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Search Results (23,433)

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22 pages, 1672 KiB  
Article
The Potential of Hydroxyapatite for the Remediation of Lead-Contaminated Territories: A Case Study of Soils in Primorsky Krai
by Svetlana Novikova, Andrei Gilev, Anastasia Brikmans, Igor Priymak, Daria Shlyk, Olga Nesterova and Andrei Egorin
Sustainability 2025, 17(6), 2369; https://doi.org/10.3390/su17062369 - 7 Mar 2025
Abstract
Finding ways to enhance the resilience of soil ecosystems in the context of heavy metal contamination remains an important and urgent challenge. This work is devoted to assessing the impact of the soil composition in Primorsky Krai on the efficiency of using hydroxyapatite [...] Read more.
Finding ways to enhance the resilience of soil ecosystems in the context of heavy metal contamination remains an important and urgent challenge. This work is devoted to assessing the impact of the soil composition in Primorsky Krai on the efficiency of using hydroxyapatite to decrease lead intake into plants. The physicochemical characteristics of Luvic Anthrosol and Gleyic Cambisol and their absorption properties with respect to lead have been studied. Adsorption, distribution of forms, and biotesting were carried out under lead saturation of soils conditions. It has been found that soil composition determines sorption properties and the proportion of mobile lead. The high organic carbon content in Gleyic Cambisol explains its high adsorption capacity and low content of water-soluble lead fraction. The addition of hydroxyapatite reduces the water solubility of lead in Luvic Anthrosol by three orders of magnitude and in the ion mobile form by one order. The capacity of hydroxyapatite decreases by more than thirty times when added to Luvic Anthrosol. With a ratio of hydroxyapatite/soil 0.2, oat germination increases by 18.7%, average seedling length increases by 7 cm, and lead uptake into tissues decreases by 83%. Full article
(This article belongs to the Special Issue Soil Pollution, Soil Ecology and Sustainable Land Use)
22 pages, 643 KiB  
Article
An Interphase Short-Circuit Fault Location Method for Distribution Networks Considering Topological Flexibility
by Hua Xie, Zhe Liu, Kai Li, Qifang Chen, Chao Yang and Tong Li
Processes 2025, 13(3), 782; https://doi.org/10.3390/pr13030782 - 7 Mar 2025
Abstract
The location of faults in distribution networks represents a crucial line of defence, ensuring the safe and reliable operation of these networks. This paper puts forth a methodology for the location of short-circuit faults between phases within the context of a distribution network [...] Read more.
The location of faults in distribution networks represents a crucial line of defence, ensuring the safe and reliable operation of these networks. This paper puts forth a methodology for the location of short-circuit faults between phases within the context of a distribution network information physics system. Firstly, a distribution network topology identification model is constructed, and a switching function based on the characteristics of an interphase short-circuit fault current is constructed to form a physical layer interphase short-circuit preconceived fault set. Subsequently, methodologies for processing information perturbations, including distortion, delay, and failure, are proposed. Fault current information is then extracted to form an information layer fault current array. Ultimately, a similarity function is constructed to correlate the fault characteristics of the physical and information layers. This is achieved through the utilization of the variational bee colony algorithm, which is employed to address the aforementioned issue. The efficacy and suitability of the proposed methodology are assessed in the context of single-point and multi-point faults, dynamic topology alterations, and information perturbations in distribution networks. To this end, a real-world project in Hebei and the IEEE system are employed as illustrative examples. The methodology proposed in this paper can facilitate the rapid and precise location of phase-to-phase short-circuits in physical information systems of distribution networks, thereby enhancing the reliability of power supply in new intelligent distribution networks. Full article
12 pages, 3752 KiB  
Article
Genome-Wide Identification and Expression Pattern of the NAC Gene Family in Panax notoginseng
by Baihui Jin, Xiaolong Hu, Na Li, Xiaohua Li, Zebin Chen, Xinyu Zhao and Xiaoni Wu
Genes 2025, 16(3), 320; https://doi.org/10.3390/genes16030320 - 7 Mar 2025
Abstract
Background: The NAC transcription factor family of genes is one of the largest families of transcription factors in plants, playing important functions in plant growth and development, response to adversity stress, disease resistance, and hormone signaling. In this study, we identified the number [...] Read more.
Background: The NAC transcription factor family of genes is one of the largest families of transcription factors in plants, playing important functions in plant growth and development, response to adversity stress, disease resistance, and hormone signaling. In this study, we identified the number of members of the Panax notoginseng NAC (PnNAC) gene family and conducted a comprehensive analysis of their physicochemical characteristics, chromosomal location, evolutionary features, and expression patterns both in different parts of the plant at different growth stages and in response to infection by Alternaria panax. Methods: The NAC gene family in P. notoginseng was identified using Hidden Markov Model (HMMER) and National Center of Biotechnology Information Conserved Domain Database (NCBI CDD), and their physicochemical properties were analyzed with Perl scripts. Phylogenetic relationships were determined using Clustal Omega and FastTree, and gene structures were visualized with an R script. Promoter regions were analyzed with PlantCARE, motifs with MEME and ggmotif, and transcriptome data were processed using Hical Indexing for Spliced Alignment of Transcripts (HISAT2) and HTseq. Results: This study identified 98 PnNAC genes in P. notoginseng, analyzed their characteristics (protein lengths 104–882 aa, molecular weights 11.78–100.20 kDa, isoelectric points 4.12–9.75), location (unevenly distributed on 12 chromosomes, no tandem repeats), evolution, and expression patterns (distinct in different parts, growth stages, and after A. panax infection). Conclusions: PnNAC plays an important role in the growth and development of P. notoginseng and in its response to A. panax. PnNAC could be a candidate gene for further research on and functional analysis of P. notoginseng disease resistance. Full article
(This article belongs to the Section Plant Genetics and Genomics)
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<p>Chromosome mapping of <span class="html-italic">NAC</span> gene family members of <span class="html-italic">P. notoginseng</span>.</p>
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<p>Phylogenetic tree of the <span class="html-italic">NAC</span> gene family of <span class="html-italic">P. notoginseng</span> and other species.</p>
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<p>Structures of the <span class="html-italic">NAC</span> gene family members of <span class="html-italic">P. notoginseng</span>.</p>
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<p>Motif analysis of the <span class="html-italic">NAC</span> gene family members of <span class="html-italic">P. notoginseng</span>.</p>
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<p>Gene structure and protein conserved domains of the <span class="html-italic">NAC</span> gene family members of <span class="html-italic">P. notoginseng</span>.</p>
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<p>The cis-acting element of the <span class="html-italic">NAC</span> gene family of <span class="html-italic">P. notoginseng</span>.</p>
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<p>Expression patterns of the <span class="html-italic">P. notoginseng NAC</span> gene family members at different sites, stages, and in response to <span class="html-italic">A. panax</span> stress.</p>
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25 pages, 2281 KiB  
Article
Phenological Shifts Since 1830 in 29 Native Plant Species of California and Their Responses to Historical Climate Change
by Andros Solakis-Tena, Noelia Hidalgo-Triana, Ryan Boynton and James H. Thorne
Plants 2025, 14(6), 843; https://doi.org/10.3390/plants14060843 - 7 Mar 2025
Abstract
Climate change is affecting Mediterranean climate regions, such as California. Retrospective phenological studies are a useful tool to track biological response to these impacts through the use of herbarium-preserved specimens. We used data from more than 12,000 herbarium specimens of 29 dominant native [...] Read more.
