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24 pages, 15927 KiB  
Article
Research on Energy Dissipation Mechanism of Hump Characteristics Based on Entropy Generation and Coupling Excitation Mechanism of Internal Vortex Structure of Waterjet Pump at Hump Region
by Min Liu, Yun Long, Hong Yin, Chenbiao Tian and Jinqing Zhong
J. Mar. Sci. Eng. 2025, 13(3), 442; https://doi.org/10.3390/jmse13030442 - 26 Feb 2025
Viewed by 100
Abstract
High-speed mixed-flow and axial-flow pumps often exhibit hump or double-hump patterns in flow–head curves. Operating in the hump region can cause flow disturbances, increased vibration, and noise in pumps and systems. Variable-speed ship navigation requires waterjet propulsion pumps to adjust speeds. Speed transitions [...] Read more.
High-speed mixed-flow and axial-flow pumps often exhibit hump or double-hump patterns in flow–head curves. Operating in the hump region can cause flow disturbances, increased vibration, and noise in pumps and systems. Variable-speed ship navigation requires waterjet propulsion pumps to adjust speeds. Speed transitions can lead pumps into the hump region, impacting efficient and quiet operation. This paper focuses on mixed-flow waterjet propulsion pumps with guide vanes. Energy, entropy production, and flow characteristic analyses investigate hump formation and internal flow properties. High-speed photography in cavitation experiments focuses on increased vibration and noise in the hump region. This study shows that in hump formation, impeller work capacity decreases less than internal fluid loss in the pump. These factors lead to an abnormal increase in the energy curve. The impeller blades show higher pressure at peak conditions than in valley conditions. Valley conditions show more pressure and velocity distribution variance in impeller flow passages, with notable low-pressure areas. This research aids in understanding pump hump phenomena, addressing flow disturbances, vibration, noise, and supporting design optimization. Full article
(This article belongs to the Section Ocean Engineering)
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Figure 1
<p>Schematic diagram of waterjet propulsion pump structure.</p>
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<p>Diagram of hump phenomenon.</p>
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<p>The effect of the hump region on the hydrodynamic performance of the pump [<a href="#B16-jmse-13-00442" class="html-bibr">16</a>,<a href="#B17-jmse-13-00442" class="html-bibr">17</a>,<a href="#B18-jmse-13-00442" class="html-bibr">18</a>,<a href="#B19-jmse-13-00442" class="html-bibr">19</a>].</p>
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<p>Components of the test model pump.</p>
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<p>Schematic of axial- or mixed-flow pump test loop [<a href="#B20-jmse-13-00442" class="html-bibr">20</a>].</p>
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<p>Installation diagram of the test pump section.</p>
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<p>Test site for the model pump.</p>
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<p>Numerical simulation results of two computational fluid domain schemes [<a href="#B21-jmse-13-00442" class="html-bibr">21</a>].</p>
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<p>Computational grid.</p>
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<p>Local grids of hydraulic components.</p>
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<p>Composition of grid numbers for different schemes and their CFD results [<a href="#B22-jmse-13-00442" class="html-bibr">22</a>].</p>
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<p>Analysis of energy characteristic curves. (<b>a</b>) Energy coefficient curve. (<b>b</b>) Slope change of energy coefficient curve. (<b>c</b>) Deflection change. (<b>d</b>) Variation of deflection contribution factor.</p>
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<p>Analysis of energy characteristic curves. (<b>a</b>) Energy coefficient curve. (<b>b</b>) Slope change of energy coefficient curve. (<b>c</b>) Deflection change. (<b>d</b>) Variation of deflection contribution factor.</p>
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<p>Analysis of energy characteristic curves in the hump region. (<b>a</b>) The slope change of the energy coefficient curve in the hump region. (<b>b</b>) The variation of the deflection contribution factor in the hump region.</p>
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<p>Distribution of entropy generation in pump.</p>
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<p>Distribution of entropy generation in pump.</p>
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<p>The average load distribution in different spans.</p>
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<p>Pressure fluctuations of different blades of the impeller under valley operating condition.</p>
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<p>Pressure fluctuations of different blades of the impeller under peak operating condition.</p>
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<p>The pressure distribution under valley operating and peak operating conditions.</p>
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<p>Streamline distribution of inside the pump under valley and peak operating conditions.</p>
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<p>The axial cross-section streamline distribution under valley operating and peak operating conditions.</p>
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<p>The pressure distribution within the impeller at span = 0.7 under valley operating conditions.</p>
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<p>The cavitation flow structure and its evolution law under the critical cavitation stage in the near valley operating condition.</p>
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<p>The cavitation flow structure and its evolution law under the critical cavitation stage in the near peak operating condition.</p>
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19 pages, 2114 KiB  
Article
Exploring Burnout at the Morgue During the COVID-19 Pandemic: A Three-Phase Analysis of Forensic and Pathology Personnel
by Lilioara-Alexandra Oprinca-Muja, Adrian-Nicolae Cristian, Elena Topîrcean, Alina Cristian, Marius Florentin Popa, Roxana Cardoș, George-Călin Oprinca, Diter Atasie, Cosmin Mihalache, Mihaela Dana Bucuță and Silviu Morar
Healthcare 2025, 13(5), 504; https://doi.org/10.3390/healthcare13050504 - 26 Feb 2025
Viewed by 83
Abstract
Background/Objectives: Burnout is a critical concern among healthcare professionals, particularly during crises such as the COVID-19 pandemic. This study investigated burnout levels among forensic medicine and pathology personnel at three distinct phases: the early pandemic period (Phase 1—September 2020), the peak of [...] Read more.
Background/Objectives: Burnout is a critical concern among healthcare professionals, particularly during crises such as the COVID-19 pandemic. This study investigated burnout levels among forensic medicine and pathology personnel at three distinct phases: the early pandemic period (Phase 1—September 2020), the peak of the pandemic (Phase 2—October 2021), and the post-pandemic period (Phase 3—October 2024). Methods: A total of 37 participants employed in forensic medicine and pathology departments completed the Maslach Burnout Inventory (MBI). A one-way repeated measures ANOVA was conducted to assess within-subject differences over time. Normality and sphericity were tested using the Shapiro–Wilk test and Mauchly’s test, with the Greenhouse-Geisser correction. Post hoc Bonferroni-adjusted comparisons identified significant differences, and partial eta squared (η2) was reported for effect sizes. Results: Results showed significant fluctuations in burnout levels across the three phases. Emotional exhaustion and low personal accomplishment peaked during Phase 2, with slight reductions observed in Phase 3. Gender differences were evident, with females reporting higher EE levels and males exhibiting higher depersonalization across all phases. Marital and parental status also influenced burnout levels, with unmarried individuals and those without children showing higher burnout scores. Medical doctors experienced the highest burnout levels among professional roles, while auxiliary staff showed significant challenges in the PA subscale. Conclusions: The COVID-19 pandemic was pivotal in exacerbating burnout levels due to increased workload, crisis decision-making, and emotional toll. Although the sample size is limited, these findings underscore the importance of implementing targeted interventions to mitigate burnout among forensic and pathology personnel, especially during healthcare emergencies. Gender-based differences in burnout suggest the necessity of specific workplace well-being strategies, while the protective role of family status demonstrates the importance of work-life balance policies. The persistence of psychological distress after a medical crisis calls for long-term monitoring and support programs. There is a need for improved workload distribution, peer support networks, and mental health training to build resilience among forensic and pathology personnel. Full article
(This article belongs to the Special Issue Burnout and Mental Health among Health Professionals)
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<p>Burnout differences between male and female workers.</p>
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<p>Burnout differences between married and unmarried workers.</p>
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<p>Burnout differences between workers with and without children in care.</p>
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<p>Burnout differences between different professional groups.</p>
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27 pages, 5221 KiB  
Article
Adaptive AI-Driven Toll Management: Enhancing Traffic Flow and Sustainability Through Real-Time Prediction, Allocation, and Task Optimization
by Satendra Chandra Pandey and Vasanthi Kumari P
Future Transp. 2025, 5(1), 21; https://doi.org/10.3390/futuretransp5010021 - 26 Feb 2025
Viewed by 104
Abstract
Efficient toll processing is critical for mitigating traffic congestion and enhancing transportation network efficiency at toll stations. This study explores the Neelamangala Toll Plaza on India’s National Highway 48, employing artificial intelligence (AI) to optimize toll operations. The research integrates a Supervised Learning [...] Read more.
