good-grade areas were almost consistent from 1995 to 2019; fourthly, the kernel density distribution of the moderate- and good-grade EEQ was located in towns within the Tingjiang River Basin and in the surroundings of the study area, respectively. This study can help managers to better understand the spatial–temporal variations in the EEQ in the study area, supporting the government in formulating a better ecological restoration strategy. Full article
(This article belongs to the Section Ecology Science and Engineering)
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<p>Geographical location of the study area.</p>
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<p>Methodology flow chart.</p>
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<p>Spatial distribution of the EEQ from 1995 to 2019.</p>
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<p>Change trend of kernel density of EEQ in each grade.</p>
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<p>Change trend of direction distribution of EEQ in each grade.</p>
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23 pages, 2798 KiB  
Systematic Review
The Effect of Rainfall and Temperature Patterns on Childhood Linear Growth in the Tropics: Systematic Review and Meta-Analysis
by Derese Tamiru Desta, Tadesse Fikre Teferra and Samson Gebremedhin
Int. J. Environ. Res. Public Health 2024, 21(10), 1269; https://doi.org/10.3390/ijerph21101269 - 25 Sep 2024
Viewed by 836
Abstract
Despite existing research on child undernutrition in the tropics, a comprehensive understanding of how weather patterns impact childhood growth remains limited. This study summarizes and estimates the effect of rainfall and temperature patterns on childhood linear growth among under-fives in the tropics. A [...] Read more.
Despite existing research on child undernutrition in the tropics, a comprehensive understanding of how weather patterns impact childhood growth remains limited. This study summarizes and estimates the effect of rainfall and temperature patterns on childhood linear growth among under-fives in the tropics. A total of 41 out of 829 studies were considered based on preset inclusion criteria. Standardized regression coefficients (β) were used to estimate effect sizes, which were subsequently pooled, and forest plots were generated to visually represent the effect size estimates along with their 95% confidence intervals. Of the total reports, 28 and 13 research articles were included in the narrative synthesis and meta-analysis, respectively. The studies establish that patterns in rainfall and temperature either increase or decrease childhood linear growth and the risk of stunting. An increase in every one standard deviation of rainfall results in a 0.049 standard deviation increase in linear growth (β = 0.049, 95% CI: 0.024 to 0.073). This positive association is likely mediated by various factors. In countries where agriculture is heavily dependent on rainfall, increased precipitation can lead to higher crop yields which could in turn result in improved food security. The improved food security positively impacts childhood nutrition and growth. However, the extent to which these benefits are realized can vary depending on moderating factors such as location and socio-economic status. Temperature pattern showed a negative correlation with linear growth, where each standard deviation increase resulted in a decrease in linear growth by 0.039 standard deviations, with specific impacts varying by regional climates (β = −0.039, 95% CI: −0.065 to −0.013). Additionally, our meta-analysis shows a small but positive relationship of childhood stunting with temperature pattern in western Africa (β = 0.064, 95% CI: 0.035, 0.093). This association is likely due to temperature patterns’ indirect effects on food security and increased disease burden. Thus, the intricate interaction between weather patterns and childhood linear growth requires further research to distinguish the relationship considering other factors in the global tropics. While our findings provide valuable insights, they are primarily based on observational studies from sub-Saharan Africa and may not be generalizable to other tropical regions. Full article
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<p>Conceptual framework showing how climate-change-induced temperature and rainfall variations affect childhood linear growth. Source: Authors’ own elaboration.</p>
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<p>PRISMA flow diagram illustrating the number of studies included in the systematic review and meta-analysis.</p>
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<p>Effect of rainfall patterns on childhood linear growth reported by the original studies in the global tropics (2024).</p>
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<p>Effect of temperature pattern on childhood linear growth reported by the original studies in the global tropics (2024).</p>
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<p>Forest plot of pooled effect size (β) showing the effect of rainfall pattern on childhood linear growth (height-for-age z-score) [<a href="#B7-ijerph-21-01269" class="html-bibr">7</a>,<a href="#B64-ijerph-21-01269" class="html-bibr">64</a>,<a href="#B69-ijerph-21-01269" class="html-bibr">69</a>,<a href="#B70-ijerph-21-01269" class="html-bibr">70</a>,<a href="#B71-ijerph-21-01269" class="html-bibr">71</a>,<a href="#B72-ijerph-21-01269" class="html-bibr">72</a>].</p>
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<p>Forest plot of pooled effect size (β) showing the effects of temperature patterns on childhood linear growth [<a href="#B41-ijerph-21-01269" class="html-bibr">41</a>,<a href="#B64-ijerph-21-01269" class="html-bibr">64</a>,<a href="#B66-ijerph-21-01269" class="html-bibr">66</a>,<a href="#B68-ijerph-21-01269" class="html-bibr">68</a>].</p>
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<p>Forest plot of pooled effect size (β) showing the effects of temperature patterns on childhood linear growth failure (height-for-age z-score &lt; −2) [<a href="#B7-ijerph-21-01269" class="html-bibr">7</a>,<a href="#B36-ijerph-21-01269" class="html-bibr">36</a>,<a href="#B65-ijerph-21-01269" class="html-bibr">65</a>,<a href="#B67-ijerph-21-01269" class="html-bibr">67</a>].</p>
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24 pages, 25883 KiB  
Article
Modular Hemipelvic Prosthesis Preserves Normal Biomechanics and Showed Good Compatibility: A Finite Element Analysis
by Yuanrui Luo, Hongtao Sheng, Yong Zhou, Li Min, Chongqi Tu and Yi Luo
J. Funct. Biomater. 2024, 15(9), 276; https://doi.org/10.3390/jfb15090276 - 21 Sep 2024
Viewed by 651
Abstract
This study aimed to evaluate the biomechanical compatibility of a modular hemipelvic prosthesis by comparing stress distributions between an implanted pelvis and a healthy pelvis. Finite element analysis was used to simulate bilateral standing loads on both models, analyzing critical regions such as [...] Read more.