Climate change is affecting Mediterranean climate regions, such as California. Retrospective phenological studies are a useful tool to track biological response to these impacts through the use of herbarium-preserved specimens. We used data from more than 12,000 herbarium specimens of 29 dominant native plant species that are characteristic of 12 broadly distributed vegetation types to investigate phenological patterns in response to climate change. We analyzed the trends of four phenophases: preflowering (FBF), flowering (F), fruiting (FS) and growth (DVG), over time (from 1830 to 2023) and through changes in climate variables (from 1896 to 2023). We also examined these trends within California’s 10 ecoregions. Among the four phenophases, the strongest response was found in the timing of flowering, which showed an advance in 28 species. Furthermore, 21 species showed sequencing in the advance of two or more phenophases. We highlight the advances found over temperature variables: 10 in FBF, 28 in F, 17 in FS and 18 in DVG. Diverse and less-consistent results were found for water-related variables with 15 species advancing and 11 delaying various phenophases in response to decreasing precipitation and increasing evapotranspiration. Jepson ecoregions displayed a more pronounced advance in F related to time and mean annual temperature in the three of the southern regions compared to the northern ones. This study underscores the role of temperature in driving phenological change, demonstrating how rising temperatures have predominantly advanced phenophase timing. These findings highlight potential threats, including risks of climatic, ecological, and biological imbalances. Full article
(This article belongs to the Section Plant Response to Abiotic Stress and Climate Change)
23 pages, 1596 KiB  
Article
Physicochemical and Functional Properties of Yanbian Cattle Bone Gelatin Extracted Using Acid, Alkaline, and Enzymatic Hydrolysis Methods
by Song Zhang, Duanduan Zhao, Lu Yin, Ruixuan Wang, Zhiyan Jin, Hongyan Xu and Guangjun Xia
Gels 2025, 11(3), 186; https://doi.org/10.3390/gels11030186 - 7 Mar 2025
Abstract
Yanbian cattle, a high-quality indigenous breed in China, were selected due to their unique biological characteristics, underutilized bone byproducts, and potential as a halal-compliant gelatin source, addressing the growing demand for alternatives to conventional mammalian gelatin in Muslim-majority regions. This study investigates the [...] Read more.
Yanbian cattle, a high-quality indigenous breed in China, were selected due to their unique biological characteristics, underutilized bone byproducts, and potential as a halal-compliant gelatin source, addressing the growing demand for alternatives to conventional mammalian gelatin in Muslim-majority regions. This study investigates the physicochemical and functional properties of gelatin extracted from Yanbian cattle bones using three different methods: acid, alkaline, and papain enzymatic hydrolysis. The extraction yields and quality of gelatin were evaluated based on hydroxyproline content, gel strength, viscosity, amino acid composition, molecular weight distribution, and structural integrity. Specifically, A gelatin, prepared using 0.075 mol/L hydrochloric acid, achieved the highest yield (18.64%) among the acid-extraction methods. B gelatin, extracted with 0.1 mol/L sodium hydroxide, achieved the highest yield (21.06%) among the alkaline-extraction methods. E gelatin, obtained through papain hydrolysis, exhibited the highest yield (25.25%) among the enzymatic methods. Gelatin extracted via papain enzymatic hydrolysis not only retained better protein structure but also exhibited higher hydroxyproline content (19.13 g/100 g), gel strength (259 g), viscosity (521.67 cP), and superior thermal stability. Structural analyses conducted using SDS-PAGE, GPC, FTIR, XRD, and CD spectroscopy confirmed that papain extraction more effectively preserved the natural structure of collagen. Furthermore, amino acid composition analysis revealed that gelatin extracted via papain hydrolysis contained higher levels of essential residues, such as glycine, proline, and hydroxyproline, emphasizing the mild and efficient nature of enzymatic treatment. These findings suggest that, compared with acid and alkaline extraction methods, enzymatic hydrolysis has potential advantages in gelatin production. Yanbian cattle bone gelatin shows promise as an alternative source for halal gelatin production. This study also provides insights into optimizing gelatin production to enhance its functionality and sustainability. Full article
(This article belongs to the Special Issue Food Gels: Fabrication, Characterization, and Application)
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<p>Yields (%) extracted from Yanbian cattle bone using different methods. (<b>a</b>) A<sub>0.025</sub> gelatin, A<sub>0.05</sub> gelatin, A<sub>0.075</sub> gelatin, A<sub>0.1</sub> gelatin, A<sub>0.125</sub> gelatin, and A<sub>0.15</sub> gelatin denote the gelatins extracted with different hydrochloric acid concentrations. (<b>b</b>) B<sub>0.025</sub> gelatin, B<sub>0.05</sub> gelatin, B<sub>0.075</sub> gelatin, B<sub>0.1</sub> gelatin, B<sub>0.125</sub> gelatin, and B<sub>0.15</sub> gelatin denote the gelatins extracted with different sodium hydroxide concentrations. (<b>c</b>) E <sub>pepsin</sub> gelatin, E <sub>papain</sub> gelatin, E <sub>ficin</sub> gelatin, E <sub>ginger protease</sub> gelatin, E <sub>composite protease</sub> gelatin, E <sub>bromelain</sub> gelatin, E <sub>trypsin</sub> gelatin, and E <sub>chymotrypsin</sub> gelatin denote the gelatins extracted with different enzymes. Results are presented as mean ± SD (<span class="html-italic">n</span> = 3). Statistical analysis was performed between samples for the same property. Different superscript letters (a–g) within the same color of bar chart indicate statistically significant differences as determined by one-way ANOVA followed by Duncan’s post hoc test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Viscoelastic properties (<b>a</b>–<b>c</b>) of A gelatin, B gelatin, and E gelatin. A gelatin denotes the gelatins extracted with 0.075 mol/L hydrochloric acid. B gelatin denotes the gelatins extracted with 0.1 mol/L sodium hydroxide. E gelatins denote the gelatins extracted with papain. hey are the highest-yielding samples from the three methods (acid method, alkaline method, and enzymatic method), respectively.</p>
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<p>SDS-PAGE patterns: (<b>a</b>) molecular weight distribution and (<b>b</b>) molecular weight ratio (<b>c</b>) of A gelatin, B gelatin, and E gelatin.</p>
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<p>FTIR spectra: (<b>a</b>) XRD patterns and (<b>b</b>) CD spectra (<b>c</b>) of A gelatin, B gelatin, and E gelatin.</p>
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15 pages, 2595 KiB  
Article
Exploring the Impact of COVID-19 on Job Satisfaction Trends: A Text Mining Analysis of Employee Reviews Using the DMR Topic Model
by Jaeyun Kim, Daeho Lee and Yuri Park
Appl. Sci. 2025, 15(6), 2912; https://doi.org/10.3390/app15062912 - 7 Mar 2025
Abstract
Job satisfaction is a critical determinant in talent acquisition and corporate value enhancement. The COVID-19 pandemic has triggered a significant increase in online-based non-face-to-face services and consumption, leading to sustained growth in ICT industry job demand. Given the ICT sector’s heavy reliance on [...] Read more.