Efficient toll processing is critical for mitigating traffic congestion and enhancing transportation network efficiency at toll stations. This study explores the Neelamangala Toll Plaza on India’s National Highway 48, employing artificial intelligence (AI) to optimize toll operations. The research integrates a Supervised Learning (SL) time series model for traffic prediction and a Reinforcement Learning (RL) framework based on a Markov Decision Process (MDP), coupled with a randomized algorithm for equitable task distribution. These AI-driven models dynamically adapt to real-time traffic conditions, preventing peak-hour system overload. Key performance metrics—Average Processing Time (APT), Queue Length Reduction (QLR), and Throughput (TP) were used to evaluate the system. Research also demonstrates the model’s superior performance in handling high traffic volumes and reducing congestion. The study underscores the potential of integrating AI and randomized algorithms in modern toll management, offering a scalable and adaptive solution for sustainable transportation infrastructure. Full article
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<p>Neelmangala Toll Plaza NH48 Bengaluru to Pune.</p>
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<p>Model of traditional toll collection at Neelmangala Toll Plaza.</p>
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<p>Workflow of AI-integrated Neelmangala Toll Plaza.</p>
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<p>ARIMA model forecast graph displaying observed traffic volume predicted values, and confidence intervals.</p>
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<p>Simulated toll plaza Model with AI-integrated Neelmangala Toll Plaza.</p>
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<p>Proposed dataflow of AI-integrated Neelmangala Toll Plaza.</p>
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<p>Graphical representation of the selected duration of traffic volume over time.</p>
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<p>Graphical representation of queue length distribution.</p>
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<p>Graphical representation of seasonal traffic trends.</p>
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<p>Graphical representation of traditional vs. AI enabled methods.</p>
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<p>Graphical representation of traditional vs. AI enabled methods.</p>
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<p>Graphical representation of traditional vs. AI-enabled methods.</p>
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<p>Graphical representation of traditional vs. AI-enabled methods.</p>
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<p>User satisfaction trends: traditional vs. AI-enabled methods.</p>
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<p>Error reduction metrics: Before vs after AI implementation.</p>
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<p>Comparative analysis of the result: AI system vs conventional methods.</p>
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29 pages, 15732 KiB  
Article
Exploration of the Optimal Spark Plug Position and the Effect of Ignition Timing on the Combustion and Emission Characteristics of Opposed Rotary Piston Engines
by Jilong Song, Xiaopan Li, Jianbing Gao, Jian Gao, Meng Zhao, Yufeng Wang, Xiaochen Wang, Mingxu Qi, Guohong Tian, Yunxi Shi and Zhongwei Meng
Processes 2025, 13(3), 657; https://doi.org/10.3390/pr13030657 - 26 Feb 2025
Viewed by 161
Abstract
The opposed rotary piston (ORP) engine, distinguished by its exceptional power-to-weight ratio and uncomplicated design, serves as an optimal power system for Unmanned Aerial Vehicles (UAVs). Based on the three-dimensional simulation platform, the engine performance, combustion, and emission characteristics of the ORP engine [...] Read more.
The opposed rotary piston (ORP) engine, distinguished by its exceptional power-to-weight ratio and uncomplicated design, serves as an optimal power system for Unmanned Aerial Vehicles (UAVs). Based on the three-dimensional simulation platform, the engine performance, combustion, and emission characteristics of the ORP engine at different speeds and ignition timings are clearly clarified. A larger angle of the spark plug position corresponds to a wider ignition timing range and higher power output. However, this increases the likelihood of engine knock. The optimal position of the spark plug is 18 deg before top dead center 2 (TDC2). As the ignition timing is advanced, both the pressure and temperature within the cylinder rise, and the crank angle associated with the peak values shifts nearer to TDC2. As the ignition timing shifts from −13.4 °CA to −22.8 °CA, the maximum in-cylinder pressure rises from 35.5 bar to 59.6 bar at 3000 r/min. The delayed ignition at a given ignition timing range accelerates flame formation due to a higher in-cylinder pressure at ignition. Advanced ignition can significantly enhance engine power and lower fuel consumption, substantially improving the endurance of UAVs. At 3000 r/min, the peak power, 36.3 kW, and minimal ISFC, 231.1 g/kWh, are achieved at an ignition timing of −22.8 °CA. Advanced ignition results in a wider flame propagation region, effectively avoiding incomplete combustion in the combustion chamber corners under high-speed engine conditions. The distribution of NOx closely follows the high-temperature region, with more accumulation observed in the opposite direction of rotation. Advanced ignition contributes substantially to HC emission reduction in the combustion chamber. Full article
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<p>ORP engine structure (<b>a</b>–<b>e</b>).</p>
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<p>Piston structure of ORP engine.</p>
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<p>Operating principle of ORP engine.</p>
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<p>Fluid domain and grid model of ORP engine model (<b>a</b>–<b>e</b>) [<a href="#B50-processes-13-00657" class="html-bibr">50</a>].</p>
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<p>Grid model of ORP engine.</p>
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<p>Grid sensitivity analysis.</p>
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<p>Verification of spray model.</p>
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<p>Piston and cylinder rotation angle.</p>
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<p>The pressure and pressure rise rate under variable speed.</p>
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<p>The pressure and pressure rise rate under variable speed.</p>
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<p>The indicated power and ITE of the ORP engine.</p>
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<p>In-cylinder pressure and pressure rise rate under maximum ignition delay.</p>
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<p>The pressure and pressure rise at variable engine speed and ignition timing.</p>
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<p>P–V diagrams of the ORP engine at different ignition timings.</p>
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<p>Pressure at the end of the expansion stroke at different speeds and ignition timings.</p>
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<p>Unburned fuel mass and combustion phase at various speeds and ignition timings.</p>
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<p>Unburned fuel mass and combustion phase at various speeds and ignition timings.</p>
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<p>Accumulated heat release and heat release rates.</p>
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<p>Energy distribution of ORP engine.</p>
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<p>Indicated power and ISFC under different speeds and ignition timings.</p>
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<p>In-cylinder flame propagation at different rotating speeds.</p>
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<p>Mass concentration of OH at different speeds.</p>
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<p>In-cylinder temperature at different ignition timings.</p>
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<p>NO<sub><span class="html-italic">x</span></sub> concentration under different ignition timings.</p>
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<p>The NO<sub><span class="html-italic">x</span></sub> distribution under different ignition timings at 3000 r/min.</p>
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<p>HC and CO emissions of ORP engine under different ignition timings.</p>
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<p>HC and CO emissions of ORP engine under different ignition timings.</p>
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24 pages, 21665 KiB  
Article
Effects of Emission Variability on Atmospheric CO2 Concentrations in Mainland China
by Wenjing Lu, Xiaoying Li, Shenshen Li, Tianhai Cheng, Yuhang Guo and Weifang Fang
Remote Sens. 2025, 17(5), 814; https://doi.org/10.3390/rs17050814 - 26 Feb 2025
Viewed by 27
Abstract
Accurately assessing the impact of anthropogenic carbon dioxide (CO2) emissions on CO2 concentrations is essential for understanding regional climate change, particularly in high-emission countries like China. This study employed the GEOS-Chem chemical transport model to simulate and compare the spatiotemporal [...] Read more.