This study aimed to evaluate the biomechanical compatibility of a modular hemipelvic prosthesis by comparing stress distributions between an implanted pelvis and a healthy pelvis. Finite element analysis was used to simulate bilateral standing loads on both models, analyzing critical regions such as the sacroiliac joints, iliac crest, acetabulum, and prosthesis connection points. Six models with varied displacements of the hip joint rotational center were also introduced to assess the impact of deviations on stress distribution. The implanted pelvis had a stress distribution closely matching that of the intact pelvis, indicating that the prosthesis design maintained the biomechanical integrity of the pelvis. Stress patterns in displacement models with deviations of less than 10 mm were similar to the standard model, with only minor changes in stress magnitude. However, backward, upward, and inward deviations resulted in stress concentrations, particularly in the prosthesis connection points, increasing the likelihood of mechanical failure. The modular hemipelvic prosthesis demonstrated good biomechanical compatibility with minimal impact on pelvic stress distribution, even with moderate deviations in the hip joint’s rotational center; outward, forward, and downward displacements are preferable to minimize stress concentration and prevent implant failure in cases where minor deviations in the rotational center are unavoidable during surgery. Full article
(This article belongs to the Section Bone Biomaterials)
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<p>Composition of the modular semi-pelvic prosthesis: (<b>A</b>) CS spinal internal fixation device; (<b>B</b>) pubic branch; (<b>C</b>) acetabular cup; (<b>D</b>) fixation screws; (<b>E</b>) spinal internal fixation device, acetabular cup, and inner cup; (<b>F</b>) ischial branch; (<b>G</b>) assembly diagram of the hemipelvic prosthesis.</p>
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<p>A pelvic CT scan of a healthy adult.</p>
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<p>Three-dimensional reconstruction of the pelvis. (<b>A</b>) Pelvic cross-sectional CT scan; (<b>B</b>) pelvic coronal CT scan; (<b>C</b>) 3D reconstruction model of the normal pelvis.</p>
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<p>Solid model of pelvis and prosthesis. (<b>A</b>) Solid model of the normal pelvis; (<b>B</b>) solid model of the modular hemipelvic prosthesis.</p>
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<p>Establishment of the solid model of the pelvic defect. (<b>A</b>) Schematic diagram of the extent of the pelvic resection (the blue line); (<b>B</b>) solid model of the pelvic defect.</p>
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<p>Hemipelvic prosthesis standard position solid model and rotation center displacement diagram. (<b>A</b>) Standard model; (<b>B</b>) lateral and vertical displacement diagram; (<b>C</b>) vertical and anterior–posterior displacement diagram.</p>
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<p>Models of different hip joint rotation centers after prosthetic replacement surgery. (<b>A</b>) Inward displacement model; (<b>B</b>) outward displacement model; (<b>C</b>) upward displacement model ((<b>E</b>) lateral view); (<b>D</b>) downward displacement model ((<b>F</b>) lateral view); (<b>G</b>) forward displacement model; (<b>H</b>) backward displacement model.</p>
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<p>Three-dimensional finite element meshed models. (<b>A</b>) normal model; (<b>B</b>) standard model; (<b>C</b>) inward displacement model; (<b>D</b>) outward displacement model; (<b>E</b>) upward displacement model; (<b>F</b>) downward displacement model; (<b>G</b>) lateral view of the outward displacement model; (<b>H</b>) lateral view of the upward displacement model; (<b>I</b>) lateral view of the forward displacement model; (<b>J</b>) lateral view of the backward displacement model.</p>
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<p>Schematic diagram of mechanical loading direction.</p>
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<p>Schematic diagram of stress measurement locations in the pelvis and prosthesis model. (<b>A</b>) Stress measurement points in the pelvic model; (<b>B</b>) stress measurement points in the prosthesis model.</p>
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<p>Stress distribution nephogram of the standing position of both feet in a normal pelvis model (e5 = × 10<sup>5</sup>, e6 = × 10<sup>6</sup>, e7 = × 10<sup>7</sup>).</p>
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<p>Standard model finite element stress distribution nephogram (e5 = × 10<sup>5</sup>, e6 = × 10<sup>6</sup>, e7 = × 10<sup>7</sup>).</p>
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<p>Finite element stress distribution nephogram of the inward displacement model (e5 = × 10<sup>5</sup>, e6 = × 10<sup>6</sup>, e7 = × 10<sup>7</sup>).</p>
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<p>Finite element stress distribution nephogram of the outward displacement model (e5 = × 10<sup>5</sup>, e6 = × 10<sup>6</sup>, e7 = × 10<sup>7</sup>).</p>
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<p>Finite element stress distribution nephogram of the backward model (e5 = × 10<sup>5</sup>, e6 = × 10<sup>6</sup>, e7 = × 10<sup>7</sup>).</p>
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<p>Finite element stress distribution nephogram of the forward model (e5 = × 10<sup>5</sup>, e6 = × 10<sup>6</sup>, e7 = × 10<sup>7</sup>).</p>
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<p>Finite element stress distribution nephogram of the upward model (e5 = × 10<sup>5</sup>, e6 = × 10<sup>6</sup>, e7 = × 10<sup>7</sup>).</p>
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<p>Finite element stress distribution nephogram of the downward model (e5 = × 10<sup>5</sup>, e6 = × 10<sup>6</sup>, e7 = × 10<sup>7</sup>).</p>
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13 pages, 2594 KiB  
Article
Evaluation of an Enzyme-Linked Magnetic Electrochemical Assay for Hepatitis a Virus Detection in Drinking and Vegetable Processing Water
by Cristine D’Agostino, Rocco Cancelliere, Antonio Ceccarelli, Danila Moscone, Loredana Cozzi, Giuseppina La Rosa, Elisabetta Suffredini and Laura Micheli
Chemosensors 2024, 12(9), 188; https://doi.org/10.3390/chemosensors12090188 - 14 Sep 2024
Viewed by 885
Abstract
Globally, waterborne viral infections significantly threaten public health. While current European Union regulations stipulate that drinking water must be devoid of harmful pathogens, they do not specifically address the presence of enteric viruses in water used for irrigation or food production. Traditional virus [...] Read more.
Globally, waterborne viral infections significantly threaten public health. While current European Union regulations stipulate that drinking water must be devoid of harmful pathogens, they do not specifically address the presence of enteric viruses in water used for irrigation or food production. Traditional virus detection methods rely on molecular biology assays, requiring specialized personnel and laboratory facilities. Here, we describe an electrochemical sandwich enzyme-linked immunomagnetic assay (ELIME) for the detection of the hepatitis A virus (HAV) in water matrices. This method employed screen-printed electrodes as the sensing platform and utilized commercially available pre-activated magnetic beads to provide a robust foundation for the immunological reaction. The ELIME assay demonstrated exceptional analytical performance in only 185 min achieving a detection limit of 0.5 genomic copies per milliliter (g.c./mL) and exhibiting good reproducibility with a relative standard deviation (RSD) of 7% in HAV-spiked drinking and processing water samples. Compared with the real-time RT-qPCR method described in ISO 15216-1, the ELIME assay demonstrated higher sensitivity, although the overall linearity of the method was moderate. These analytical attributes highlight the potential of the ELIME assay as a rapid and viable alternative for HAV detection in water used for agriculture and food processing. Full article
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Graphical abstract

Graphical abstract
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<p>Protocol used in the ELIME detection.</p>
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<p>Extraction method for HAV from drinking water and water for vegetable processing.</p>
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<p>General schematic of ELIME functioning. (<b>a</b>) Immobilization of Goat Anti-Mouse IgG magnetic nanoparticles (MN-MAb1) on SPE, (<b>b</b>) antigen–antibody interaction, (<b>c</b>) immunocomplex detection using Mab-AP, and (<b>d</b>) electrochemical detection using DPV.</p>
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<p>Mab1 optimization. (<b>a</b>) The effect of the MAbI concentration on the signal. Protocol: 3% dry milk (DM); [MAbI] 0, 5, 10, and 15 μg/mL; [HAV] 10<sup>−8</sup> UI/mL; [MAb-AP] 1:10,000 (<span class="html-italic">v</span>/<span class="html-italic">v</span>) in PBS; and 5 mg/mL 1-NPP; in red circle the selected concentration is underlined. (<b>b</b>) The DPV measurement potential range 0–600 mV, the pulse width of 50 ms, pulse amplitude of 70 mV, and a scan rate of 100 mV/s.</p>
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<p>Study of the blocking reagent at different concentrations of MAb-AP. Three percent BSA, 3% DM, and 1% PVA; [MAb-AP] 1:5000, 1:10,000, and 1:25,000 (<span class="html-italic">v</span>/<span class="html-italic">v</span>) in PBS; 5 mg/mL 1-NPP; DPV measurement potential range 0–600 mV; pulse width of 50 ms; pulse amplitude of 70 mV; and scan rate of 100 mV/s.</p>
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<p>Electrochemical LbL-EC of ELIME assembly. (<b>a</b>) cv voltammograms and (<b>b</b>) EIS spectra. At least three experiments for each curve were conducted.</p>
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<p>Calibration curve for HAV. [MAb<sub>I</sub>] 10 μg/mL, 3% BSA, [HAV] 0–10 g.c./mL, [MAb-AP] 1:25,000 (<span class="html-italic">v</span>/<span class="html-italic">v</span>), and 5 mg/mL 1-NPP. DPV measurement potential range 0–600 mV, pulse width of 50 ms, pulse amplitude of 70 mV, and a scan rate of 100 mV/s. (<b>a</b>) 4-parameter logistic calibration curv. Parameters: <span class="html-italic">a</span> = 96.53, <span class="html-italic">b</span> = −3.03, <span class="html-italic">x</span><sub>0</sub> = 0.89, <span class="html-italic">y</span><sub>0</sub> = −0.18. LOD = 0.5 g.c./mL, RSD% 7%. (<b>b</b>) DPV measures used for the calibration curve.</p>
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18 pages, 6428 KiB  
Article
Calibration of Low-Cost Moisture Sensors in a Biochar-Amended Sandy Loam Soil with Different Salinity Levels
by María José Gómez-Astorga, Karolina Villagra-Mendoza, Federico Masís-Meléndez, Aníbal Ruíz-Barquero and Renato Rimolo-Donadio
Sensors 2024, 24(18), 5958; https://doi.org/10.3390/s24185958 - 13 Sep 2024
Viewed by 896
Abstract
With the increasing focus on irrigation management, it is crucial to consider cost-effective alternatives for soil water monitoring, such as multi-point monitoring with low-cost soil moisture sensors. This study assesses the accuracy and functionality of low-cost sensors in a sandy loam (SL) soil [...] Read more.