Job satisfaction is a critical determinant in talent acquisition and corporate value enhancement. The COVID-19 pandemic has triggered a significant increase in online-based non-face-to-face services and consumption, leading to sustained growth in ICT industry job demand. Given the ICT sector’s heavy reliance on human capital and its growing workforce demands, understanding the evolving factors of job satisfaction in this sector has become increasingly crucial. This study analyzed job satisfaction factors derived from employee reviews on an online job review platform using the Dirichlet Multinomial Regression (DMR) topic model, examining temporal changes in these factors before and after the COVID-19 pandemic. As a result, 25 distinct job satisfaction-related topics were identified, and their temporal distribution patterns were categorized into three trajectories: ascending, descending, and stable. Topics exhibiting ascending patterns included work–life balance, organizational systems, corporate culture, employee benefits, work environment, and software development practices. Conversely, factors demonstrating descending patterns encompassed annual compensation, task characteristics, supervisory relationships, employee treatment, commuting conditions, work-related stress, and welfare programs. The remaining topics maintained relatively stable patterns throughout the observation period. These findings contribute to both academic literature and industry practice by elucidating the evolutionary trends in job satisfaction determinants during the COVID-19 pandemic, thereby facilitating more informed strategic human resource management decisions in the ICT sector. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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<p>Research Framework. Source: Author’s own work.</p>
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<p>Example of collected review.</p>
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<p>Number of reviews after filtering.</p>
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<p>Result of topic coherence.</p>
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<p>DMR model [<a href="#B18-applsci-15-02912" class="html-bibr">18</a>].</p>
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<p>Results of DMR topic modeling.</p>
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28 pages, 18223 KiB  
Article
A Spatiotemporal Dynamic Evaluation of Soil Erosion at a Monthly Scale and the Identification of Driving Factors in Hainan Island Based on the Chinese Soil Loss Equation Model
by Shengling Lin, Yi Zou, Yanhu He, Shiyu Xue, Lirong Zhu and Changqing Ye
Sustainability 2025, 17(6), 2361; https://doi.org/10.3390/su17062361 - 7 Mar 2025
Abstract
The damage caused by soil erosion to global ecosystems is undeniable. However, traditional research methods often do not consider the unique soil characteristics specific to China and rainfall intensity variability in different periods on vegetation, and relatively few research efforts have addressed the [...] Read more.
The damage caused by soil erosion to global ecosystems is undeniable. However, traditional research methods often do not consider the unique soil characteristics specific to China and rainfall intensity variability in different periods on vegetation, and relatively few research efforts have addressed the attribution analysis of soil erosion changes in tropical islands. Therefore, this study applied a modification of the Chinese Soil Loss Equation (CSLE) to evaluate the monthly mean soil erosion modulus in Hainan Island over the past two decades, aiming to assess the potential soil erosion risk. The model demonstrated a relatively high R2, with validation results for the three basins yielding R2 values of 0.77, 0.64, and 0.78, respectively. The results indicated that the annual average soil erosion modulus was 92.76 t·hm−2·year−1, and the monthly average soil erosion modulus was 7.73 t·hm−2·month−1. The key months for soil erosion were May to October, which coincided with the rainy season, having an average erosion modulus of 8.11, 9.41, 14.49, 17.05, 18.33, and 15.36 t·hm−2·month−1, respectively. September marked the most critical period for soil erosion. High-erosion-risk zones are predominantly distributed in the central and eastern sections of the study area, gradually extending into the southwest. The monthly average soil erosion modulus increased with rising elevation and slope. The monthly variation trend in rainfall erosivity factor had a greater impact on soil water erosion than vegetation cover and biological practice factor. The identification of dynamic factors is crucial in areas prone to soil erosion, as it provides a scientific underpinning for monitoring soil erosion and implementing comprehensive water erosion management in these regions. Full article
(This article belongs to the Special Issue Sustainable Agriculture, Soil Erosion and Soil Conservation)
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Figure 1
<p>Geographical location, altitude, and meteorological stations of the study area.</p>
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<p>Spatial arrangement of static factors in Hainan Island. (<b>a</b>) Soil erodibility factor; (<b>b</b>) Slope length factor; (<b>c</b>) Slope steepness factor.</p>
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<p>Monthly mean rainfall erosivity factor from 2003 to 2021.</p>
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<p>Monthly slope trend of rainfall erosivity factors.</p>
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<p>Monthly vegetation cover and biological practices factor in Hainan Island, 2003 to 2021.</p>
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<p>Spatial distribution of tillage practice factor (T). (<b>a</b>) 2003, (<b>b</b>) 2006, (<b>c</b>) 2011, (<b>d</b>) 2016, (<b>e</b>) 2021.</p>
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<p>Monthly soil erosion modulus for each period.</p>
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<p>CSLE model validation results. (<b>a</b>) Longtang station; (<b>b</b>) Baoqiao station; (<b>c</b>) Jiaji station.</p>
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<p>Monthly distribution maps of soil erosion intensity.</p>
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<p>Variations in the soil erosion modulus by month and the dynamic factors throughout the twelve-month period. The soil erosion modulus was denoted by A; mean soil erosion modulus on a monthly basis was expressed as the A-average; rainfall erosivity factor is represented by R; and vegetation cover and biological measures factors are denoted by B. (<b>a</b>) 2003, (<b>b</b>) 2006, (<b>c</b>) 2011, (<b>d</b>) 2016, (<b>e</b>) 2021.</p>
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<p>Erosion conditions across various spatial units on Hainan Island. (<b>a</b>) Soil erosion modulus at different altitudes from 2003 to 2021. (<b>b</b>) Soil erosion modulus of different slopes from 2003 to 2021.</p>
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<p>Land use pattern distribution map of Hainan Island.</p>
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<p>Relative importance of soil erosion factors.</p>
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<p>Spatial distribution of rainfall change contributions to soil erosion.</p>
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<p>Spatial distribution of vegetation change contributions to soil erosion.</p>
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<p>Driving mechanisms of soil erosion change. Note: rainfall erosivity is denoted by R; vegetation cover and biological practice is denoted by B; soil erosion changes are denoted by ΔA; the contribution of vegetation spatial distribution to soil erosion is denoted by ΔA<sub>B</sub>; and the contribution of spatial distribution of rainfall to soil erosion is denoted by ΔA<sub>R</sub>.</p>
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20 pages, 6538 KiB  
Article
The Influence of Wind on the Spatial Distribution of Pelagic Sargassum Aggregations in the Tropical Atlantic
by Marine Laval, Yamina Aimene, Jacques Descloitres, Luc Courtrai, Paulo Duarte-Neto, Adán Salazar-Garibay, Alex Costa da Silva, Pascal Zongo, René Dorville and Cristèle Chevalier
Water 2025, 17(6), 776; https://doi.org/10.3390/w17060776 - 7 Mar 2025
Abstract
Since 2011, Sargassum seaweed has spread widely outside the Sargasso Sea, causing massive strandings on the coasts of the West Indies and Mexico, causing serious economic, ecological, and health problems. This Atlantic pelagic alga has the characteristic of moving in rafts. According to [...] Read more.