Accurately assessing the impact of anthropogenic carbon dioxide (CO2) emissions on CO2 concentrations is essential for understanding regional climate change, particularly in high-emission countries like China. This study employed the GEOS-Chem chemical transport model to simulate and compare the spatiotemporal distributions of XCO2 of three anthropogenic CO2 emission inventories in mainland China for the 2018–2020 period and analyzed the effects of emission variations on atmospheric CO2 concentrations. In eastern China, particularly in the Yangtze River Delta (YRD) and Beijing-Tianjin-Hebei (BTH) regions, column-averaged dry air mole fractions of CO2 (XCO2) can exceed 420 ppm during peak periods, with emissions from these areas contributing significantly to the national total. The simulation results were validated by comparing them with OCO-2 satellite observations and ground-based monitoring data, showing that more than 70% of the monitoring stations exhibited a correlation coefficient greater than 0.7 between simulated and observed data. The average bias relative to satellite observations was less than 1 ppm, with the Emissions Database for Global Atmospheric Research (EDGAR) showing the highest degree of agreement with both satellite and ground-based observations. During the study period, anthropogenic CO2 emissions resulted in an increase in XCO2 exceeding 10 ppm, particularly in the North China Plain and the YRD. In scenarios where emissions from either the BTH or YRD regions were reduced by 50%, a corresponding decrease of 1 ppm in XCO2 was observed in the study area and its surrounding regions. These findings underscore the critical role of emission control policies in mitigating the rise in atmospheric CO2 concentrations in densely populated and industrialized areas. This research elucidates the impacts of variations in anthropogenic emissions on the spatiotemporal distribution of atmospheric CO2 and emphasizes the need for improved accuracy of CO2 emission inventories. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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<p>Spatial distribution of emissions (gC/m<sup>2</sup>/d) in mainland China from three inventories in 2018–2020: (<b>a</b>) ODIAC, (<b>b</b>) MEIC and (<b>c</b>) EDGAR, with zoomed-in views of the BTH and YRD regions in the right panel.</p>
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<p>Time series of monthly anthropogenic CO<sub>2</sub> emissions over mainland China from 2018 to 2020 based on three emission inventories: ODIAC, MEIC, and EDGAR.</p>
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<p>Distribution of mean XCO<sub>2</sub> for three emission inventories (ODIAC, MEIC, and EDGAR) simulations (first column) and discrepancy between each of the three simulations and the mean (second to fourth columns) in 2018–2020 (ANN: annual; MAM: March–April-May; JJA: June–July–August; SON: September–October–November; DJF: December–February).</p>
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<p>Bias (XCO<sub>2</sub>_OCO minus XCO<sub>2</sub>_Sim) distributions of satellite XCO<sub>2</sub> observations and model simulations for ANN, MAM, JJA, SON and DJF in 2020, with the first, second, and third columns showing the differences between observations and simulations based on the ODIAC, MEIC, and EDGAR inventories ((<b>a</b>) Obs minus Sim_ODIAC, (<b>b</b>) Obs minus Sim_MEIC, and (<b>c</b>) Obs minus Sim_EDGAR).</p>
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<p>Geophysical locations of the selected sites.</p>
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<p>The Taylor diagram comparing the XCO<sub>2</sub> simulated by the three anthropogenic emission inventories based on GEOS-Chem with the Ground-based CO<sub>2</sub> observations in the 2018–2020 period. The radial coordinates represent the standard deviation ratio between the simulated CO<sub>2</sub> and the observed CO<sub>2</sub> in each stie, while the angular coordinates indicate the correlation coefficient. The gray dashed lines denote the centered root mean square error (RMSE-c).</p>
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<p>Spatial distribution of average differences between simulations from inventories and no_fossil simulations ((<b>a</b>) Sim_ODIAC minus Sim_no_fossil, (<b>b</b>) Sim_MEIC minus Sim_no_fossil and (<b>c</b>) Sim_EDGAR minus Sim_no_fossil) in 2018–2020.</p>
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<p>The distribution of average XCO<sub>2</sub> discrepancy between the adjusted emission scenarios ((<b>a</b>) +20%, (<b>b</b>) −20%, (<b>c</b>) +50%, (<b>d</b>) −50%, (<b>e</b>) +100%, and (<b>f</b>) −100% scenarios) and the baseline experiment (Sim_EDGAR) for 2020.</p>
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<p>The distribution of XCO<sub>2</sub> average discrepancy between the adjusted emission scenarios (solely on the BTH region or YRD region, (<b>a</b>) +20%, (<b>b</b>) −20%, (<b>c</b>) +50%, (<b>d</b>) −50%, (<b>e</b>) +100%, and (<b>f</b>) −100% scenarios) and the baseline experiment (Sim_EDGAR) in 2020.</p>
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<p>Scatter plots of the OCO-2 XCO<sub>2</sub> and GEOS-Chem simulated XCO<sub>2</sub> for the year 2020.</p>
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<p>Heatmap of Spearman’s correlation coefficients between anthropogenic CO<sub>2</sub> emissions (using the EDGAR inventory) and simulated XCO<sub>2</sub> concentrations for 2018–2020. Blank regions over mainland China indicate areas where the correlation did not meet statistical significance.</p>
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15 pages, 10623 KiB  
Article
Optical Transitions Dominated by Orbital Interactions in Two-Dimensional Fullerene Networks
by Haonan Bai, Xinwen Gai, Yi Zou and Jingang Wang
C 2025, 11(1), 17; https://doi.org/10.3390/c11010017 (registering DOI) - 25 Feb 2025
Viewed by 165
Abstract
Fullerenes are a class of highly symmetric spherical carbon materials that have attracted significant attention in optoelectronic applications due to their excellent electron transport properties. However, the isotropy of their spherical structure often leads to disordered inter-sphere stacking in practical applications, limiting in-depth [...] Read more.
Fullerenes are a class of highly symmetric spherical carbon materials that have attracted significant attention in optoelectronic applications due to their excellent electron transport properties. However, the isotropy of their spherical structure often leads to disordered inter-sphere stacking in practical applications, limiting in-depth studies of their electron transport behavior. The successful fabrication of long-range ordered two-dimensional fullerene arrays has opened up new opportunities for exploring the structure–activity relationship in spatial charge transport. In this study, theoretical calculations were performed to analyze the effects of different periodic arrangements in two-dimensional fullerene arrays on electronic excitation and optical behavior. The results show that HLOPC60 exhibits a strong absorption peak at 1050 nm, while TLOPC60 displays prominent absorption features at 700 nm and 1300 nm, indicating that their electronic excitation characteristics are significantly influenced by the periodic structure. Additionally, analyses of orbital distribution and the spatial electron density reveal a close relationship between carrier transport and the structural topology. Quantitative studies further indicate that the interlayer interaction energies of the HLOPC60 and TLOPC60 arrangements are −105.65 kJ/mol and −135.25 kJ/mol, respectively. TLOPC60 also exhibits stronger dispersion interactions, leading to enhanced interlayer binding. These findings provide new insights into the structural regulation of fullerene materials and offer theoretical guidance for the design and synthesis of novel organic optoelectronic materials. Full article
(This article belongs to the Special Issue High-Performance Carbon Materials and Their Composites)
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Graphical abstract
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<p>Structural diagram of HLOPC<sub>60</sub> (<b>A</b>) and TLOPC<sub>60</sub> (<b>B</b>).</p>
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<p>(<b>A</b>) Absorption spectrum of HLOPC<sub>60</sub>. S<sub>3</sub> (<b>B</b>), S<sub>19</sub> (<b>C</b>), S<sub>70</sub> (<b>D</b>), S<sub>82</sub> (<b>E</b>) and S<sub>144</sub> (<b>F</b>) are the CDDs of each excited state. The red and blue isosurfaces represent the electron and hole, respectively.</p>
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<p>(<b>A</b>) Absorption spectrum of TLOPC<sub>60</sub>. S<sub>29</sub> (<b>B</b>), S<sub>50</sub> (<b>C</b>), S<sub>134</sub> (<b>D</b>), S<sub>152</sub> (<b>E</b>) and S<sub>176</sub> (<b>F</b>) are the CDDs of each excited state. The red and blue isosurfaces represent the electron and hole, respectively.</p>
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<p>Empty orbitals (<b>A</b>) and occupied orbitals (<b>B</b>) that make major contributions in each excited state of HLOPC<sub>60</sub>.</p>
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<p>Empty orbitals (<b>A</b>) and occupied orbitals (<b>B</b>) that make major contributions in each excited state of TLOPC<sub>60</sub>.</p>
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<p>(<b>A</b>) Real-space functions (electron density (i), energy density (ii), potential energy density (iii), Laplacian electron density (iv), Hamiltonian kinetic energy (v), and electron localization function (vi)) at critical points. Grey and red represent HLOPC<sub>60</sub> and TLOPC<sub>60</sub>, respectively. (<b>B</b>) Schematic representation of bond critical points (blue), ring critical points (red), and cage critical points (green) in HLOPC<sub>60</sub> and TLOPC<sub>60</sub>, where (i–iii) are three different directions of HLOPC<sub>60</sub>; (iv–vi) are the three different directions of TLOPC<sub>60</sub>.</p>
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<p>(<b>A</b>) Van der Waals potential of HLOPC<sub>60</sub> and TLOPC<sub>60</sub>. The He atom is the probe atom. The blue isosurface represents regions where the van der Waals potential is significantly negative, and the small green ball is the van der Waals potential minimum point. (<b>B</b>) Interlayer interactions between bilayer HLOPC<sub>60</sub> and TLOPC<sub>60</sub>.</p>
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19 pages, 12391 KiB  
Article
Investigation into Enhancing Ultrasonic Cleaning Efficiency Through Symmetrical Transducer Configuration
by Lei Wei, Sheng Liu and Fang Dong
Symmetry 2025, 17(3), 348; https://doi.org/10.3390/sym17030348 - 25 Feb 2025
Viewed by 110
Abstract
This paper investigates the symmetrical layout effect in ultrasonic cleaning via acoustic solid coupling simulation, with emphasis on how the symmetrical arrangement of transducers influences sound pressure distribution. Two specific transducer layout methods are examined: uniform arrangement at the bottom and symmetrical arrangement [...] Read more.