With the increasing focus on irrigation management, it is crucial to consider cost-effective alternatives for soil water monitoring, such as multi-point monitoring with low-cost soil moisture sensors. This study assesses the accuracy and functionality of low-cost sensors in a sandy loam (SL) soil amended with biochar at rates of 15.6 and 31.2 tons/ha by calibrating the sensors in the presence of two nitrogen (N) and potassium (K) commercial fertilizers at three salinity levels (non/slightly/moderately) and six soil water contents. Sensors were calibrated across nine SL-soil combinations with biochar and N and K fertilizers, counting for 21 treatments. The best fit for soil water content calibration was obtained using polynomial equations, demonstrating reliability with R2 values greater than 0.98 for each case. After a second calibration, low-cost soil moisture sensors provide acceptable results concerning previous calibration, especially for non- and slightly saline treatments and at soil moisture levels lower than 0.17 cm3cm−3. The results showed that at low frequencies, biochar and salinity increase the capacitance detected by the sensors, with calibration curves deviating up to 30% from the control sandy loam soil. Due to changes in the physical and chemical properties of soil resulting from biochar amendments and the conductive properties influenced by fertilization practices, it is required to conduct specific and continuous calibrations of soil water content sensor, leading to better agricultural management decisions. Full article
(This article belongs to the Section Smart Agriculture)
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<p>Soil moisture sensor used (<b>a</b>) capacitive soil moisture sensor v1.2 and (<b>b</b>) set of 5 soil moisture sensors installed for calibration.</p>
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<p>Sensor output performance over time under non-saline (NS) conditions for six soil moisture levels.</p>
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<p>Sensor output dispersion per treatment under non-saline (NS) conditions for six soil moisture levels.</p>
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<p>Sensor output performance over time under slightly saline (SS) conditions for different soil moisture levels.</p>
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<p>Sensor output dispersion per treatment under slightly saline (SS) conditions for different soil moisture levels.</p>
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<p>Sensor output performance over time under moderately saline (MS) conditions for six soil moisture levels.</p>
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<p>Sensor output dispersion per treatment under moderately saline (MS) conditions for six soil moisture levels.</p>
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<p>Calibration curves for all treatments under the NS, SS, and MS scenarios. The blue continuous line indicates the calibration curve for SLB0 without fertilizer and the gray lines represent the error deviation of 10%, 20%, and 30% with respect to SLB0.</p>
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<p>Performance of the soil moisture sensor through errors in treatment calibrations in a 2-week interval.</p>
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<p>Heat map of the <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> pattern for all treatments for (<b>a</b>) first calibration and (<b>b</b>) second calibration after a 2-week interval.</p>
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17 pages, 9864 KiB  
Article
Evaluation of δ-Phase ZrH1.4 to ZrH1.7 Thermal Neutron Scattering Laws Using Ab Initio Molecular Dynamics Simulations
by Vedant K. Mehta, Daniel A. Rehn and Pär A. T. Olsson
J. Nucl. Eng. 2024, 5(3), 330-346; https://doi.org/10.3390/jne5030022 - 13 Sep 2024
Viewed by 434
Abstract
Zirconium hydride is commonly used for next-generation reactor designs due to its excellent hydrogen retention capacity at temperatures below 1000 K. These types of reactors operate at thermal neutron energies and require accurate representation of thermal scattering laws (TSLs) to optimize moderator performance [...] Read more.
Zirconium hydride is commonly used for next-generation reactor designs due to its excellent hydrogen retention capacity at temperatures below 1000 K. These types of reactors operate at thermal neutron energies and require accurate representation of thermal scattering laws (TSLs) to optimize moderator performance and evaluate the safety indicators for reactor design. In this work, we present an atomic-scale representation of sub-stoichiometric ZrH2−x(0.3x0.6), which relies on ab initio molecular dynamics (AIMD) in tandem with velocity auto-correlation (VAC) analysis to generate phonon density of states (DOS) for TSL development. The novel NJOY+NCrystal tool, developed by the European Spallation Source community, was utilized to generate the TSL formulations in the A Compact ENDF (ACE) format for its utility in neutron transport software. First, stoichiometric zirconium hydride cross sections were benchmarked with experiments. Then sub-stoichiometric zirconium hydride TSLs were developed. Significant deviations were observed between the new δ-phase ZrH2−x TSLs and the TSLs in the current ENDF release. It was also observed that varying the hydrogen vacancy defect concentration and sites did not cause as significant a change in the TSLs (e.g., ZrH1.4 vs. ZrH1.7) as was caused by the lattice transformation from ϵ- to δ-phase. Full article
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<p>Hydrogen atom density in various metal–hydrogen systems as function of temperature in equilibrium with 1 atm hydrogen gas (from [<a href="#B17-jne-05-00022" class="html-bibr">17</a>], based on the data from references listed therein).</p>
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<p>Zr-H phase diagram with PCT curves. (from [<a href="#B18-jne-05-00022" class="html-bibr">18</a>,<a href="#B19-jne-05-00022" class="html-bibr">19</a>], based on the data from references listed therein). The dashed line box represents potential steady state reactor operation regime.</p>
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<p>(<b>Left</b>) The non-stoichiometric ZrH<sub>1.6</sub> phase with random hydrogen occupancy used in the AIMD simulations. The green and blue atoms correspond to Zr and H, respectively, while the red spheres indicate random vacant H sites. (<b>Right</b>) Hydrogen concentration-dependent lattice parameter compared with experimental data [<a href="#B35-jne-05-00022" class="html-bibr">35</a>,<a href="#B36-jne-05-00022" class="html-bibr">36</a>,<a href="#B37-jne-05-00022" class="html-bibr">37</a>,<a href="#B38-jne-05-00022" class="html-bibr">38</a>].</p>
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<p>(<b>Top Left</b>) Phonon DOS for Zr and (<b>Top Right</b>) H in <math display="inline"><semantics> <mrow> <mi>ϵ</mi> </mrow> </semantics></math>-ZrH<sub>2</sub>. (<b>Bottom Left</b>) Hydrogen phonon density of states for sub-stoichiometric <math display="inline"><semantics> <mrow> <mi>δ</mi> </mrow> </semantics></math>-ZrH<sub>2−<span class="html-italic">x</span></sub> computed using AIMD. The experimental data corresponds to <span class="html-italic">x</span> = 0.44. (<b>Bottom Right</b>) specific heat capacity for <math display="inline"><semantics> <mrow> <mi>ϵ</mi> </mrow> </semantics></math>-ZrH<sub>2</sub>. The experimental INS and heat capacity data are from [<a href="#B63-jne-05-00022" class="html-bibr">63</a>,<a href="#B64-jne-05-00022" class="html-bibr">64</a>,<a href="#B65-jne-05-00022" class="html-bibr">65</a>] and the AILD data is from [<a href="#B55-jne-05-00022" class="html-bibr">55</a>].</p>
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<p>Verification and validation for the <math display="inline"><semantics> <mrow> <mi>ϵ</mi> </mrow> </semantics></math>-ZrH<sub>2</sub> TSLs (this study) with ENDF evaluation [<a href="#B58-jne-05-00022" class="html-bibr">58</a>] and experimental data [<a href="#B66-jne-05-00022" class="html-bibr">66</a>,<a href="#B67-jne-05-00022" class="html-bibr">67</a>].</p>
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<p>Cross sections for varying stoichiometries generated using Ncrystal. ZrH<sub>2</sub> is in the <math display="inline"><semantics> <mrow> <mi>ϵ</mi> </mrow> </semantics></math>-phase.</p>
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<p>The hydrogen inelastic (<b>top left</b>) and zirconium elastic (<b>top right</b>) normalized ACE file components for room temperature. Along with total hydrogen bound in ZrH<sub>2−<span class="html-italic">x</span></sub> (<b>bottom left</b>) and zirconium bound in ZrH<sub>2−<span class="html-italic">x</span></sub> (<b>bottom right</b>) normalized ACE file TSLs compared with current ENDF release (in brown) [<a href="#B58-jne-05-00022" class="html-bibr">58</a>]. The TSLs were generated using the NJOY+NCrystal software.</p>
Full article ">Figure 8
<p>ACE cross section at 293 K (<b>left</b>) and 1000 K (<b>right)</b> for various ZrH<sub>2−<span class="html-italic">x</span></sub> sub-stoichiometries. The TSLs are plotted as H(ZrH<sub>2−<span class="html-italic">x</span></sub>) + Zr(ZrH<sub>2−<span class="html-italic">x</span></sub>) (instead of the total (H/Zr) × H(ZrH<sub>2−<span class="html-italic">x</span></sub>) + Zr(ZrH<sub>2−<span class="html-italic">x</span></sub>) cross section) for a normalized comparison.</p>
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<p>Detailed thermal scattering law profiles for all stoichiometries evaluated in this work. Plots generated using NCrystal. Cross sections are on a per atom basis as defaulted by NCrystal.</p>
Full article ">Figure A1 Cont.