Since 2011, Sargassum seaweed has spread widely outside the Sargasso Sea, causing massive strandings on the coasts of the West Indies and Mexico, causing serious economic, ecological, and health problems. This Atlantic pelagic alga has the characteristic of moving in rafts. According to in situ observations, their size and shape can vary with the wind. To better understand the effect of wind on Sargassum coverage and aggregation size, we conducted a large temporal (2019–2022) and spatial scale study in the West Indies using OLCI/Sentinel-3 satellite imagery. During this period, a database of nearly 1 million Sentinel-3 aggregations, including their geometric and wind characteristics, was established. Analysis of the size distribution showed that wind has a dual effect on disaggregation and agglomeration depending on wind speed and aggregation size: (1) low winds favor agglomeration for the smallest aggregations and disaggregation for the largest aggregations; (2) high winds favor disaggregation for all aggregation sizes. In addition, topography also plays a role in size distribution: the Caribbean arc favors agglomeration over offshore zones, and coastal areas favor disaggregation over offshore zones. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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Figure 1
<p>Zones of the study area: Caribbean Zone (CZ), Atlantic Zone (AZ), Caribbean Antilles Zone (CAZ), Atlantic Antilles Zone (AAZ), and the coastal zone. Land areas are shown in dark green.</p>
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<p>Temporal variation in the <span class="html-italic">Sargassum</span> fraction (green line) and its moving mean (black line) in the whole study area between 2019 and 2022. Periods of low <span class="html-italic">Sargassum</span> fraction with a moving mean below 0.02 (dashed red horizontal line) are shown in gray.</p>
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<p>Diagram of a <span class="html-italic">Sargassum</span> aggregation and the characteristics derived from its reference ellipse: major axis, minor axis, and center.</p>
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<p>Boxplot of <span class="html-italic">Sargassum</span> fraction as a function of cloud fraction. “+” indicates the outlier data not considered in the boxplot estimation.</p>
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<p>The number of images for each wind class, for the different sampling of the satellite images: (1) sample outside the <span class="html-italic">Sargassum</span> period, high cloud fraction, and low <span class="html-italic">Sargassum</span> abundance; (2) after deletion of classes unrepresentative of water and <span class="html-italic">Sargassum</span> fraction.</p>
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<p>Moving mean of wind speed from 2019 to 2022, outside the coastal zone, for the whole study area (dashed black line), the Caribbean zone (CZ + CAZ; solid blue line) with its standard deviation (solid blue zone), and the Atlantic zone (AZ + AAZ, solid red line) with its standard deviation (solid red zone).</p>
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<p>Monthly distribution of cloud fraction for 2019, 2020, 2021, and 2022, illustrated by boxplot. “+” indicates the outlier data not considered in the boxplot estimation.</p>
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<p>Moving mean of <span class="html-italic">Sargassum</span> fraction from 2019 to 2022 for the entire study area (dashed black line), the Caribbean zone (CZ + CAZ; solid dark blue line), and the Atlantic zone (AZ + AAZ, solid light blue line).</p>
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<p>Boxplot of the ratio W/L distribution as a function of the area classes corresponding to the distribution quantiles and deciles.</p>
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<p>Distribution of the area of <span class="html-italic">Sargassum</span> aggregations represented by (i) the histogram of the area classes (gray bars) and its probability density function (gray line), indicated by the left y-axis, and (ii) dots representing the distribution of the area classes on a logarithmic scale indicated in the right y-axis. The red and blue points correspond to those used to estimate the linear regressions, represented by the dashed red and blue lines, respectively.</p>
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<p>Values of (<b>a</b>) the mode, (<b>b</b>) the left slope, and (<b>c</b>) the right slope of the surface area distribution as a function of the zones (Coastal, CZ, AZ, AAZ, and CAZ).</p>
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<p>Average <span class="html-italic">Sargassum</span> fraction in each wind class (black line) and per month from January to September (yellow to blue lines). The gray envelope represents the standard deviation of the average <span class="html-italic">Sargassum</span> fraction.</p>
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<p>Mode (<b>a</b>) and absolute value (<b>b</b>) of the left-slope (dots) and right-slope (squares) of the surface area distribution for each wind class.</p>
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<p>Water, cloud (left y-axis), and <span class="html-italic">Sargassum</span> (right y-axis) fraction in each wind speed class over the <span class="html-italic">Sargassum</span> period. <span class="html-italic">Sargassum</span> fraction from <a href="#water-17-00776-f012" class="html-fig">Figure 12</a>; dots represent the <span class="html-italic">Sargassum</span> fraction discarded in wind classes due to insufficient data (<a href="#water-17-00776-f005" class="html-fig">Figure 5</a>).</p>
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<p>Schematic representation of the evolution of the size distribution with wind, from a lower wind speed (black curve) to a higher wind speed (red curve) for (<b>a</b>) low to moderate wind conditions, from 2 to 9 m·s<sup>−1</sup> and for (<b>b</b>) high wind conditions, from 9 to 12 m·s<sup>−1</sup>. The background colour indicates the dominant process: disaggregation (blue) or agglomeration (green).</p>
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<p>Density plot of the area of <span class="html-italic">Sargassum</span> aggregations as a function of their length (<b>a</b>) and width (<b>b</b>), presented by the logarithmic scale axis. The color scale represents the probability density. Area, length, and width are from the entire dataset. The black line represents a second-order polynomial fitting the distribution of the <span class="html-italic">Sargassum</span> area as a function of their length (<b>a</b>) and (<b>b</b>) width (R<sup>2</sup> = 0.97 and 0.96, respectively).</p>
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18 pages, 7319 KiB  
Article
Parametric Aerodynamic Study of Galloping Piezoelectric Energy Harvester with Arcuate Protruding and Depressed Features
by Xiaokang Yang, Bingke Xu, Zhendong Shang, Chunyang Liu, Haichao Cai and Xiangyi Hu
Sensors 2025, 25(6), 1657; https://doi.org/10.3390/s25061657 - 7 Mar 2025
Abstract
This study explores the potential effect of a cross-sectional shape with an arcuate protruding and depressed features on the performance. The geometric configurations include two feature types (protruding and depressed), each with six distinct perimeter arrangements and three depths per arrangement, yielding thirty-six [...] Read more.