This paper investigates the symmetrical layout effect in ultrasonic cleaning via acoustic solid coupling simulation, with emphasis on how the symmetrical arrangement of transducers influences sound pressure distribution. Two specific transducer layout methods are examined: uniform arrangement at the bottom and symmetrical arrangement along the sides. The findings indicate that when the tank length is an integer multiple of one-quarter of the acoustic wavelength, the symmetrical side arrangement markedly enhances the sound pressure level within the tank and optimizes the propagation and reflection of acoustic waves. In megasonic cleaning, focusing is achieved through a 7 × 7 transducer array by precisely controlling the phase, and the symmetrical arrangement ensures uniform sound pressure distribution. By integrating 1 MHz megasonic sources from both focused and unfocused configurations, the overall sound pressure distribution and peak sound pressure at the focal point are calculated using multi-physics field coupling simulations. A comparative analysis of the sound fields generated by focused and unfocused sources reveals that the focused source can produce significantly higher sound pressure in specific regions. Leveraging the enhanced cleaning capability of the focused acoustic wave in targeted areas while maintaining broad coverage with the unfocused acoustic wave significantly improves the overall cleaning efficiency. Ultrasonic cleaning finds extensive applications in industries such as electronic component manufacturing, medical device sterilization, and automotive parts cleaning. Its efficiency and environmental friendliness make it highly significant for both daily life and industrial production. Full article
(This article belongs to the Section Engineering and Materials)
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<p>Schematic diagram of acoustic cavitation collapse.</p>
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<p>Shows the evolution process of a single bubble radius under the influence of an acoustic field (<b>a</b>) Different radii (<b>b</b>) Theoretical simulation comparison (<b>c</b>) Different sound pressures (<b>d</b>) Resonant frequency.</p>
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<p>(<b>a</b>) Aluminum foil corrosion test results [<a href="#B29-symmetry-17-00348" class="html-bibr">29</a>] (<b>b</b>) Two types of ultrasonic cleaning models.</p>
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<p>Model of the flow field diagram.</p>
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<p>The thickness of the boundary layer decreases as the frequency increases [<a href="#B30-symmetry-17-00348" class="html-bibr">30</a>] (<b>a</b>) Acoustic boundary layer (<b>b</b>) Thickness of acoustic boundary layer.</p>
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<p>Megasonic cleaning [<a href="#B25-symmetry-17-00348" class="html-bibr">25</a>].</p>
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<p>Comparison before and after cleaning [<a href="#B25-symmetry-17-00348" class="html-bibr">25</a>]. (<b>a</b>) Ultrasonic cleaning. (<b>b</b>) Megasonic cleaning.</p>
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<p>(<b>a</b>) Unfocused megasonic source (Model a) (<b>b</b>) Focused megasonic source (Model b).</p>
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<p>Non-focused sound sources combined with focused sound sources (<b>a</b>) Model c (<b>b</b>) Model d.</p>
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<p>The distribution of the sound pressure and sound pressure level at 40 kHz (<b>a</b>) Model 1 (<b>b</b>) Model 2 (<b>c</b>) Model 3.</p>
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<p>The pressure exerted on the silicon wafers in the cleaning tank. (<b>a</b>) Model 1 (<b>b</b>) Model 2. (<b>c</b>) Model 3.</p>
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<p>The distribution of the sound pressure and sound pressure level on the horizontal cross section.</p>
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<p>Sound pressure distribution (<b>a</b>) Model a (<b>b</b>) Model b.</p>
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<p>The distribution of the cross-sectional sound pressure and the values of sound pressure along the cross-sectional line (<b>a</b>) Model a (<b>b</b>) Model b.</p>
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<p>The pressure exerted on the silicon wafer (<b>a</b>) Model a (<b>b</b>) Model b.</p>
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<p>Sound pressure distribution (<b>a</b>) Model c (<b>b</b>) Model d.</p>
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<p>The distribution of the cross-sectional sound pressure and the values of the sound pressure along the cross-sectional line (<b>a</b>) Model c (<b>b</b>) Model d.</p>
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<p>The pressure exerted on the silicon wafe (<b>a</b>) Model c (<b>b</b>) Model d.</p>
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18 pages, 5990 KiB  
Article
The Influence of Roof Opening and Closure on the Overall Wind Pressure Distribution of Airport Terminal Roof
by Mingjie Li, Xiaomin Zhang, Yuxuan Bao, Jiwei Lin, Cheng Pei, Xiaokang Cheng and Cunming Ma
Buildings 2025, 15(5), 735; https://doi.org/10.3390/buildings15050735 - 25 Feb 2025
Viewed by 116
Abstract
This article investigates the effects of roof opening and closure conditions on the mean and fluctuating wind pressure coefficient of the roof surface through rigid model wind tunnel tests and further explores the non-Gaussian characteristics of wind pressure (skewness, kurtosis, and wind pressure [...] Read more.
This article investigates the effects of roof opening and closure conditions on the mean and fluctuating wind pressure coefficient of the roof surface through rigid model wind tunnel tests and further explores the non-Gaussian characteristics of wind pressure (skewness, kurtosis, and wind pressure probability density) under the two conditions. Then, based on the non-Gaussian characteristics under two working conditions, this paper constructs a Hermite moment model to solve the peak factor of the roof surface to evaluate the impact of roof opening and closure on the most unfavorable extreme wind pressure. The research results show that under the two working conditions of roof opening and closure, the windward leading edge’s mean and fluctuating wind pressure coefficients change most significantly, leading to an increase in the degree of flow separation at the windward leading edge. This causes the skewness, kurtosis, and probability density function of the wind pressure at the windward leading edge of the roof to deviate significantly from the standard Gaussian distribution, exhibiting strong non-Gaussian characteristics. Meanwhile, based on the Hermite moment model, it is found that the peak factor of most measuring points is concentrated between 3.5 and 5.0 under both roof opening and closure conditions, significantly higher than the recommended value of 2.5 in GB 50009-2012. In addition, under roof opening, the most unfavorable negative pressure coefficient is −4.54, and the absolute value of its most unfavorable negative pressure extreme is 1.3% higher than the roof opening closure condition. Full article
(This article belongs to the Special Issue Wind Load Effects on High-Rise and Long-Span Structures: 2nd Edition)
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<p>Some domestic airport terminals have suffered wind-induced damage incidents: (<b>a</b>) Capital Airport (China); (<b>b</b>) Changbei Airport (China).</p>
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<p>The diagram and experimental model of the long-span roof. (<b>a</b>) Overall Structure View; (<b>b</b>) Detailed dimensions of the roof.</p>
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<p>Wind tunnel test model photos (roof opening and roof opening closure).</p>
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<p>Model measurement points and settings of wind orientation angle.</p>
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<p>Experimental simulation of wind field: (<b>a</b>) average velocity and intensity; (<b>b</b>) wind spectrum.</p>
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<p>Cloud map of mean wind pressure coefficient for roof opening and closure under typical wind direction angles: (<b>a</b>–<b>c</b>): <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mi>C</mi> <mo stretchy="true">¯</mo> </mover> </mrow> <mi>p</mi> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0°, 90° and 180° (Roof Opening); (<b>d</b>–<b>f</b>): <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mi>C</mi> <mo stretchy="true">¯</mo> </mover> </mrow> <mi>p</mi> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0°, 90° and 180° (Roof Opening Closure).</p>
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<p>Cloud map of varying wind pressure coefficient for roof opening and closure under common wind orientation angles: (<b>a</b>–<b>c</b>): <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mi>p</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0°, 90° and 180° (Roof Opening); (<b>d</b>–<b>f</b>): <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mrow> <mi>p</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0°, 90° and 180° (Roof Opening Closure).</p>
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<p>Scatter plots of skewness and kurtosis of roof openings and closures on the roof surface under discrepant wind orientation angles: (<b>a</b>) <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0°; (<b>b</b>) <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 90°; (<b>c</b>) <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 180°.</p>
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<p>Wind pressure skewness and kurtosis cloud maps of roof opening and closure under typical wind direction angles: (<b>a</b>–<b>f</b>): <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>k</mi> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0°, 90° and 180° (roof opening and roof opening closure); (<b>g</b>–<b>l</b>): <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>u</mi> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0°, 90° and 180° (roof opening and roof opening closure).</p>
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<p>Probability density distribution curve of varying wind pressure on the roof surface under roof opening and closure: (<b>a</b>–<b>c</b>): point B10, C12, E11 (PDF at <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0°); (<b>d</b>–<b>f</b>): point A3, A7, A9 (PDF at <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 90°); (<b>g</b>–<b>i</b>): point C1, D11, F1 (PDF at <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 180°).</p>
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<p>Cloud map of the envelope diagram of the minimum wind pressure coefficient on the roof surface under all wind direction angles: (<b>a</b>): roof opening; (<b>b</b>): roof opening closure.</p>
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18 pages, 7376 KiB  
Article
Smart Electronic Device-Based Monitoring of SAR and Temperature Variations in Indoor Human Tissue Interaction
by Filippo Laganà, Luigi Bibbò, Salvatore Calcagno, Domenico De Carlo, Salvatore A. Pullano, Danilo Pratticò and Giovanni Angiulli
Appl. Sci. 2025, 15(5), 2439; https://doi.org/10.3390/app15052439 - 25 Feb 2025
Viewed by 201
Abstract
The daily use of devices generating electric and magnetic fields has led to potential human overexposure in home and work environments. This paper assesses the possible effects of electric fields on human health at low and high frequencies. It presents an electronic monitoring [...] Read more.