<p>Detailed thermal scattering law profiles for all stoichiometries evaluated in this work. Plots generated using NCrystal. Cross sections are on a per atom basis as defaulted by NCrystal.</p>
Full article ">Figure A1 Cont.
<p>Detailed thermal scattering law profiles for all stoichiometries evaluated in this work. Plots generated using NCrystal. Cross sections are on a per atom basis as defaulted by NCrystal.</p>
Full article ">Figure A1 Cont.
<p>Detailed thermal scattering law profiles for all stoichiometries evaluated in this work. Plots generated using NCrystal. Cross sections are on a per atom basis as defaulted by NCrystal.</p>
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12 pages, 1322 KiB  
Article
Oxylipins in Aqueous Humor of Primary Open-Angle Glaucoma Patients
by Jianming Xu, Kewen Zhou, Changzhen Fu, Chong-Bo Chen, Yaru Sun, Xin Wen, Luxi Yang, Tsz-Kin Ng, Qingping Liu and Mingzhi Zhang
Biomolecules 2024, 14(9), 1127; https://doi.org/10.3390/biom14091127 - 5 Sep 2024
Viewed by 465
Abstract
Purpose: Investigate the oxylipin profiles in the aqueous humor of primary open-angle glaucoma (POAG) patients. Methods: Aqueous humor samples were collected from 17 POAG patients and 15 cataract subjects and subjected to a liquid chromatography/mass spectrometry (LC-MS) analysis to detect the oxylipins. The [...] Read more.
Purpose: Investigate the oxylipin profiles in the aqueous humor of primary open-angle glaucoma (POAG) patients. Methods: Aqueous humor samples were collected from 17 POAG patients and 15 cataract subjects and subjected to a liquid chromatography/mass spectrometry (LC-MS) analysis to detect the oxylipins. The prediction potential of the differential abundant oxylipins was assessed by the receiver operating characteristic (ROC) curves. Pathway and correlation analyses on the oxylipins and clinical and biochemical parameters were also conducted. Results: The LC-MS analysis detected a total of 76 oxylipins, of which 29 oxylipins reached the detection limit. The multivariate analysis identified five differential abundant oxylipins, 15-keto-prostaglandin F2 alpha (15-kPGF2α), Leukotriene B4 (LTB4), 12,13-Epoxyoctadecenoic acid (12,13-Epome), 15-Hydroxyeicosatetraenoic acid (15-HETE) and 11-Hydroxyeicosatetraenoic acid (11-HETE). The five oxylipins are enriched in the arachidonic acid metabolism and linoleic acid metabolism pathways. Pearson correlation analysis showed that 11-HETE was positively correlated with intraocular pressure and central corneal thickness and negatively with cup/disk area ratio in the POAG patients. In addition, 15-kPGF2α was moderately and positively correlated with the mean deviation (MD) of visual field defect, and LTB4 was moderately and negatively correlated with macular thickness. Conclusions: This study revealed the oxylipin profile in the aqueous humor of POAG patients. Oxylipins involved in the arachidonic acid metabolism pathway could play a role in POAG, and anti-inflammatory therapies could be potential treatment strategies for POAG. Full article
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<p>Univariate significance analysis. (<b>A</b>) Word Cloud diagram: a total of 76 oxylipins; Grouped Radiographic Histogram: Classification and specific content of 29 oxylipins in the POAG group (<b>B</b>) and control (<b>C</b>). Blue represents lipoxygenase, orange represents arachidonic acid, pink represents cytochrome P450, and green represents cyclooxygenase. (<b>D</b>) Histogram: red represents POAG, blue represents the control group, * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 2
<p>Metabolomic multivariate analysis. (<b>A</b>) Principal component analysis (PCA) plots and (<b>B</b>) orthogonal projections to latent structures–discriminate analysis (OPLS-DA) score plots illustrating the clustering and dispersion of the two groups. Red represents POAG; blue represents the control group. (<b>C</b>) Volcano plot: circles represent each differential oxylipin; red represents up-regulation and blue represents down-regulation. (<b>D</b>) Hierarchical clustering heatmap demonstrates the distribution of oxylipins in POAG and control groups. Red represents up-regulation and blue represents down-regulation. (<b>E</b>) The ROC curve evaluates the diagnostic performance.</p>
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<p>Pearson correlation analysis. (<b>A</b>) Scatter plot and (<b>B</b>) Pearson correlation analysis of oxylipins and clinical and biochemical parameters: red represents a positive correlation; blue represents a negative correlation. The darker the color, the greater the strength of the relationship, and vice versa. * stands for <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Pathway analysis. (<b>A</b>) Bubble plot and (<b>B</b>) enriched metabolite analysis indicate enrichment pathways in the AH between POAG and control. (<b>C</b>) Molecular structure and oxidation sites of 5 oxylipins.</p>
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<p>Metabolic pathway profiling. Linoleic acid–arachidonic acid–oxylipins.</p>
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33 pages, 31036 KiB  
Article
Enhancing Extreme Precipitation Forecasts through Machine Learning Quality Control of Precipitable Water Data from Satellite FengYun-2E: A Comparative Study of Minimum Covariance Determinant and Isolation Forest Methods
by Wenqi Shen, Siqi Chen, Jianjun Xu, Yu Zhang, Xudong Liang and Yong Zhang
Remote Sens. 2024, 16(16), 3104; https://doi.org/10.3390/rs16163104 - 22 Aug 2024
Viewed by 1122
Abstract
Variational data assimilation theoretically assumes Gaussian-distributed observational errors, yet actual data often deviate from this assumption. Traditional quality control methods have limitations when dealing with nonlinear and non-Gaussian-distributed data. To address this issue, our study innovatively applies two advanced machine learning (ML)-based quality [...] Read more.