This study explores the potential effect of a cross-sectional shape with an arcuate protruding and depressed features on the performance. The geometric configurations include two feature types (protruding and depressed), each with six distinct perimeter arrangements and three depths per arrangement, yielding thirty-six different cross-sectional shapes for systematic evaluation. The aerodynamic characteristics and electrical performance are numerically analyzed, using a computational fluid dynamics model and a distributed parameter electromechanical coupling model, respectively. A smooth protruding feature on the front, top, or bottom side suppresses the electrical output; however, when located on the rear side, it significantly increases the slope of the power versus wind speed curve. Depressed features on the rear, top, or bottom side only reduce the critical wind speed and the power enhancement positively correlates with the feature depth. Compared to a square, a harvester with depressed feature on both top and bottom sides exhibits a significant jump in power at the critical wind speed, greatly improving the power. These findings provide important design guidelines for structural optimization of galloping piezoelectric energy harvesters, enabling them to match the wind energy distribution characteristics of specific regions with optimal performance. Full article
(This article belongs to the Special Issue Energy Harvesting and Self-Powered Sensors)
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<p>Schematic of the GPEH and the configuration of its cross-sectional shape.</p>
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<p>(<b>a</b>) Experimental setup; (<b>b</b>) power versus load; (<b>c</b>) power versus frequency.</p>
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<p>(<b>a</b>) Flow chart of the simulation process; (<b>b</b>) experimental and numerical results for the prototype; (<b>c</b>) comparison of results from the proposed model and the previous study by Yang [<a href="#B9-sensors-25-01657" class="html-bibr">9</a>].</p>
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<p>(<b>a</b>) Computational mesh; comparison of the results in time domain: (<b>b</b>) <span class="html-italic">C<sub>L</sub></span>, (<b>c</b>) <span class="html-italic">C<sub>D</sub></span>.</p>
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<p>Numerical coefficients versus the angle of attack for protruding and depressed features on the front and/or rear sides: (<b>a</b>) <span class="html-italic">C<sub>L</sub></span>, (<b>b</b>) <span class="html-italic">C<sub>D</sub></span>, and (<b>c</b>) <span class="html-italic">C<sub>Fy</sub></span> for the protruding feature; (<b>d</b>) <span class="html-italic">C<sub>L</sub></span>, (<b>e</b>) <span class="html-italic">C<sub>D</sub></span>, and (<b>f</b>) <span class="html-italic">C<sub>Fy</sub></span> for the depressed feature.</p>
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<p>Numerical coefficients versus the angle of attack for protruding and depressed features on the front and/or rear sides: (<b>a</b>) <span class="html-italic">C<sub>L</sub></span>, (<b>b</b>) <span class="html-italic">C<sub>D</sub></span>, and (<b>c</b>) <span class="html-italic">C<sub>Fy</sub></span> for the protruding feature; (<b>d</b>) <span class="html-italic">C<sub>L</sub></span>, (<b>e</b>) <span class="html-italic">C<sub>D</sub></span>, and (<b>f</b>) <span class="html-italic">C<sub>Fy</sub></span> for the depressed feature.</p>
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<p>Numerical coefficients versus the angle of attack for protruding and depressed features on the top and/or bottom sides: (<b>a</b>) <span class="html-italic">C<sub>L</sub></span>, (<b>b</b>) <span class="html-italic">C<sub>D</sub></span>, and (<b>c</b>) <span class="html-italic">C<sub>Fy</sub></span> for the protruding feature; (<b>d</b>) <span class="html-italic">C<sub>L</sub></span>, (<b>e</b>) <span class="html-italic">C<sub>D</sub></span>, and (<b>f</b>) <span class="html-italic">C<sub>Fy</sub></span> for the depressed feature.</p>
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<p>Numerical power versus wind speed: protruding features on (<b>a</b>) the front side only, (<b>b</b>) the rear side only, and (<b>c</b>) both the front and rear sides; depressed features on (<b>d</b>) the front side only, (<b>e</b>) the rear side only, and (<b>f</b>) both the front and rear sides.</p>
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<p>Numerical power versus wind speed: protruding features on (<b>a</b>) the top or bottom side only, and (<b>b</b>) both the top and bottom sides; depressed features on (<b>c</b>) the top or bottom side only, and (<b>d</b>) both the top and bottom sides.</p>
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27 pages, 6155 KiB  
Article
Construction and Zoning of Ecological Security Patterns in Yichang City
by Qi Zhang, Yi Sun, Diwei Tang, Hu Cheng and Yi Tu
Sustainability 2025, 17(6), 2354; https://doi.org/10.3390/su17062354 - 7 Mar 2025
Abstract
The study of ecological security patterns is of great significance to the balance between regional economic development and environmental protection. By optimizing the regional ecological security pattern through reasonable land-use planning and resource management strategies, the purpose of maintaining ecosystem stability and improving [...] Read more.
The study of ecological security patterns is of great significance to the balance between regional economic development and environmental protection. By optimizing the regional ecological security pattern through reasonable land-use planning and resource management strategies, the purpose of maintaining ecosystem stability and improving ecosystem service capacity can be achieved, and ultimately regional ecological security can be achieved. As a typical ecological civilization city in the middle reaches of the Yangtze River, Yichang City is also facing the dual challenges of urban expansion and environmental pressure. The construction and optimization of its ecological security pattern is the key to achieving the harmonious coexistence of economic development and environmental protection and ensuring regional sustainable development. Based on the ecological environment characteristics and land-use data of Yichang City, this paper uses morphological spatial pattern analysis and landscape connectivity analysis to identify core ecological sources, constructs a comprehensive ecological resistance surface based on the sensitivity–pressure–resilience (SPR) model, and combines circuit theory and Linkage Mapper tools to extract ecological corridors, ecological pinch points, and ecological barrier points and construct the ecological security pattern of Yichang City with ecological elements of points, lines, and surfaces. Finally, the community mining method was introduced and combined with habitat quality to analyze the spatial topological structure of the ecological network in Yichang City and conduct ecological security zoning management. The following conclusions were drawn: Yichang City has a good ecological background value. A total of 64 core ecological sources were screened out with a total area of 3239.5 km². In total, 157 ecological corridors in Yichang City were identified. These corridors were divided into 104 general corridors, 42 important corridors, and 11 key corridors according to the flow centrality score. In addition, 49 key ecological pinch points and 36 ecological barrier points were identified. The combination of these points, lines, and surfaces formed the ecological security pattern of Yichang City. Based on the community mining algorithm in complex networks and the principle of Thiessen polygons, Yichang City was divided into five ecological functional zones. Among them, Community No. 2 has the highest ecological security level, high vegetation coverage, close distribution of ecological sources, a large number of corridors, and high connectivity. Community No. 5 has the largest area, but it contains most of the human activity space and construction and development zones, with low habitat quality and severely squeezed ecological space. In this regard, large-scale ecological restoration projects should be implemented, such as artificial wetland construction and ecological island establishment, to supplement ecological activity space and mobility and enhance ecosystem service functions. This study aims to construct a multi-scale ecological security pattern in Yichang City, propose a dynamic zoning management strategy based on complex network analysis, and provide a scientific basis for ecological protection and restoration in rapidly urbanizing areas. Full article
24 pages, 8784 KiB  
Article
Genome-Wide Identification of GLK Family Genes in Phoebe bournei and Their Transcriptional Analysis Under Abiotic Stresses
by Yiran Lian, Liang Peng, Xinying Shi, Qiumian Zheng, Dunjin Fan, Zhiyi Feng, Xiaomin Liu, Huanhuan Ma, Shijiang Cao and Weiyin Chang
Int. J. Mol. Sci. 2025, 26(6), 2387; https://doi.org/10.3390/ijms26062387 - 7 Mar 2025
Abstract
GOLDEN2-LIKE (GLK) transcription factors are crucial regulators of chloroplast development and stress responses in plants. In this study, we investigated the GLK gene family in Phoebe bournei (Hemsl.) Yen C. Yang, a near-threatened species important for forestry and wood utilization in China. We [...] Read more.