The daily use of devices generating electric and magnetic fields has led to potential human overexposure in home and work environments. This paper assesses the possible effects of electric fields on human health at low and high frequencies. It presents an electronic monitoring device that captures the incidence of specific absorption rate (SAR) and temperature variation (∆T) on the human body. The system transmits data to a cloud platform, where a feedforward neural network (FFNN) processes the received information. SAR and surface temperature values are detected in an indoor environment, monitoring stationary and moving subjects. The results effectively assess temperature distribution due to electromagnetic fields. The prototype detected temperature peaks and high SAR values when the subjects remained motionless. Predictive analysis confirms the need for workplaces with materials shielding external electromagnetic signals and attenuating internal sources. Moderate mobile phone use could lower SAR and temperature values. Full article
(This article belongs to the Special Issue Robotics, IoT and AI Technologies in Bioengineering)
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<p>Scenario analysed in indoor environment—(<b>a</b>) body area monitored (head); (<b>b</b>) Wi-Fi source; (<b>c</b>) SAR sensors.</p>
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<p>Measurement environments: (<b>a</b>) stationary subject; (<b>b</b>) moving subject.</p>
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<p>Electric field limit values determined by ICNIRP and HHI for humans.</p>
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<p>Flowchart monitoring system.</p>
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<p>Monitoring board design for SAR and temperature variation signal acquisition.</p>
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<p>Feedforward neural network architecture.</p>
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<p>PCA: dimensionality reduction for SAR and temperature signals.</p>
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<p>Comparison between the SAR and temperature values measured by the device and the normative limits.</p>
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<p>Variation of SAR and temperature on tissues: (<b>a</b>) impact in an indoor environment; (<b>b</b>) impact of SAR on human tissue as the temperature changes.</p>
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<p>Impact of SAR on human tissue: (<b>a</b>) as temperature changes; (<b>b</b>) as source frequency changes.</p>
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<p>Temperature variation (Δ<span class="html-italic">T</span>) as a function of electromagnetic wave frequency (Hz) for a stationary subject at different distances from the source.</p>
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<p>SAR as a function of electromagnetic wave frequency (in GHz) for a subject exposed to electromagnetic sources located at three different distances: 0.5 m, 1.0 m, and 2.0 m.</p>
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<p>FFNN results: (<b>a</b>) true vs. predicted SAR values; (<b>b</b>) residuals distribution.</p>
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24 pages, 4424 KiB  
Article
Impact of Temperature Manipulations on Growth Performance, Body Composition, and Selected Genes of Koi Carp (Cyprinus carpio koi)
by Kennedy Emeka Amuneke, Ahmed E. Elshafey, Yuanhao Liu, Jianzhong Gao, Justice Frimpong Amankwah, Bin Wen and Zaizhong Chen
Fishes 2025, 10(3), 95; https://doi.org/10.3390/fishes10030095 - 24 Feb 2025
Viewed by 126
Abstract
Aquatic organisms face substantial challenges from climate change, particularly due to rising water temperatures, which significantly impact their growth and survival. This investigation utilized 960 Koi carp (Cyprinus carpio koi) (Initial Body Weight, 0.304 ± 0.005 g). After a 10-day acclimatization [...] Read more.
Aquatic organisms face substantial challenges from climate change, particularly due to rising water temperatures, which significantly impact their growth and survival. This investigation utilized 960 Koi carp (Cyprinus carpio koi) (Initial Body Weight, 0.304 ± 0.005 g). After a 10-day acclimatization period, the fish were distributed equally across 12 glass aquaria (80 × 40 × 45 cm), with three replicates per treatment. This study encompassed two phases. The first phase (10–60 Days Post-Hatching, dph) involved four temperature regimes: T1 (26 °C), T2 (28 °C), T3 (30 °C), and T4 (26/30 °C daily fluctuation). The second phase (60–120 dph) maintained all groups at 30 °C. Initially, T1 exhibited the best growth performance, indicated by the highest Final Body Weight, Weight Gain, Specific Growth Rate (SGR), and Thermal Growth Coefficient (TGC), along with the highest survival rate. Gene expression analysis revealed that HSP70, HSP90, SOD, BCL-2, and FASN were upregulated in T3 and T4, indicative of stress, while MYOD was highest in T1. During the second phase, T4 displayed superior growth and a healthier body composition with elevated moisture and protein, and reduced fat content compared to T1 and T2. HSP70, HSP90, and BCL-2 expression increased significantly in T1, suggesting thermal stress, whereas MYOD levels rose across all treatments, peaking in T4, which correlated with its growth. Further, there were strong relationships among growth parameters, gene expression, and body composition, with T4 exhibiting the highest essential and non-essential amino acids and a unique fatty acid profile. Overall, the results suggest that manipulated temperature significantly influences Koi carp’s characteristics, making it more adaptable to future environmental stress. Full article
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<p>Experimental design, where colors represent fish treatment temperatures: 26 °C (light green, T1), 28 °C (dark green, T2), 30 °C (light red, T3), and fluctuating between 26 °C and 30 °C (dark red).</p>
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<p>Survival rate of Koi carp exposed to different temperatures (60 Days Post-Hatching, First phase).</p>
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<p>Survival rate of Koi carp after exposure to stress temperatures (120 Days Post-Hatching, second phase). * Indicates significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Relative gene expression results: (<span class="html-italic">HSP70HSP70</span>) Heat Shock Protein 70 gene (<b>A</b>); (<span class="html-italic">HSP90</span>) Heat Shock Protein 90 gene (<b>B</b>); (<span class="html-italic">BCL-2</span>) B-Cell Lymphoma 2 gene (<b>C</b>); (<span class="html-italic">MYOD</span>) Myogenic Differentiation 1 gene (<b>D</b>); (<span class="html-italic">SOD</span>) Superoxide Dismutase gene (<b>E</b>); and (<span class="html-italic">FASN</span>) Fatty Acid Synthase gene (<b>F</b>). Koi carp were exposed to different temperatures at the first phase (60 Days Post-Hatching). Different letters above the bars indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Relative gene expression results: (<span class="html-italic">HSP70HSP70</span>) Heat Shock Protein 70 gene (<b>A</b>); (<span class="html-italic">HSP90</span>) Heat Shock Protein 90 gene (<b>B</b>); (<span class="html-italic">BCL-2</span>) B-Cell Lymphoma 2 gene (<b>C</b>); (<span class="html-italic">MYOD</span>) Myogenic Differentiation 1 gene (<b>D</b>); (<span class="html-italic">SOD</span>) Superoxide Dismutase gene (<b>E</b>); and (<span class="html-italic">FASN</span>) Fatty Acid Synthase gene (<b>F</b>). Koi carp were exposed to different temperatures at the first phase (60 Days Post-Hatching). Different letters above the bars indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Relative gene expression results: (<span class="html-italic">HSP70</span>) Heat Shock Protein 70 gene (<b>A</b>); (<span class="html-italic">HSP90</span>) Heat Shock Protein 90 gene (<b>B</b>); (<span class="html-italic">BCL-2</span>) B-Cell Lymphoma 2 gene (<b>C</b>); (<span class="html-italic">MYOD</span>) Myogenic Differentiation 1 gene (<b>D</b>); (<span class="html-italic">SOD</span>) Superoxide Dismutase gene (<b>E</b>); and (<span class="html-italic">FASN</span>) Fatty Acid Synthase gene (<b>F</b>). Koi carp were exposed to stress temperatures at the second phase (120 Days Post-Hatching). Different letters above the bars indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>A heatmap was generated to visualize the correlation coefficient matrix of gene expression levels for <span class="html-italic">HSP70</span> (Heat Shock Protein 70), <span class="html-italic">HSP90</span> (Heat Shock Protein 90), <span class="html-italic">BCL-2</span> (B-Cell Lymphoma 2), <span class="html-italic">MYOD</span> (Myogenic Differentiation 1), <span class="html-italic">SOD</span> (Superoxide Dismutase), and <span class="html-italic">FASN</span> (Fatty Acid Synthase) at two developmental phases: 60 Days Post-Hatching and 120 Days Post-Hatching. Magenta tones within the heatmap indicate positive correlations, while red tones represent negative correlations between gene expression levels.</p>