Variational data assimilation theoretically assumes Gaussian-distributed observational errors, yet actual data often deviate from this assumption. Traditional quality control methods have limitations when dealing with nonlinear and non-Gaussian-distributed data. To address this issue, our study innovatively applies two advanced machine learning (ML)-based quality control (QC) methods, Minimum Covariance Determinant (MCD) and Isolation Forest, to process precipitable water (PW) data derived from satellite FengYun-2E (FY2E). We assimilated the ML QC-processed TPW data using the Gridpoint Statistical Interpolation (GSI) system and evaluated its impact on heavy precipitation forecasts with the Weather Research and Forecasting (WRF) v4.2 model. Both methods notably enhanced data quality, leading to more Gaussian-like distributions and marked improvements in the model’s simulation of precipitation intensity, spatial distribution, and large-scale circulation structures. During key precipitation phases, the Fraction Skill Score (FSS) for moderate to heavy rainfall generally increased to above 0.4. Quantitative analysis showed that both methods substantially reduced Root Mean Square Error (RMSE) and bias in precipitation forecasting, with the MCD method achieving RMSE reductions of up to 58% in early forecast hours. Notably, the MCD method improved forecasts of heavy and extremely heavy rainfall, whereas the Isolation Forest method demonstrated a superior performance in predicting moderate to heavy rainfall intensities. This research not only provides a basis for method selection in forecasting various precipitation intensities but also offers an innovative solution for enhancing the accuracy of extreme weather event predictions. Full article
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<p>The 500 hPa geopotential height (blue contours; gpm), 850 hPa water vapor flux (shading; <math display="inline"><semantics> <mrow> <mi>kg</mi> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>), and 850 hPa wind vectors (arrows; <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) from 06:00 UTC 7 July 2013 to 06:00 UTC 9 July 2013, based on ERA5 reanalysis data (outer domain, d01); the purple line indicates the boundary of the Tibetan Plateau, and the black bold line indicates the boundary of Sichuan Province. The study domain and WRF model domains are shown in <a href="#remotesensing-16-03104-f002" class="html-fig">Figure 2</a>.</p>
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<p>Simulation domains and model nesting. The purple line indicates the boundary of the Tibetan Plateau, and the black bold line indicates the boundary of Sichuan Province. Red dots represent major cities (Chengdu, Mianyang, Ya’an, and Chongqing). The blue and red rectangles denote the outer (d01) and inner (d02) model domains, respectively. Background shading shows terrain elevation.</p>
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<p>Distribution of PW data from FY2E satellite (<b>left</b>) and CMA ground stations (<b>right</b>) at 12:00 UTC on 8 July 2013. The different colors represent different PW value ranges.</p>
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<p>Distribution of FY2E TPW data innovation at three representative time points: 06:00 UTC 8 July (<b>top row</b>), 06:00 UTC 9 July (<b>middle row</b>), and 06:00 UTC 10 July (<b>bottom row</b>). Each row shows the data distribution before QC (<b>left column</b>), after applying the MCD method (<b>middle column</b>), and after applying the Isolation Forest method (<b>right column</b>). Green histograms represent the data distribution, red dashed lines indicate fitted Gaussian distribution curves, and blue dashed lines mark zero innovation.</p>
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<p>Box plots of FY-2E TPW data innovation (in mm) at 9 assimilation times before and after QC. The different colors represent different time points.</p>
Full article ">Figure 6
<p>(<b>A</b>) Spatial distribution of reject points of FY-2E TPW data at 9 assimilation times after QC using the MCD method. (<b>B</b>) Spatial distribution of pass points of FY-2E TPW data at 9 assimilation times after QC using the MCD method.</p>
Full article ">Figure 6 Cont.
<p>(<b>A</b>) Spatial distribution of reject points of FY-2E TPW data at 9 assimilation times after QC using the MCD method. (<b>B</b>) Spatial distribution of pass points of FY-2E TPW data at 9 assimilation times after QC using the MCD method.</p>
Full article ">Figure 7
<p>(<b>A</b>) Spatial distribution of reject points of FY-2E TPW data at 9 assimilation times after QC using the Isolation Forest method. (<b>B</b>) Spatial distribution of pass points of FY-2E TPW data at 9 assimilation times after QC using the Isolation Forest method.</p>
Full article ">Figure 7 Cont.
<p>(<b>A</b>) Spatial distribution of reject points of FY-2E TPW data at 9 assimilation times after QC using the Isolation Forest method. (<b>B</b>) Spatial distribution of pass points of FY-2E TPW data at 9 assimilation times after QC using the Isolation Forest method.</p>
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<p>(<b>A</b>) A 500 hPa geopotential height (blue contours; gpm), vertically integrated water vapor flux (shading; <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <mi mathvariant="normal">g</mi> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">cm</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mi mathvariant="normal">h</mi> <mi mathvariant="normal">P</mi> <msup> <mrow> <mi mathvariant="normal">a</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) from 925 to 750 hPa, and 850 hPa wind vectors (arrows; <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013, based on the CTRL experiment simulation (outer domain, d01); the purple line indicates the boundary of the Tibetan Plateau, and the black bold line indicates the boundary of Sichuan Province. (<b>B</b>) Similar to (<b>A</b>) but for the EXPR1 experimental simulation. (<b>C</b>) Similar to (<b>A</b>) but for the EXPR2(MCD) experimental simulation. (<b>D</b>) Similar to (<b>A</b>) but for the EXPR3 (Isolation Forest) experimental simulation.</p>
Full article ">Figure 8 Cont.
<p>(<b>A</b>) A 500 hPa geopotential height (blue contours; gpm), vertically integrated water vapor flux (shading; <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <mi mathvariant="normal">g</mi> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">cm</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mi mathvariant="normal">h</mi> <mi mathvariant="normal">P</mi> <msup> <mrow> <mi mathvariant="normal">a</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) from 925 to 750 hPa, and 850 hPa wind vectors (arrows; <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013, based on the CTRL experiment simulation (outer domain, d01); the purple line indicates the boundary of the Tibetan Plateau, and the black bold line indicates the boundary of Sichuan Province. (<b>B</b>) Similar to (<b>A</b>) but for the EXPR1 experimental simulation. (<b>C</b>) Similar to (<b>A</b>) but for the EXPR2(MCD) experimental simulation. (<b>D</b>) Similar to (<b>A</b>) but for the EXPR3 (Isolation Forest) experimental simulation.</p>
Full article ">Figure 8 Cont.
<p>(<b>A</b>) A 500 hPa geopotential height (blue contours; gpm), vertically integrated water vapor flux (shading; <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <mi mathvariant="normal">g</mi> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">cm</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mi mathvariant="normal">h</mi> <mi mathvariant="normal">P</mi> <msup> <mrow> <mi mathvariant="normal">a</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) from 925 to 750 hPa, and 850 hPa wind vectors (arrows; <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013, based on the CTRL experiment simulation (outer domain, d01); the purple line indicates the boundary of the Tibetan Plateau, and the black bold line indicates the boundary of Sichuan Province. (<b>B</b>) Similar to (<b>A</b>) but for the EXPR1 experimental simulation. (<b>C</b>) Similar to (<b>A</b>) but for the EXPR2(MCD) experimental simulation. (<b>D</b>) Similar to (<b>A</b>) but for the EXPR3 (Isolation Forest) experimental simulation.</p>
Full article ">Figure 8 Cont.