GOLDEN2-LIKE (GLK) transcription factors are crucial regulators of chloroplast development and stress responses in plants. In this study, we investigated the GLK gene family in Phoebe bournei (Hemsl.) Yen C. Yang, a near-threatened species important for forestry and wood utilization in China. We identified 61 PbGLK genes which were classified into seven subfamilies. Our analyses of their phylogenetic relationships, gene structures, and chromosomal distribution revealed diverse characteristics. Expression profiling under different tissues and abiotic stresses showed that PbGLK25 and PbGLK30 were particularly responsive to drought, heat, light, and shade stresses, with significant upregulation. These findings highlight the potential role of PbGLK genes in stress adaptation and provide insights for the genetic improvement of P. bournei. Full article
(This article belongs to the Special Issue Advances in Plant Genomics and Genetics: 2nd Edition)
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<p>Distribution of <span class="html-italic">PbGLK</span> genes in <span class="html-italic">P. bournei</span> chromosomes. The chromosome number is shown on the left side of each chromosome. The scale on the left can be used to assess chromosomal length and gene position.</p>
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<p>Phylogenetic tree of three plants’ GLK proteins. The different colored arcs represent different subfamilies of GLK proteins. The tree was built using 61 PbGLKs from <span class="html-italic">P. bournei</span>, 64 AtGLKs from <span class="html-italic">A. thaliana</span>, and 66 TcGLKs from <span class="html-italic">Theobroma cacao</span>. MEGA 11.0 was used to create a maximum likelihood phylogenetic tree, and the Bootstrap test replicate was set to 1000. All GLK proteins were divided into seven classes, with each class represented by a different color: Class A is represented by red, Class B is represented by yellow, Class C is represented by light blue, Class D is represented by dark brown, Class E is represented by light brown, Class F is represented by dark green, and Class G is represented by dark blue.</p>
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<p>Protein motifs, domains, and structures of <span class="html-italic">GLK</span> gene family in <span class="html-italic">P. bournei.</span> (<b>A</b>) Class A is represented by red, Class B is represented by yellow, Class C is represented by light blue, Class D is represented by dark brown, Class E is represented by light brown, Class F is represented by dark green, and Class G is represented by dark blue. Phylogenetic tree constructed in MEGA using Maximum Likelihood algorithm using Bootstrap with 1000 replications. (<b>B</b>) Protein motifs in PbGLK members. The colorful boxes delineate different motifs. (<b>C</b>) Analysis of functional conserved domains was performed in the Pfam database (<a href="http://pfam.janelia.org/" target="_blank">http://pfam.janelia.org/</a>). (<b>D</b>) Gene structures of <span class="html-italic">PbGLK</span> gene family. CDS and UTR displayed using yellow and green rectangles, respectively. Black lines denote introns.</p>
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<p>Schematic diagram of <span class="html-italic">cis</span>-element locations. The <span class="html-italic">cis</span>-element prediction of 61 <span class="html-italic">PbGLK</span> gene promoter sequences (−2000 bp) was analyzed using PlantCARE (<a href="https://bioinformatics.psb.ugent.be/webtools/plantcare/html/" target="_blank">https://bioinformatics.psb.ugent.be/webtools/plantcare/html/</a>, accessed on 10 October 2024).</p>
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<p>Analysis of inter- and intra-chromosomal fragment duplication of <span class="html-italic">GLK</span> genes in the <span class="html-italic">P</span>. <span class="html-italic">bournei</span> genome. The gray lines represent all synthetic blocks, and the colored lines specifically indicate the duplicated pairs among the 61 <span class="html-italic">PbGLK</span> genes. Tandem duplicated genes are set off by a red background.</p>
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<p>Synteny analysis of the genomes in <span class="html-italic">P. bournei</span>, <span class="html-italic">Oryza sativa</span> (<b>A</b>), and <span class="html-italic">A. thaliana</span> (<b>B</b>). Gray lines indicate collinear blocks within <span class="html-italic">P. bournei</span> and other plant genomes; the red line indicates collinear gene pairs.</p>
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<p>Expression profiles of 61 <span class="html-italic">PbGLK</span> genes across various tissues depicted in a schematic diagram, where red blocks indicate high expression levels and white blocks signify low expression levels.</p>
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<p>The expression profiles of <span class="html-italic">PbGLK</span> genes in <span class="html-italic">P</span>. <span class="html-italic">bournei</span> were detected via qRT-PCR in response to drought, heat, light, and shade stress. (<b>A</b>) Relative gene expression levels under heat stress over the same time period (0, 4, 8, 12, and 24 h). (<b>B</b>) Relative gene expression levels under drought treatment over the same time period (0, 4, 8, 12, and 24 h). (<b>C</b>) Relative gene expression levels under light stress (0, 24, 48, and 24 h). (<b>D</b>) Relative gene expression levels under shade stress (0, 12, 24, 48, and 72 h). Significant differences (<span class="html-italic">p</span> &lt; 0.05) were determined by the LSD test, expressed by different letters above the bar. Different letters indicate significant differences between groups, while the same letters indicate no significant differences between groups.</p>
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<p>The expression profiles of <span class="html-italic">PbGLK</span> genes in <span class="html-italic">P</span>. <span class="html-italic">bournei</span> were detected via qRT-PCR in response to drought, heat, light, and shade stress. (<b>A</b>) Relative gene expression levels under heat stress over the same time period (0, 4, 8, 12, and 24 h). (<b>B</b>) Relative gene expression levels under drought treatment over the same time period (0, 4, 8, 12, and 24 h). (<b>C</b>) Relative gene expression levels under light stress (0, 24, 48, and 24 h). (<b>D</b>) Relative gene expression levels under shade stress (0, 12, 24, 48, and 72 h). Significant differences (<span class="html-italic">p</span> &lt; 0.05) were determined by the LSD test, expressed by different letters above the bar. Different letters indicate significant differences between groups, while the same letters indicate no significant differences between groups.</p>
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<p>Prediction model of <span class="html-italic">GLK</span> pathway in chlorophyll biosynthesis of <span class="html-italic">P. bournei</span>. HEMA1 (glutamyl-tRNA reductase [GluTR]) initiates the crucial and rate-determining step in the biosynthesis of tetrapyrroles, CHLH (the H subunit of Mg-chelatase) plays a pivotal role in channeling tetrapyrrole molecules towards the chlorophyll production pathway, GUN4 is required for efficient Mg-chelatase activity, and CAO catalyzes the conversion of chlorophyllide a to chlorophyllide b.</p>
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16 pages, 5239 KiB  
Article
The Distribution Characteristics and Genesis Analysis of Overpressure in the Qiongzhusi Formation in the Zizhong Area, Sichuan Basin
by Xuewen Shi, Yuran Yang, Qiuzi Wu, Yanyou Li, Yifan He, He Tian, Zhenxue Jiang, Zhongyu Bi and Huan Miao
Appl. Sci. 2025, 15(6), 2888; https://doi.org/10.3390/app15062888 - 7 Mar 2025
Abstract
Accurately predicting the genesis and distribution of reservoir pressure is essential for comprehending the distribution of oil and gas reservoirs while mitigating drilling risks. In the Qiongzhusi Formation of the Sichuan Basin, overpressure has developed, leading to high production levels in several wells. [...] Read more.