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<p>Correlation between growth performance indicators (Specific Growth Rate (SGR), Weight Gain (WG), Geometric Mean Weight (GMW), and Thermal Growth Coefficient (TGC)) and the expression levels of key genes (<span class="html-italic">HSP70</span> (Heat Shock Protein 70), <span class="html-italic">HSP90</span> (Heat Shock Protein 90), <span class="html-italic">BCL-2</span> (B-Cell Lymphoma 2), <span class="html-italic">MYOD</span> (Myogenic Differentiation 1), <span class="html-italic">SOD</span> (Superoxide Dismutase), and <span class="html-italic">FASN</span> (Fatty Acid Synthase)) in the first phase (60 Days Post-Hatching). The pie-shaped openings show the degree of significant differences, where dark blue tones within the figure indicate positive correlations, while dark red tones represent negative correlations between gene expression levels.</p>
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<p>Correlation between growth performance indicators (Specific Growth Rate (SGR), Weight Gain (WG), Geometric Mean Weight (GMW), Thermal Growth Coefficient (TGC)) and the expression levels of key genes (<span class="html-italic">HSP70</span> (Heat Shock Protein 70), <span class="html-italic">HSP90</span> (Heat Shock Protein 90), <span class="html-italic">BCL-2</span> (B-Cell Lymphoma 2), <span class="html-italic">MYOD</span> (Myogenic Differentiation 1), <span class="html-italic">SOD</span> (Superoxide Dismutase), and <span class="html-italic">FASN</span> (Fatty Acid Synthase)) in the second phase (120 Days Post-Hatching). The pie-shaped openings show the degree of significant differences, where dark blue tones within the figure indicate positive correlations, while dark red tones represent negative correlations between gene expression levels.</p>
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<p>Correlation between body chemical composition and the expression levels of key genes (<span class="html-italic">HSP70</span> (Heat Shock Protein 70), <span class="html-italic">HSP90</span> (Heat Shock Protein 90), <span class="html-italic">BCL-2</span> (B-Cell Lymphoma 2), <span class="html-italic">MYOD</span> (Myogenic Differentiation 1), <span class="html-italic">SOD</span> (Superoxide Dismutase), and <span class="html-italic">FASN</span> (Fatty Acid Synthase)) in the second phase (120 Days Post-Hatching). The shape size and color of the ellipses indicate the degree of significant differences, where dark blue tones within the figure indicate positive correlations, while dark red tones represent negative correlations between gene expression levels.</p>
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24 pages, 7497 KiB  
Article
Experimental Analysis of Vacuum Solar Collectors as an Auxiliary Heating Source for Residential Buildings
by Rafał Urbaniak, Bartosz Ciupek and Paweł Grobelny
Energies 2025, 18(5), 1093; https://doi.org/10.3390/en18051093 - 24 Feb 2025
Viewed by 168
Abstract
This study presents an experimental analysis of two vacuum solar air collectors designed for residential heating applications. The research was conducted from November 2022 to April 2024 in real operating conditions. This study focused on assessing the thermal performance, energy efficiency, and feasibility [...] Read more.
This study presents an experimental analysis of two vacuum solar air collectors designed for residential heating applications. The research was conducted from November 2022 to April 2024 in real operating conditions. This study focused on assessing the thermal performance, energy efficiency, and feasibility of integrating these systems into hybrid heating solutions. The first collector (Solar Dragon 2022) utilized five vacuum tubes and achieved a total thermal energy output of 397.67 kWh over five months, with a peak thermal power of 0.55 kW. The second system (Solar Dragon 2023), equipped with 24 vacuum tubes, demonstrated a significantly higher performance, generating 911.69 kWh over the same period, with a peak thermal power of 1.8 kW. The study also identified challenges related to airflow distribution and excessive outlet air temperatures, reaching up to 84 °C in the modified system, which could negatively impact indoor comfort. The findings highlight the potential of vacuum solar collectors as an auxiliary heating source, particularly in transitional seasons, while emphasizing the need for optimized airflow control and thermal regulation strategies to enhance their practical application. Full article
(This article belongs to the Section J1: Heat and Mass Transfer)
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<p>Annual balance of solar heat supply and heat and hot water demand recorded in Germany in the first decade of the 21st century [<a href="#B20-energies-18-01093" class="html-bibr">20</a>].</p>
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<p>Efficiency chart of selected solar collectors, where T<sub>m</sub>—the average temperature of the working fluid; T<sub>a</sub>—the ambient temperature; and I—the intensity of solar radiation [<a href="#B23-energies-18-01093" class="html-bibr">23</a>].</p>
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<p>Diagram of the system for distributing the heated medium in a solar collector [<a href="#B36-energies-18-01093" class="html-bibr">36</a>].</p>
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<p>Solar Dragon 2022 solar collector’s construction (<b>a</b>,<b>b</b>) and installation in the terrace window (<b>c</b>): 1—heat-exchanger housing; 2—support frame; 3—cold-air inlet pipes; 4—intake fan; 5—exhaust fan; 6—cold-air chamber; 7—hot-air chamber; 8—solar vacuum tube; and 9—heat-exchanger cover.</p>
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<p>Construction of the Solar Dragon 2023 solar collector: 1—outer casing; 2—vacuum tube frame; 3—heat-exchanger casing; 4—cold-air inlet; 5—cold-air chamber; 6—cold-air supply tubes; 7—hot-air chamber; 8—solar vacuum tube; and 9—hot-air outlet.</p>
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<p>Installed collector without the heat-exchanger cover and outer casing.</p>
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<p>Example of data recorded in the SUPLA environment.</p>
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<p>View of the installed solar system and its orientation in relation to the cardinal directions.</p>
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<p>Total thermal energy production in individual months.</p>
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<p>Average external temperature and total thermal energy production for individual days in March 2023.</p>
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<p>Average external temperature and total thermal energy production for each hour on 26 March 2023.</p>
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<p>Average temperature values for each hour on 26 March 2023.</p>
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<p>Average temperature values and instantaneous power output for each hour on 26 March 2023.</p>
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<p>Total thermal energy production in individual months.</p>
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<p>Average external temperature and total thermal energy produced for each day in December 2023.</p>
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<p>Average external temperature and total thermal energy production for individual days in March 2024.</p>
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<p>Average external temperature and total thermal energy produced for each hour on 11 December 2023.</p>
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<p>Average external temperature and total thermal energy produced for each hour on 17 March 2024.</p>
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<p>Average temperature values for each hour on 11 December 2023.</p>
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<p>Average temperature values for each hour on 17 March 2024.</p>
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<p>Average temperature values and average instantaneous power for each hour on 17 March 2024.</p>
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20 pages, 5125 KiB  
Article
Quantifying Land Subsidence Probability and Intensity Using Weighted Bayesian Modeling in Shanghai, China
by Chengming Jin, Qing Zhan, Yujin Shi, Chengcheng Wan, Huan Zhang, Luna Zhao, Jianli Liu, Tongfei Tian, Zilong Liu and Jiahong Wen
Land 2025, 14(3), 470; https://doi.org/10.3390/land14030470 - 24 Feb 2025
Viewed by 142
Abstract
Land subsidence, a slow-onset geohazard, poses a severe threat to cities worldwide. However, the lack of quantification in terms of intensity, probability, and hazard zoning complicates the assessment and understanding of the land subsidence risk. In this study, we employ a weighted Bayesian [...] Read more.