<p>(<b>A</b>) A 500 hPa geopotential height (blue contours; gpm), vertically integrated water vapor flux (shading; <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <mi mathvariant="normal">g</mi> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">cm</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mi mathvariant="normal">h</mi> <mi mathvariant="normal">P</mi> <msup> <mrow> <mi mathvariant="normal">a</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) from 925 to 750 hPa, and 850 hPa wind vectors (arrows; <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013, based on the CTRL experiment simulation (outer domain, d01); the purple line indicates the boundary of the Tibetan Plateau, and the black bold line indicates the boundary of Sichuan Province. (<b>B</b>) Similar to (<b>A</b>) but for the EXPR1 experimental simulation. (<b>C</b>) Similar to (<b>A</b>) but for the EXPR2(MCD) experimental simulation. (<b>D</b>) Similar to (<b>A</b>) but for the EXPR3 (Isolation Forest) experimental simulation.</p>
Full article ">Figure 9
<p>Distribution of observed 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013, provided by the CMA; unit: mm. Dot size is proportional to precipitation intensity, with larger dots indicating higher precipitation amounts.</p>
Full article ">Figure 10
<p>(<b>A</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the CTRL experiment; unit: mm. (<b>B</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR1 experiment; unit: mm. (<b>C</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR2 experiment QC by MCD method; unit: mm. (<b>D</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR3 experiment QC by Isolation Forest method; unit: mm.</p>
Full article ">Figure 10 Cont.
<p>(<b>A</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the CTRL experiment; unit: mm. (<b>B</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR1 experiment; unit: mm. (<b>C</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR2 experiment QC by MCD method; unit: mm. (<b>D</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR3 experiment QC by Isolation Forest method; unit: mm.</p>
Full article ">Figure 10 Cont.
<p>(<b>A</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the CTRL experiment; unit: mm. (<b>B</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR1 experiment; unit: mm. (<b>C</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR2 experiment QC by MCD method; unit: mm. (<b>D</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR3 experiment QC by Isolation Forest method; unit: mm.</p>
Full article ">Figure 10 Cont.
<p>(<b>A</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the CTRL experiment; unit: mm. (<b>B</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR1 experiment; unit: mm. (<b>C</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR2 experiment QC by MCD method; unit: mm. (<b>D</b>) Distribution of 6 h accumulated precipitation (inner domain, d02) from 06:00 UTC 8 July 2013 to 06:00 UTC 10 July 2013 in the EXPR3 experiment QC by Isolation Forest method; unit: mm.</p>
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<p>Bar graph of FSSs for the four experimental groups at nine assimilation times.</p>
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<p>Bar graph of the mean FSSs for the four groups of experiments.</p>
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16 pages, 1141 KiB  
Article
Weight Status, Autonomic Function, and Systemic Inflammation in Children with Obstructive Sleep Apnea
by Hai-Hua Chuang, Chung-Guei Huang, Jen-Fu Hsu, Li-Pang Chuang, Yu-Shu Huang, Hsueh-Yu Li and Li-Ang Lee
Int. J. Mol. Sci. 2024, 25(16), 8951; https://doi.org/10.3390/ijms25168951 - 16 Aug 2024
Viewed by 544
Abstract
Children with obstructive sleep apnea (OSA) frequently experience chronic low-grade systemic inflammation, with the inflammasome playing a central role in OSA. This cross-sectional study evaluated the relationship between weight status, autonomic function, and systemic inflammation in a cohort of 55 children with OSA, [...] Read more.
Children with obstructive sleep apnea (OSA) frequently experience chronic low-grade systemic inflammation, with the inflammasome playing a central role in OSA. This cross-sectional study evaluated the relationship between weight status, autonomic function, and systemic inflammation in a cohort of 55 children with OSA, predominantly boys (78%) with an average age of 7.4 ± 2.2 years and an apnea-hypopnea index of 14.12 ± 17.05 events/hour. Measurements were taken of body mass index (BMI), sleep heart-rate variability, morning circulatory levels of interleukin-1β, interleukin-1 receptor antagonist, and interleukin-6, and tumor necrosis factor-α, anthropometry, and polysomnography. Multiple linear regression modeling showed that an apnea-hypopnea index was significantly associated with BMI, the standard deviation of successive differences between normal-to-normal intervals during N3 sleep, and the proportion of normal-to-normal interval pairs differing by more than 50 ms during rapid-eye-movement sleep. A moderated mediation model revealed that interleukin-1 receptor antagonist levels mediated the association between BMI and interleukin-6 levels, with sympathovagal balance during N3 sleep and minimum blood oxygen saturation further moderating these relationships. This study highlights the complex relationships between BMI, polysomnographic parameters, sleep heart-rate-variability metrics, and inflammatory markers in children with OSA, underlining the importance of weight management in this context. Full article
(This article belongs to the Special Issue Roles of Inflammasomes in Inflammatory Responses and Human Diseases)
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<p>Flowchart of the present study.</p>
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<p>Significant associations across important variables of weight status, polysomnographic parameters, heart-rate-variability metrics, and systemic inflammatory markers. Abbreviations: AHI: apnea-hypopnea index; AI: apnea index; ANR: adenoidal-nasopharyngeal ratio; BMI: body mass index; IL: interleukin; IL-1RA: IL-1 receptor antagonist; pNN50: proportion of N-N interval pairs differing by more than 50 ms; REM: rapid eye movement; SDNN: standard deviation of all normal-to-normal intervals; SDSD: standard deviation of successive differences between normal-to-normal intervals; SpO<sub>2</sub>: blood oxygen saturation; TNF: tumor necrosis factor. Data are summarized as correlation coefficients. Blank spaces mean two-sided adjusted <span class="html-italic">p</span>-values ≥ 0.05.</p>
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<p>This figure depicts a moderated mediation model with BMI as the independent variable affecting the level of interleukin-6 (IL-6), the dependent variable, mediated by the level of IL-1 receptor antagonist (IL-1RA). Additionally, the standard deviation of all normal-to-normal intervals (SDNN)/standard deviation of successive differences between normal-to-normal intervals (SDSD) ratio during N3 sleep acts as a moderator in the relationship between body mass index (BMI) and IL-1 receptor antagonist (IL-1RA) levels. The minimum blood oxygen saturation (SpO<sub>2</sub>) acts as a moderator in the relationship between IL-1RA and IL-6 levels. Regression coefficients (β) and standard errors (SE) are provided, illustrating both the direct and indirect pathways within the model.</p>
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17 pages, 244 KiB  
Article
Government Environmental Expenditure, Budget Management, and Regional Carbon Emissions: Provincial Panel Data from China
by Ziru Tang, Zenglian Zhang and Wenyueyang Deng
Sustainability 2024, 16(15), 6707; https://doi.org/10.3390/su16156707 - 5 Aug 2024
Cited by 1 | Viewed by 866
Abstract
To explore the impact of government fiscal intervention on regional carbon emissions, this paper employs a two-way fixed-effects model to analyze data from 30 provinces in China, spanning the period from 2008 to 2017. This study investigates the effects of local government environmental [...] Read more.
To explore the impact of government fiscal intervention on regional carbon emissions, this paper employs a two-way fixed-effects model to analyze data from 30 provinces in China, spanning the period from 2008 to 2017. This study investigates the effects of local government environmental expenditure and government budget on the per capita volume, intensity, and performance of regional carbon emissions. The results show that government environmental expenditure is beneficial to reducing regional carbon emissions and improving regional carbon emission performance. Second, the smaller the deviation between the government budget and final accounts, the more conducive it is to reducing carbon emissions. Third, we found that government environmental expenditure has the strongest inhibitory effect on regional carbon emissions in the eastern region, followed by the central region, and the weakest in the western region. Finally, government financial transparency positively moderates the inhibitory effect of government budget management on regional carbon emissions, that is, when fiscal transparency is high, the amplification effect of budget deviation on regional carbon emissions is weakened. Full article
19 pages, 427 KiB  
Review
Potential Benefits of Continuous Glucose Monitoring for Predicting Vascular Outcomes in Type 2 Diabetes: A Rapid Review of Primary Research
by Radhika Kiritsinh Jadav, Kwang Choon Yee, Murray Turner and Reza Mortazavi
Healthcare 2024, 12(15), 1542; https://doi.org/10.3390/healthcare12151542 - 4 Aug 2024
Viewed by 1456
Abstract
(1) Background: Chronic hyperglycaemia is a cause of vascular damage and other adverse clinical outcomes in type 2 diabetes mellitus (T2DM). Emerging evidence suggests a significant and independent role for glycaemic variability (GV) in contributing to those outcomes. Continuous glucose monitoring (CGM) provides [...] Read more.