Accurately predicting the genesis and distribution of reservoir pressure is essential for comprehending the distribution of oil and gas reservoirs while mitigating drilling risks. In the Qiongzhusi Formation of the Sichuan Basin, overpressure has developed, leading to high production levels in several wells. However, the distribution and causal mechanism of overpressure within the Qiongzhusi Formation remain unclear at present. This study utilizes logging data from representative drilling wells to identify the causes of overpressure in the Qiongzhusi Formation and predict the characteristics of pressure distribution. The results indicate that the pressure coefficient of the Qiongzhusi Formation ranges from 1.01 to 2.05 and increases with burial depth. The overpressure in the Qiongzhusi Formation is attributed to fluid expansion, disequilibrium compaction, and pressure transmission. The contribution of disequilibrium compaction to pressure is 9.44 MPa, while hydrocarbon generation from organic matter contributes 82.66 MPa, and pressure transmission contributes 37.98 MPa. Additionally, the uplift erosion unloading effect and geothermal decline result in pressure reductions of approximately 26.68 MPa and 56.56 MPa, respectively. This study systematically elucidates the causes and distribution of overpressure in the Qiongzhusi Formation, providing valuable insights for subsequent exploration and development of shale gas in this formation. Full article
(This article belongs to the Section Energy Science and Technology)
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<p>Geological background of the research area: (<b>a</b>) Geographical location of Sichuan Basin; (<b>b</b>) Geographical location of Zizhong area; (<b>c</b>) Contour map of mudstone thickness in Qiongzhusi Formation in Zizhong area; (<b>d</b>) Stratigraphic chart of Qiongzhusi Formation (modified after [<a href="#B18-applsci-15-02888" class="html-bibr">18</a>,<a href="#B19-applsci-15-02888" class="html-bibr">19</a>]).</p>
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<p>Characteristics of Pressure in the Qiongzhusi Formation: (<b>a</b>) Pressure vs. depth; (<b>b</b>) Pressure coefficient vs. depth.</p>
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<p>Typical drilling pressure prediction results of the Qiongzhusi Formation: (<b>a</b>) Z201; (<b>b</b>) GS17; (<b>c</b>) W207; (<b>d</b>) MX9.</p>
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<p>The prediction results of pressure in Qiongzhusi Formation: (<b>a</b>) plane distribution of pressure coefficient; (<b>b</b>) A–A’ pressure coefficient profile.</p>
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<p>Overpressure identification chart and overpressure cause identification of Qiongzhusi Formation: (<b>a</b>) resistivity–density plate; (<b>b</b>) sonic velocity–density plate; (<b>c</b>,<b>d</b>) overpressure cause identification of Z201; (<b>e</b>,<b>f</b>) overpressure cause identification of GS17; (<b>g</b>,<b>h</b>) overpressure cause identification of WY1H.</p>
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<p>Plate of Bowers method and overpressure cause identification of Qiongzhusi Formation: (<b>a</b>,<b>b</b>) Plate of Bowers method; (<b>c</b>,<b>d</b>) overpressure cause identification of Z201; (<b>e</b>,<b>f</b>) overpressure cause identification of GS17; (<b>g</b>,<b>h</b>) overpressure cause identification of WY1H.</p>
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<p>The contribution of different causes of overpressure to the pressure of the Qiongzhusi Formation: (<b>a</b>) disequilibrium compaction; (<b>b</b>) pressure transmission; (<b>c</b>) uplift erosion unloading effect; (<b>d</b>) geothermal decline.</p>
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22 pages, 994 KiB  
Article
A Ray-Tracing-Based Irradiance Model for Agrivoltaic Greenhouses: Development and Application
by Anna Kujawa, Natalie Hanrieder, Stefan Wilbert, Álvaro Fernández Solas, Sergio González Rodríguez, María del Carmen Alonso-García, Jesús Polo, José Antonio Carballo, Guadalupe López-Díaz, Cristina Cornaro and Robert Pitz-Paal
Agronomy 2025, 15(3), 665; https://doi.org/10.3390/agronomy15030665 - 7 Mar 2025
Viewed by 87
Abstract
A key challenge in designing agrivoltaic systems is avoiding or minimizing the negative impact of photovoltaic-induced shading on crops. This study introduces a novel ray-tracing-based irradiance model for evaluating the irradiance distribution inside agrivoltaic greenhouses taking into account the transmission characteristics of the [...] Read more.
A key challenge in designing agrivoltaic systems is avoiding or minimizing the negative impact of photovoltaic-induced shading on crops. This study introduces a novel ray-tracing-based irradiance model for evaluating the irradiance distribution inside agrivoltaic greenhouses taking into account the transmission characteristics of the greenhouse’s cover material. Simulations are based on satellite-derived irradiance data and are performed with high spatial and temporal resolution. The model is tested by reproducing the agrivoltaic greenhouse experiment of a previous study and comparing the simulated irradiance to the experimentally measured data. The coordinates of the sensor positions in the presented application are optimized based on one day of raw data of minutely measured irradiance from the experimental study. These coordinates are then used to perform simulations over an extended timeframe of several months to take into account the seasonal changes throughout a crop cycle. The average deviation between the simulations and the experimental measurements in terms of radiation reduction is determined as 2.88 percentage points for the entire crop cycle. Full article
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<p>Rendering of the experimental Venlo greenhouse (GH) implemented in the simulation framework. The estimated position of radiation sensors are indicated by stars.</p>
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<p>Overall transmittance of <tt>Radiance</tt> <math display="inline"><semantics> <mrow> <mi>t</mi> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>s</mi> </mrow> </semantics></math> materials depending on <math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>p</mi> <mi>e</mi> <mi>c</mi> </mrow> </msub> </semantics></math>. The color scheme relates to the overall transmittance of the tested material, i.e., the ratio of transmitted irradiance to incident irradiance.</p>
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<p>Schematic picture of the ray tracing procedure for an agrivoltaic (APV) GH. Specular reflection (red), diffuse reflection (blue), specular transmission (red), and diffuse transmission (green) are shown. The sky dome is indicated with the dotted semicircle, and the sun is indicated with a yellow circle.</p>
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<p>Minimization of normalized Root Mean Square Error (nRMSE) values to find the coordinates of the radiation sensors in the simulation. The nRMSE values for the west sensor in the 0% control zone are presented. For that sensor, the pair of x- and y-coordinates with the lowest nRMSE of 0.10 is located at <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mo>−</mo> <mn>0.25</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>y</mi> <mo>=</mo> <mo>−</mo> <mn>0.20</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> according to the primary guess of the position.</p>
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<p>Comparison between simulation and experimentally measured irradiance by López-Díaz et al. [<a href="#B41-agronomy-15-00665" class="html-bibr">41</a>] for 23 January 2015. The plots on the left refer to the sensors placed on the west of the GH; the plots on the right refer to the sensors placed on the east. The zones are presented in ascending order, with the 0% zone in the first row and 50% in the last row. Red stars: simulation; blue line: irradiance measured by López-Díaz et al. [<a href="#B41-agronomy-15-00665" class="html-bibr">41</a>]; green points: 15 min means of experimentally measured data with <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> <mi>σ</mi> </mrow> </semantics></math> standard deviation; black curve: outside Global Horizontal Irradiance (GHI) from Copernicus Atmosphere Monitoring Service (CAMS) used as model’s input.</p>
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<p>Overall radiation reduction with respect to the control zone as a function of shading treatments of 15%, 30% and 50% for the simulation and experimental values of López-Díaz et al. [<a href="#B41-agronomy-15-00665" class="html-bibr">41</a>]. A 2nd-degree polynomial function is fitted to the simulated data (<math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">R</mi> <mn>2</mn> </msup> <mo>&gt;</mo> <mn>0.99</mn> </mrow> </semantics></math>).</p>
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<p>Computation times for timestamp 23.01.2015 at 12:00:00 for varying numbers of pixels.</p>
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25 pages, 14178 KiB  
Article
Research on the Spatial Differentiation Pattern of High-Temperature Disaster Resilience and Strategies for Enhancing Resilience: A Case Study of Hangzhou, China
by Shanfeng Zhang, Yilin Xu, Hao Wu, Wenting Wu and Yuhao Lou
Sustainability 2025, 17(6), 2338; https://doi.org/10.3390/su17062338 - 7 Mar 2025
Viewed by 68
Abstract
With the intensification of climate change and urbanization, the impact of high-temperature disasters on urban resilience has become increasingly significant. Based on the “Pressure-State-Response” (PSR) model, this study proposes a novel assessment method for urban high-temperature disaster resilience. Through 15 evaluation indicators across [...] Read more.