Land subsidence, a slow-onset geohazard, poses a severe threat to cities worldwide. However, the lack of quantification in terms of intensity, probability, and hazard zoning complicates the assessment and understanding of the land subsidence risk. In this study, we employ a weighted Bayesian model to explicitly present the spatial distribution of land subsidence probability and map hazard zoning in Shanghai. Two scenarios based on distinct aquifers are analyzed. Our findings reveal the following: (1) The cumulative land subsidence probability density functions in Shanghai follow a skewed distribution, primarily ranging between 0 and 50 mm, with a peak probability at 25 mm for the period 2017–2021. The proportions of cumulative subsidence above 100 mm and between 50 and 100 mm are significantly lower for 2017–2021 compared to those for 2012–2016, indicating a continuous slowdown in land subsidence in Shanghai. (2) Using the cumulative subsidence from 2017–2021 as a measure of posterior probability, the probability distribution of land subsidence under the first scenario ranges from 0.02 to 0.97. The very high probability areas are mainly located in the eastern peripheral regions of Shanghai and the peripheral areas of Chongming District. Under the second scenario, the probability ranges from 0.04 to 0.98, with high probability areas concentrated in the eastern coastal area of Pudong District and regions with intensive construction activity. (3) The Fit statistics for Scenario I and Scenario II are 67% and 70%, respectively, indicating a better fit for Scenario II. (4) High-, medium-, low-, and very low-hazard zones in Shanghai account for 14.2%, 48.7%, 23.6%, and 13.5% of the city, respectively. This work develops a method based on the weighted Bayesian model for assessing and zoning land subsidence hazards, providing a basis for land subsidence risk assessment in Shanghai. Full article
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<p>Map showing study area of Shanghai City with 16 districts.</p>
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<p>Spatial distribution of influencing factors of land subsidence in Shanghai. (<b>a</b>) Aquifers thickness; (<b>b</b>) Soft soil layer thickness; (<b>c</b>) Intensity of construction activities; (<b>d</b>) Newly reclaimed areas since 1958.</p>
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<p>Cumulative land subsidence maps in Shanghai. (<b>a</b>) Cumulative land subsidence in 2012–2016; (<b>b</b>) cumulative land subsidence in 2017–2021; (<b>c</b>) percentage of different cumulative land subsidence in 2012–2016; (<b>d</b>) percentage of different cumulative land subsidence in 2017–2021; (<b>e</b>) probability distribution of cumulative land subsidence in 2012–2016 and in 2017–2021.</p>
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<p>A Bayesian structural probabilistic computational model. (<b>a</b>) A directed acyclic graph of the weighted Bayesian structural model; (<b>b</b>) the probability of the spatial distribution of the indicators affecting land subsidence; and (<b>c</b>) the probability of land subsidence (posterior probability).</p>
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<p>The methodology framework of land subsidence probability analysis in this study. (<b>a</b>) Aquifers thickness; (<b>b</b>) Soft soil layer thickness; (<b>c</b>) Intensity of construction activities; (<b>d</b>) Newly reclaimed areas since 1958.</p>
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<p>Map of results of expert scoring of importance of indicators. (<b>a</b>) Aquifer thickness; (<b>b</b>) soft soil layer thickness; (<b>c</b>) intensity of construction activities; (<b>d</b>) newly reclaimed areas since 1958.</p>
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<p>Land subsidence hazard (intensity–probability) matrix modeling.</p>
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<p>Probabilities of each indicator under land subsidence conditions (yellow) and non-subsidence conditions (green) for Scenario I and Scenario II using 2017–2021 cumulative land subsidence probability as measure of posterior probability.</p>
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<p>Spatial distribution of land subsidence probability using 2017–2021 cumulative land subsidence as measure of posterior probability. (<b>a</b>) Scenario I; (<b>b</b>) Scenario II.</p>
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<p>Hazard zoning map of land subsidence in Shanghai.</p>
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<p>Probabilities of each indicator under land subsidence conditions (yellow) and non-subsidence conditions (green) for Scenario I and Scenario II using 2012–2016 cumulative land subsidence probability as measure of posterior probability.</p>
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<p>Spatial distribution of land subsidence probability using 2012–2016 cumulative land subsidence as measure of posterior probability. (<b>a</b>) Scenario I; (<b>b</b>) Scenario II.</p>
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<p>Comparison of land subsidence probability distribution in Shanghai using weighted Bayesian model and actual subsidence results. (<b>a</b>) Map of land subsidence and non-subsidence in 2017–2021. (<b>b</b>) Map of land subsidence categories under Scenario I. (<b>c</b>) Map of land subsidence categories under Scenario II. (<b>d</b>) Map of differences between (<b>a</b>,<b>b</b>). (<b>e</b>) Map of differences between (<b>a</b>,<b>c</b>).</p>
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16 pages, 5071 KiB  
Article
Simulation and Experimental Studies of Heat-Mass Transfer and Stress–Strain in Carrots During Hot Air Drying
by Yanyan Li, Mingxia Liang, Jinyan Li, Keyi Jiang, Xiyang Li and Zhaohui Zheng
Agriculture 2025, 15(5), 484; https://doi.org/10.3390/agriculture15050484 - 24 Feb 2025
Viewed by 80
Abstract
Models were developed to study the heat-mass transfer and stress–strain process in carrots during hot air drying. The distribution and variation in temperature, moisture content, strain, and stress of the samples were investigated at different drying temperatures. The results showed that the models [...] Read more.
Models were developed to study the heat-mass transfer and stress–strain process in carrots during hot air drying. The distribution and variation in temperature, moisture content, strain, and stress of the samples were investigated at different drying temperatures. The results showed that the models developed could be used to simulate the hot air drying process of carrots; the maximum weighted absolute percentage errors were 9.01%. The difference between the heat flux and vapor diffusion flux in the regions led to a non-uniform temperature and moisture content distribution, which resulted in non-uniform strain, causing stress within the carrots. The value of the thermal strain and stress was small compared to that of the moisture strain and stress. The thermal stress and moisture stress increased first and then decreased; the peak values of thermal stress and moisture stress occurred in the middle period of the whole drying process. When the hot air drying temperature was higher, the peak value of stress was higher. These results are helpful for understanding the drying mechanism and optimizing operating conditions in carrot drying. Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
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<p>Schematic diagram of the hot air drying system. The arrows represent the direction of the airflow.</p>
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<p>Comparison of moisture contents (<b>a</b>), temperatures (<b>b</b>), and shrinkage rates (<b>c</b>) obtained from experiments and simulations at different drying temperatures.</p>
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<p>Average drying rates (<b>a</b>) and shrinkage rates (<b>b</b>) at various temperatures. Letters above the bars indicate significant differences based on a Duncan test at a level of significance of <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Temperature distributions and heat fluxes of carrot samples during drying at different hot air temperatures: (<b>a</b>) 40 °C, (<b>b</b>) 60 °C, (<b>c</b>) 80 °C.</p>
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<p>Moisture content distributions and vapor diffusion fluxes of carrot samples during drying at different hot air temperatures: (<b>a</b>) 40 °C, (<b>b</b>) 60 °C, (<b>c</b>) 80 °C.</p>
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<p>The coefficients of variation in the moisture content (<b>a</b>) and temperature (<b>b</b>) distributions at different drying temperatures.</p>
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<p>Strain and stress distributions of carrot samples during drying at different hot air temperatures: (<b>a</b>) 40 °C, (<b>b</b>) 60 °C, (<b>c</b>) 80 °C.</p>
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<p>Strain and stress distributions of carrot samples during drying at different hot air temperatures: (<b>a</b>) 40 °C, (<b>b</b>) 60 °C, (<b>c</b>) 80 °C.</p>
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<p>The moisture strain (<b>a</b>) and thermal strain (<b>b</b>) curves of the carrot samples at different hot air temperatures.</p>
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<p>The moisture stress (<b>a</b>) and thermal stress (<b>b</b>) curves of the carrot samples at different hot air temperatures.</p>
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22 pages, 7032 KiB  
Article
Magnetic Excitation for Coupled Pendulum and Piezoelectric Wave Energy Harvester
by Wuwei Feng, Xiang Luo, Shujie Yang and Qingping Zou
Micromachines 2025, 16(3), 252; https://doi.org/10.3390/mi16030252 - 24 Feb 2025
Viewed by 156
Abstract
Wave energy is one of the most reliable and promising renewable energy sources that has attracted lots of attention, including piezoelectric wave energy harvesting devices. One of the challenges for piezoelectric wave power generation is the relatively low-frequency wave environments in the ocean. [...] Read more.