(1) Background: Chronic hyperglycaemia is a cause of vascular damage and other adverse clinical outcomes in type 2 diabetes mellitus (T2DM). Emerging evidence suggests a significant and independent role for glycaemic variability (GV) in contributing to those outcomes. Continuous glucose monitoring (CGM) provides valuable insights into GV. Unlike in type 1 diabetes mellitus, the use of CGM-derived GV indices has not been widely adopted in the management of T2DM due to the limited evidence of their effectiveness in predicting clinical outcomes. This study aimed to explore the associations between GV metrics and short- or long-term vascular and clinical complications in T2DM. (2) Methods: A rapid literature review was conducted using the Cochrane Library, MEDLINE, and Scopus databases to seek high-level evidence. Lower-quality studies such as cross-sectional studies were excluded, but their content was reviewed. (3) Results: Six studies (five prospective cohort studies and one clinical trial) reported associations between GV indices (coefficient of variation (CV), standard deviation (SD), Mean Amplitude of Glycaemic Excursions (MAGE), Time in Range (TIR), Time Above Range (TAR), and Time Below Range (TBR)), and clinical complications. However, since most evidence came from moderate to low-quality studies, the results should be interpreted with caution. (4) Conclusions: Limited but significant evidence suggests that GV indices may predict clinical compilations in T2DM both in the short term and long term. There is a need for longitudinal studies in larger and more diverse populations, longer follow-ups, and the use of numerous CGM-derived GV indices while collecting information about all microvascular and macrovascular complications. Full article
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<p>The search process according to PRISMA 2020 flow diagram [<a href="#B35-healthcare-12-01542" class="html-bibr">35</a>].</p>
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11 pages, 1109 KiB  
Article
Musculoskeletal Dimension and Brightness Reference Values in Lumbar Magnetic Resonance Imaging—A Radio-Anatomic Investigation in 80 Healthy Adult Individuals
by Horst Balling, Boris Michael Holzapfel, Wolfgang Böcker, Dominic Simon, Paul Reidler and Joerg Arnholdt
J. Clin. Med. 2024, 13(15), 4496; https://doi.org/10.3390/jcm13154496 - 1 Aug 2024
Viewed by 730
Abstract
Background/Objectives: Magnetic resonance imaging (MRI) is the preferred diagnostic means to visualize spinal pathologies, and offers the possibility of precise structural tissue analysis. However, knowledge about MRI-based measurements of physiological cross-sectional musculoskeletal dimensions and associated tissue-specific average structural brightness in the lumbar [...] Read more.
Background/Objectives: Magnetic resonance imaging (MRI) is the preferred diagnostic means to visualize spinal pathologies, and offers the possibility of precise structural tissue analysis. However, knowledge about MRI-based measurements of physiological cross-sectional musculoskeletal dimensions and associated tissue-specific average structural brightness in the lumbar spine of healthy young women and men is scarce. The current study was planned to investigate characteristic intersexual differences and to provide MRI-related musculoskeletal baseline values before the onset of biological aging. Methods: At a single medical center, lumbar MRI scans of 40 women and 40 men aged 20–40 years who presented with moderate nonspecific low back pain were retrospectively evaluated for sex-specific differences in cross-sectional sizes of the fifth lumbar vertebrae, psoas and posterior paravertebral muscles, and respective sex- and age-dependent average brightness alterations on T2-weighted axial sections in the L5-level. Results: In women (mean age 33.5 years ± 5.0 (standard deviation)), the investigated musculoskeletal cross-sectional area sizes were significantly smaller (p < 0.001) compared to those in men (mean age 33.0 years ± 5.7). Respective average musculoskeletal brightness values were higher in women compared to those in men, and most pronounced in posterior paravertebral muscles (p < 0.001). By correlating brightness results to those of subcutaneous fat tissue, all intersexual differences, including those between fifth lumbar vertebrae and psoas muscles, turned out to be statistically significant. This phenomenon was least pronounced in psoas muscles. Conclusions: Lumbar musculoskeletal parameters showed significantly larger dimensions of investigated anatomical structures in men compared to those in women aged 20–40 years, and an earlier onset and faster progress of bone loss and muscle degradation in women. Full article
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<p>Example showing measurement schemes of LIC area size and brightness in the fifth lumbar vertebral body, with both psoas and both erector spinae muscles. Axial MRI slice through the lower lumbar spine showing vertebral and muscle structures of a 39-year-old man without spinal pathologies. LIC indicates largest inscribed circle; MRI, magnetic resonance imaging; L5, lumbar vertebra V; PM, psoas muscle; PPVM, posterior paravertebral muscle; MVB, mean vertebral (body) brightness; MPB, mean psoas (muscle) brightness; MPPVB, mean posterior paravertebral (muscle) brightness.</p>
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<p>(<b>a</b>–<b>c</b>) Results from MRI-based LIC area (<b>a</b>) and mean brightness measurements (<b>b</b>) in LV5s, PMs, and PPVMs in 40 women (circles) and 40 men (triangles) ordered by increasing age (x-axis: age in years). Black dashed lines in graphs are trend lines of males’ data points, and grey dotted lines are trend lines of females’ data points. LIC areas were significantly larger in men, and PPVMs were significantly brighter in women. As soon as brightness measurements were correlated to subcutaneous fat tissues (<b>c</b>), significantly higher values resulted for all investigated structures in women. MRI indicates magnetic resonance imaging; LIC, largest inscribed circle; L5, lumbar vertebra V; PM, psoas muscle; PPVM, posterior paravertebral muscle; MVB, mean vertebral (body) brightness; MPB, mean psoas (muscle) brightness; MPPVB, mean posterior paravertebral (muscle) brightness; rel, related to the brightness of subcutaneous fat tissue.</p>
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<p>MRI-based cross-sectional muscle area measurements consisting of LIC areas of PMs and PPVMs at a horizontal plane cut parallel through the upper half of the L5-level in 40 women (circles) and 40 men (triangles) ordered by increasing age. The black dashed line is the trend line of males’ data points, and the grey dotted line is the trend line of females’ data points. Total cross-sectional muscle mass seems to be constant in women, but decreases in men with growing age (x-axis: age in years; y-axis: mm<sup>2</sup>). MRI indicates magnetic resonance imaging; LIC, largest inscribed circle; PM, psoas muscle; PPVM, posterior paravertebral muscle; L5, lumbar vertebra V; mm<sup>2</sup>, square millimeter.</p>
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<p>(<b>a</b>–<b>f</b>) Graphical depiction of means and 95% CIs of cross-sectional vertebral body (<b>a</b>) and muscle dimensions (<b>b</b>–<b>d</b>) at the upper half of the L5-level, and of absolute (<b>e</b>) and relative brightness (<b>f</b>) in corresponding locations, i.e., LV5, PM, PPVM, and total perivertebral muscle mass in 40 women and 40 men aged 20–40 years. L5/LV5 indicates lumbar vertebra V; 95% CI, 95% confidence interval; LIC, largest inscribed circle; mm<sup>2</sup>, square millimeter; PM, psoas muscle; PPVM, posterior paravertebral muscle; MVB, mean vertebral (body) brightness; MPB, mean psoas (muscle) brightness; MPPVB, mean posterior paravertebral (muscle) brightness; MTMB, mean total muscle brightness; rel, related to the brightness of subcutaneous fat tissue.</p>
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15 pages, 14638 KiB  
Article
Mapping the Distribution of Curculio davidi Fairmaire 1878 under Climate Change via Geographical Data and the MaxEnt Model (CMIP6)
by Junhao Wu, Xinju Wei, Zhuoyuan Wang, Yaqin Peng, Biyu Liu and Zhihang Zhuo
Insects 2024, 15(8), 583; https://doi.org/10.3390/insects15080583 - 31 Jul 2024
Viewed by 671
Abstract
Curculio davidi is a major pest in chestnut-producing regions in China, and there have been many studies on its occurrence, biological characteristics, and management strategies. However, few of them have focused on the distribution changes of the pest under climate change. In this [...] Read more.