With the intensification of climate change and urbanization, the impact of high-temperature disasters on urban resilience has become increasingly significant. Based on the “Pressure-State-Response” (PSR) model, this study proposes a novel assessment method for urban high-temperature disaster resilience. Through 15 evaluation indicators across 3 categories, we quantified the high-temperature disaster resilience level in Hangzhou and constructed a SOM-K-means second-order clustering algorithm to classify the study area into different resilience zones, exploring the spatial differentiation characteristics of high-temperature disaster resilience. The research results indicate the following: (1) Hangzhou exhibits a relatively low level of high-temperature disaster resilience, with a spatial distribution pattern showing a radial decrease from the main city area at the center, followed by a slight increase in the far periphery of the main city area. (2) The study area was divided into four distinct high-temperature disaster resilience zones, demonstrating significant spatial differentiation characteristics. This study innovatively integrates the PSR model with the SOM-K-means clustering method, providing a new perspective for the quantitative assessment and spatial zoning of urban high-temperature disaster resilience. The findings offer valuable decision-making support for enhancing urban resilience. Full article
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<p>Study area and temperature period distribution map.</p>
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<p>Experimental flowchart.</p>
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<p>The “Pressure-State-Response” process of a city disturbed by high temperature.</p>
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<p>Flow chart of spatial partitioning of high-temperature disaster resilience based on SOM-K-means.</p>
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<p>Portrait of resilience assessment indicators for high-temperature disasters in Hangzhou.</p>
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<p>Spatial distribution pattern of high-temperature disaster resilience in Hangzhou (grid level).</p>
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<p>Spatial distribution pattern of high-temperature disaster resilience in Hangzhou (street/town level).</p>
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<p>Spatial zoning of high-temperature disaster resilience in Hangzhou.</p>
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15 pages, 2964 KiB  
Article
Do Faster-Growing Holoparasitic Plant Species Exhibit Broader Niches and Wider Global Distributions?
by Quanzhong Zhang and Jinming Hu
Plants 2025, 14(6), 831; https://doi.org/10.3390/plants14060831 - 7 Mar 2025
Viewed by 165
Abstract
Parasitic organisms, as an important component of ecosystems, have long been a focal point in ecological research, particularly concerning the relationship between their growth characteristics, ecological niche, and distribution patterns. This study selects the holoparasitic plant species Cuscuta campestris Yunck., Cuscuta australis R.Br., [...] Read more.
Parasitic organisms, as an important component of ecosystems, have long been a focal point in ecological research, particularly concerning the relationship between their growth characteristics, ecological niche, and distribution patterns. This study selects the holoparasitic plant species Cuscuta campestris Yunck., Cuscuta australis R.Br., and Cuscuta chinensis Lam. from the Cuscuta subgenus Grammica as model species to explore the relationship between the growth rate, ecological niche breadth, and global distribution patterns of parasitic plants. Through greenhouse experiments and data analysis, the main findings of this study indicate a strong positive correlation between the growth rate, ecological niche breadth, number of global occurrence points, and global distribution area for C. campestris, C. australis, and C. chinensis. The significant correlation between growth rate and ecological niche breadth suggests that the intrinsic growth characteristics of parasitic plants may significantly influence their realized ecological niche. Furthermore, the experimental results show that when C. campestris, C. australis, and C. chinensis parasitize non-native hosts from the Americas, they produce greater biomass than when parasitizing native hosts from China. In conclusion, this study provides new support for ecological theories regarding species adaptability, distribution patterns, and environmental influences, and offers directions for future research. Full article
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Figure 1

Figure 1
<p>Stem biomass produced by <span class="html-italic">C. chinensis</span>, <span class="html-italic">C. campestris</span>, and <span class="html-italic">C. australis</span> parasitising eight different host species. Panels (<b>A</b>–<b>H</b>) represent the parasitism of the three <span class="html-italic">Cuscuta</span> species from the subg. <span class="html-italic">Grammica</span> on the corresponding host species, as follows: (<b>A</b>) <span class="html-italic">Bidens pilosa</span>; (<b>B</b>) <span class="html-italic">Bidens biternata</span>; (<b>C</b>) <span class="html-italic">Ageratina adenophora</span>; (<b>D</b>) <span class="html-italic">Eupatorium heterophyllum</span>; (<b>E</b>) <span class="html-italic">Solidago canadensis</span>; (<b>F</b>) <span class="html-italic">Solidago decurrens</span>; (<b>G</b>) <span class="html-italic">Phytolacca americana</span>; (<b>H</b>) <span class="html-italic">Phytolacca acinosa</span>. The significance level was set at <span class="html-italic">p</span> &lt; 0.05. The lines in the bar charts represent the standard error of the mean (SEM) for each data group. The lowercase letters on each bar represent the significance between groups. If a bar shares the same letter with another bar, it indicates that there is no significant difference between them. If all the letters on one bar differ from those on another bar, it indicates a significant difference between the two.</p>
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<p>Total biomass of subg. <span class="html-italic">Grammica</span> species parasitizing non-native American hosts and native hosts from China. (<b>A</b>) represents the species pair <span class="html-italic">B. pilosa</span> and <span class="html-italic">B. biternata</span>; (<b>B</b>) represents the species pair <span class="html-italic">A. adenophora</span> and <span class="html-italic">E. heterophyllum</span>; (<b>C</b>) represents the species pair <span class="html-italic">S. canadensis</span> and <span class="html-italic">S. decurrens</span>; (<b>D</b>) represents the species pair <span class="html-italic">P. americana</span> and <span class="html-italic">P. acinosa</span>. The red bar represents the total biomass of subg. <span class="html-italic">Grammica</span> species parasitizing non-native American hosts. The blue bar represents the total biomass of subg. <span class="html-italic">Grammica</span> species parasitizing native hosts. Statistical significance was set at <span class="html-italic">p</span> &lt; 0.05. The lines in the bar charts represent the standard error of the mean (SEM) for each group of data. The lowercase letters on each bar represent the significance between groups. If a bar shares the same letter with another bar, it indicates that there is no significant difference between them. If all the letters on one bar differ from those on another bar, it indicates a significant difference between the two.</p>
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<p>Relative total biomass of hosts in the uninfected (control) and infected groups. Panels (<b>A</b>–<b>H</b>) represent 8 host species, as follows: (<b>A</b>) <span class="html-italic">B. pilosa</span>; (<b>B</b>) <span class="html-italic">B. biternata</span>; (<b>C</b>) <span class="html-italic">A. adenophora</span>; (<b>D</b>) <span class="html-italic">E. heterophyllum</span>; (<b>E</b>) <span class="html-italic">S. canadensis</span>; (<b>F</b>) <span class="html-italic">S. decurrens</span>; (<b>G</b>) <span class="html-italic">P. americana</span>; (<b>H</b>) <span class="html-italic">P. acinosa</span>. The red bar represents the relative total biomass of hosts in the uninfected (control) group. The blue bar represents the relative total biomass of hosts in the infected group. Statistical significance was set at <span class="html-italic">p</span> &lt; 0.05. The lines in the bar charts represent the standard error of the mean (SEM) for each group of data. The lowercase letters on each bar represent the significance between groups. If a bar shares the same letter with another bar, it indicates that there is no significant difference between them. If all the letters on one bar differ from those on another bar, it indicates a significant difference between the two.</p>
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<p>Niche breadth of <span class="html-italic">C. chinensis</span>, <span class="html-italic">C. campestris</span>, and <span class="html-italic">C. australis</span> in the environmental factor PCA space.</p>
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<p>Scatterplot matrix showing the relationships between growth rate, niche breadth, number of global occurrence points, and global distribution area for <span class="html-italic">C. chinensis</span>, <span class="html-italic">C. australis</span>, and <span class="html-italic">C. campestris</span>. And the asterisk represents a strong correlation.</p>
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