Wave energy is one of the most reliable and promising renewable energy sources that has attracted lots of attention, including piezoelectric wave energy harvesting devices. One of the challenges for piezoelectric wave power generation is the relatively low-frequency wave environments in the ocean. Magnetic excitations are one of the techniques used to overcome this issue. However, there is a lack of understanding of the mechanisms to maximize the electric power output of piezoelectric wave energy harvesters through magnetic excitations. In the present study, magnetic excitation experiments were conducted to investigate the power generation of a coupled spring pendulum piezoelectric energy harvester under various magnetic field conditions. Firstly, the mass of the load magnet that can induce the resonance phenomenon in piezoelectric elements was experimentally determined. Then, the power generation of piezoelectric elements was tested under different excitation magnetic spacings. Finally, the influence of different distribution patterns of excitation magnets on the performance of piezoelectric elements was tested. It was found that under the conditions of a load magnet mass of 2 g, excitation magnet spacing of 4 mm, and two excitation magnets stacked on the inner pendulum, optimum power generation of the piezoelectric wave harvester was achieved with a peak-to-peak output voltage of 39 V. The outcome of this study provides new insight for magnetic excitation devices for piezoelectric wave energy harvesting to increase the feasibility and efficiency of wave energy conversion to electrical energy. Full article
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<p>Layout of the coupled spring pendulum and wave energy piezoelectric harvester. 1—universal joint; 2—spring; 3—load magnet; 4—PZT-5H piezoelectric plate; 5—carbon fiber support rod; 6—square magnet carrier box; 7—excitation magnet; 8—five-way base.</p>
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<p>Schematic diagram of the magnetic excitation component.</p>
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<p>Diagram of the magnetic coupling nonlinear vibration system.</p>
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<p>Magnetic excitation experimental system, consisting of an external excitation system, magnetic excitation power generation system, and voltage signal acquisition system.</p>
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<p>(<b>a</b>) Power generation during free vibration of piezoelectric ceramic plates. (<b>b</b>) Spectrum of piezoelectric ceramic plate power generation during free vibration.</p>
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<p>Natural vibration frequency of piezoelectric ceramic plates vs. load magnet mass.</p>
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<p>Frequency spectra of piezoelectric ceramic plates with different load magnet masses under magnetic excitation. (<b>a</b>) For a loaded magnet mass of 2 g, the main vibration frequency of the piezoelectric ceramic plate was about 19 Hz. (<b>b</b>) For a magnet mass was 4 g, the main vibration frequency of the piezoelectric ceramic plate was about 14 Hz. (<b>c</b>) For a loaded magnet mass of 6 g, the main vibration frequency of the piezoelectric ceramic plate was about 12 Hz.</p>
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<p>Vibration frequencies of piezoelectric ceramic plates vs. load magnet mass under magnetic excitation.</p>
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<p>Comparison of the relationship between the natural frequency of the piezoelectric plate (black) and the vibration frequency of the piezoelectric plate with external magnetic excitation (red) under different load magnet masses.</p>
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<p>Voltage signals of piezoelectric ceramic plates under different distances between excitation and load magnets. (<b>a</b>) At an excitation magnet spacing of 1 mm, the piezoelectric plate showed the phenomenon of forced vibration. (<b>b</b>) At an excitation magnet spacing of 4 mm, the piezoelectric plate showed the phenomenon of vibration attenuation, which was close to free vibration. (<b>c</b>) At an excitation magnet spacing of 10 mm, the vibration attenuation of the piezoelectric plate became weaker.</p>
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<p>Maximum voltages of piezoelectric ceramic plates under different excitation magnet spacing.</p>
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<p>The main vibration frequency (blue) and voltage amplitude at resonance frequency (red) of piezoelectric ceramic plates under excitation magnet spacing of 1 to 10 mm.</p>
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<p>The relationship between the voltage amplitude at the resonance frequency of piezoelectric ceramic plates (red) and their peak-to-peak voltage (black) for magnet spacing of 1 to 10 mm.</p>
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<p>Excitation magnets arranged vertically in a single-row experiment.</p>
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<p>Voltage signals of piezoelectric ceramic plates with 1 to 6 excitation magnets stacked vertically in a row. (<b>a</b>) For 2 magnets, the piezoelectric plate showed the vibration attenuation, which was close to free vibration. (<b>b</b>) For 4 magnets, the vibration attenuation of the piezoelectric plate became weaker. (<b>c</b>) For 6 magnets, the vibration attenuation of the piezoelectric plate disappeared completely.</p>
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<p>Maximum voltage of piezoelectric ceramic plates under 1 to 6 stacked magnets.</p>
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<p>Excitation magnet array experiment.</p>
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<p>Voltage signals of piezoelectric ceramic plates under an array with 1 to 6 magnets. (<b>a</b>) When the number of magnets was 2, the piezoelectric plate showed the vibration attenuation. (<b>b</b>) When the number of magnets was 4, the vibration attenuation of the piezoelectric plate became weaker. (<b>c</b>) When the number of magnets was 6, the vibration attenuation of the piezoelectric plate disappeared completely.</p>
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<p>Maximum voltage of piezoelectric ceramic plates under an array with 1 to 6 magnets.</p>
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<p>Comparison of peak-to-peak voltage and amplitude at resonance frequency of the piezoelectric ceramic plate under an array or stacked with 1 to 6 excitation magnets.</p>
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17 pages, 11073 KiB  
Article
An Investigation of the Effect of Fissure Inclination on Specimen Deformation and the Damage Mechanism Based on the DIC Method
by Hongwei Wang, Fuxiang Xie, Xi Fu, Yongyan Wang and Zhaoming Yin
Buildings 2025, 15(5), 713; https://doi.org/10.3390/buildings15050713 - 24 Feb 2025
Viewed by 186
Abstract
In order to investigate the effect of fissure inclination on the mechanical properties, deformation, and crack evolution of the surrounding rock in the roadway, uniaxial compression experiments were conducted on sandstone-like materials with prefabricated fissures. The high-speed camera and DIC (digital image correlation) [...] Read more.
In order to investigate the effect of fissure inclination on the mechanical properties, deformation, and crack evolution of the surrounding rock in the roadway, uniaxial compression experiments were conducted on sandstone-like materials with prefabricated fissures. The high-speed camera and DIC (digital image correlation) method were employed to analyze the strain distribution and the crack evolution of the specimen. The results demonstrated that the presence of fissures reduces the stress for crack initiation, with intact specimens producing new cracks from about 75% of peak strength and fissured specimens producing new cracks from 50% to 60% of peak strength. The fissure reduced the strength and elastic modulus of the specimen while increasing the strain. The fissure inclination of 45° exhibited the most significant changes compared to the intact specimen. The peak strength and elastic modulus decreased by 54.52% and 35.95%, respectively, and the strain increased by 151.42%. The intact specimen and specimen with 90° inclination are mainly distributed with the shear crack, tensile crack, and far-field crack, which are mainly tensile–tension damage; specimens with 0~75° inclination are mainly distributed with the wing crack, anti-wing crack, oblique secondary crack, and coplanar secondary crack, which are mainly shear slip damage. The direction of the extension of cracks is related to the fissure inclination. For specimens with 0° inclination, the new cracks mainly propagate in the direction perpendicular to the fissure; for specimens with 30° and 45° inclinations, the new cracks mainly propagate in the direction parallel and perpendicular to the fissure; for specimens with 60° and 75° inclinations, the new cracks propagate in the direction parallel to the fissure; and for specimens with 90° inclination, the new cracks propagate in the direction parallel to the fissure. Full article
(This article belongs to the Section Building Structures)
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<p>Schematic diagram of the specimen and the prefabricated fracture.</p>
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<p>Sample loading.</p>
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<p>Component materials and tools for specimen fabrication.</p>
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<p>Part of prepared specimens.</p>
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<p>Mechanical experiment system.</p>
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<p>The deformation test method.</p>
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<p>Stress–strain curves of the specimen.</p>
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<p>Strain distribution for intact specimens (* the percentage indicates the ratio of the load to the peak stress).</p>
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<p>Strain distribution of the specimen with a fissure of 0°.</p>
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<p>Strain distribution of the specimen with a fissure of 30°.</p>
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<p>Strain distribution of the specimen with a fissure of 45°.</p>
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<p>Strain distribution of the specimen with a fissure of 60°.</p>
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<p>Strain distribution of the specimen with a fissure of 75°.</p>
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<p>Strain distribution of the specimen with a fissure of 90°.</p>
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<p>The trend of peak strength with the fissure inclination angle.</p>
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<p>The trend of the elastic modulus with the fissure inclination angle.</p>
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<p>Cracking patterns of fissured rock mass upon breakage (the red line represents the visible cracks on the surface of the specimen following its breakage).</p>
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