Curculio davidi is a major pest in chestnut-producing regions in China, and there have been many studies on its occurrence, biological characteristics, and management strategies. However, few of them have focused on the distribution changes of the pest under climate change. In this study, the MaxEnt model (version 3.3.4) and ArcGIS software (version 10.8) were first employed to map the current and future (2050 s and 2080 s) suitable habitat distribution of the weevil under climate change (CMIP 6: SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results indicate that the highly suitable areas for C. davidi are mainly concentrated in Hubei, Henan, Anhui, Jiangxi, Jiangsu, Zhejiang, the coastal areas of Shandong, and eastern Guizhou, northwestern Hunan, and northeastern Sichuan provinces in China. Through the Jackknife test of 19 climate factors, six climate factors affecting the distribution of C. davidi were identified, with precipitation from July (Prec7), precipitation of warmest quarter (Bio18), and temperature seasonality (standard deviation × 100) (Bio4) contributing a combined percentage of 86.3%. Under three different climate scenarios (CMIP 6: SSP1-2.6, SSP2-4.5, and SSP5-8.5), the area of moderately suitable regions is projected to increase by 22.12–27.33% in the 2050 s and by 17.80–38.22% in the 2080 s compared to the current distribution, while the area of highly suitable regions shows a shrinking trend. This study provides data support for the management strategies of C. davidi and offers new insights into the dynamic changes of similar forestry pests. Full article
(This article belongs to the Section Insect Pest and Vector Management)
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<p>Adult of <span class="html-italic">C. davidi</span>.</p>
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<p>Holes in the nutshell of chestnuts. (<b>A</b>): the site of egg laying; (<b>B</b>): Symptoms of <span class="html-italic">C. davidi</span> infestation.</p>
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<p>Geographical distribution points of <span class="html-italic">C. davidi</span> and <span class="html-italic">C. mollissima</span> in China. Pink points: <span class="html-italic">C. davidi</span>; green triangle: <span class="html-italic">C. mollissima</span>; dark blue: average annual low temperature; positive red: average annual high temperature.</p>
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<p>The accuracy of the MaxEnt model on <span class="html-italic">C. davidi</span>. (<b>A</b>): Curve of omission and predicted area; (<b>B</b>): ROC curve of potential distribution prediction.</p>
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<p>Current suitable climatic distribution of <span class="html-italic">C. davidi</span> in China. Red: highly suitable area with a probability of higher than 0.66; orange: moderately suitable area with a probability of 0.33–0.66; yellow: poorly suitable area with a probability ranging from 0.05–0.33; White: unsuitable areas.</p>
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<p>Importance of environmental variables to <span class="html-italic">C. davidi</span> via Jackknife test.</p>
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<p>Response curves of the environmental variables that contributed most to the MaxEnt models. (<b>A</b>): Precipitation of July (prec 7); (<b>B</b>): Precipitation of Warmest Quarter (bio 18); (<b>C</b>): Mean Temperature of Warmest Quarter (bio 10); (<b>D</b>): Precipitation of May (prec 5); (<b>E</b>): Temperature Seasonality (Standard Deviation of × 100) (bio 4); (<b>F</b>): Precipitation Seasonality (Coefficient of Variation) (bio 15).</p>
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<p>Appropriate potential distribution of <span class="html-italic">C. davidi</span> under different climate change scenarios in China. The color level of this area shows the probability of <span class="html-italic">C. davidi</span>, with red indicating that the area is highly suitable for a probability higher than 0.66, orange representing a moderately suitable area with a probability of 0.33–0.66, yellow indicating a poorly suitable area with a probability of 0.05–0.33, and white representing the unsuitable area response curves of the environmental variables that contributed most to the MaxEnt models. (<b>A</b>): SSP1-2.6 (2050S); (<b>B</b>): SSP1-2.6 (2080S); (<b>C</b>): SSP2-4.5 (2050S); (<b>D</b>): SSP2-4.5 (2080S); (<b>E</b>): SSP5-8.5 (2050S); (<b>F</b>): SSP5-8.5 (2080S).</p>
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<p>The area changes of suitable habitat for <span class="html-italic">C. davidi</span> under different climate change scenarios. (<b>A</b>): highly suitability area; (<b>B</b>): moderate suitability area; (<b>C</b>): low suitability area.</p>
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19 pages, 3957 KiB  
Article
A Consensus-Based Likert–LMBP Model for Evaluating the Earthquake Resistance of Existing Buildings
by Burak Oz and Memduh Karalar
Appl. Sci. 2024, 14(15), 6492; https://doi.org/10.3390/app14156492 - 25 Jul 2024
Cited by 1 | Viewed by 725
Abstract
Almost every year, earthquakes threaten many lives, so not only do developing countries suffer negative effects from earthquakes on their economies but also developed ones that lose significant economic resources, suffer massive fatalities, and have to suspend businesses and occupancy. Existing buildings in [...] Read more.
Almost every year, earthquakes threaten many lives, so not only do developing countries suffer negative effects from earthquakes on their economies but also developed ones that lose significant economic resources, suffer massive fatalities, and have to suspend businesses and occupancy. Existing buildings in earthquake-prone areas need structural safety assessments or seismic vulnerability assessments. It is crucial to assess earthquake damage before an earthquake to prevent further losses, and to assess building damage after an earthquake to aid emergency responders. Many models do not take into account the surveyor’s subjectivity, which causes observational vagueness and uncertainty. Additionally, a lack of experience or knowledge, engineering errors, and inconspicuous parameters could affect the assessment. Thus, a consensus-based Likert–LMBP (the Levenberg–Marquardt backpropagation algorithm) model was developed to rapidly assess the seismic performance of buildings based on post-earthquake visual images in the devastating Kahramanmaraş earthquake, which occurred on 6 February 2023 and had magnitudes of 7.7 and 7.6 and severely affected 11 districts in Türkiye. Vulnerability variables for buildings are assessed using linguistic variables on a five-point Likert scale based on expert consensus values derived from post-earthquake visual images. The building vulnerability parameters required for the proposed model are determined as the top hill–slope effect, weak story effect, soft story effect, short column effect, plan irregularity, pounding effect, heavy overhang effect, number of stories, construction year, structural system state, and apparent building quality. Structural analyses categorized buildings as no damage, slight damage, moderate damage, or severe damage/collapse. Training the model resulted in quite good performance (mse = 7.26306 × 10−5). Based on the statistical analysis of the entire data set, the mean and the standard deviation of the errors were 0.00068 and 0.00852, respectively. Full article
(This article belongs to the Special Issue Structural Seismic Design and Evaluation)
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<p>The structure of the consensus-based Likert–ANN model, only the output processing frame was adapted from Hagan et al., 2014 [<a href="#B58-applsci-14-06492" class="html-bibr">58</a>].</p>
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<p>Model’s LMBP training flow adapted from Hagan et al. [<a href="#B58-applsci-14-06492" class="html-bibr">58</a>] and Demuth et al. [<a href="#B61-applsci-14-06492" class="html-bibr">61</a>].</p>
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<p>A view of the model’s input and output values.</p>
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<p>NND design, training parameters, and stopping criteria.</p>
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<p>The model’s initial and final parameters.</p>
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<p>Model’s neural network performance graph.</p>
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<p>Target–output graph after training.</p>
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