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18 pages, 7614 KiB  
Article
The Influence of Print Orientation and Discontinuous Carbon Fiber Content on the Tensile Properties of Selective Laser-Sintered Polyamide 12
by Jonathan J. Slager, Joshua T. Green, Samuel D. Levine and Roger V. Gonzalez
Polymers 2025, 17(15), 2028; https://doi.org/10.3390/polym17152028 - 25 Jul 2025
Abstract
Discontinuous fibers are commonly added to matrix materials in additive manufacturing to enhance properties, but such benefits may be constrained by print and fiber orientation. The additive processes of forming rasters and layers in powder bed fusion inherently cause anisotropy in printed parts. [...] Read more.
Discontinuous fibers are commonly added to matrix materials in additive manufacturing to enhance properties, but such benefits may be constrained by print and fiber orientation. The additive processes of forming rasters and layers in powder bed fusion inherently cause anisotropy in printed parts. Many print parameters, such as laser, temperature, and hatch pattern, influence the anisotropy of tensile properties. This study characterizes fiber orientation attributed to recoating non-encapsulated fibers and the resulting anisotropic tensile properties. Tensile and fracture properties of polyamide 12 reinforced with 0%, 2.5%, 5%, and 10% discontinuous carbon fibers by volume were characterized in two primary print/tensile loading orientations: tensile loading parallel to the recoater (“horizontal specimens”) and tensile load along the build axis (“vertical specimens”). Density and fractographic analysis indicate a homogeneous mixture with low porosity and primary fiber orientation along the recoating direction for both print orientations. Neat specimens (zero fiber) loaded in either direction have similar tensile properties. However, fiber-reinforced vertical specimens have significantly reduced consistency and tensile strength as fiber content increased, while the opposite is true for horizontal specimens. These datasets and results provide a mechanism to tune material properties and improve the functionality of selectively laser-sintered fiber-reinforced parts through print orientation selection. These datasets could be used to customize functionally graded parts with multi-material selective laser-sintering manufacturing. Full article
(This article belongs to the Special Issue Polymeric Composites: Manufacturing, Processing and Applications)
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21 pages, 6563 KiB  
Article
Determining the Structural Characteristics of Farmland Shelterbelts in a Desert Oasis Using LiDAR
by Xiaoxiao Jia, Huijie Xiao, Zhiming Xin, Junran Li and Guangpeng Fan
Forests 2025, 16(8), 1221; https://doi.org/10.3390/f16081221 - 24 Jul 2025
Abstract
The structural analysis of shelterbelts forms the foundation of their planning and management, yet the scientific and effective quantification of shelterbelt structures requires further investigation. This study developed an innovative heterogeneous analytical framework, integrating three key methodologies: the LeWoS algorithm for wood–leaf separation, [...] Read more.
The structural analysis of shelterbelts forms the foundation of their planning and management, yet the scientific and effective quantification of shelterbelt structures requires further investigation. This study developed an innovative heterogeneous analytical framework, integrating three key methodologies: the LeWoS algorithm for wood–leaf separation, TreeQSM for structural reconstruction, and 3D alpha-shape spatial quantification, using terrestrial laser scanning (TLS) technology. This framework was applied to three typical farmland shelterbelts in the Ulan Buh Desert oasis, enabling the first precise quantitative characterization of structural components during the leaf-on stage. The results showed the following to be true: (1) The combined three-algorithm method achieved ≥90.774% relative accuracy in extracting structural parameters for all measured traits except leaf surface area. (2) Branch length, diameter, surface area, and volume decreased progressively from first- to fourth-order branches, while branch angles increased with ascending branch order. (3) The trunk, branch, and leaf components exhibited distinct vertical stratification. Trunk volume and surface area decreased linearly with height, while branch and leaf volumes and surface areas followed an inverted U-shaped distribution. (4) Horizontally, both surface area density (Scd) and volume density (Vcd) in each cube unit exhibited pronounced edge effects. Specifically, the Scd and Vcd were greatest between 0.33 and 0.60 times the shelterbelt’s height (H, i.e., mid-canopy). In contrast, the optical porosity (Op) was at a minimum of 0.43 H to 0.67 H, while the volumetric porosity (Vp) was at a minimum at 0.25 H to 0.50 H. (5) The proposed volumetric stratified porosity (Vsp) metric provides a scientific basis for regional farmland shelterbelt management strategies. This three-dimensional structural analytical framework enables precision silviculture, with particular relevance to strengthening ecological barrier efficacy in arid regions. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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26 pages, 412 KiB  
Article
Entropy and Stability: Reduced Hamiltonian Formalism of Non-Barotropic Flows and Instability Constraints
by Asher Yahalom
Entropy 2025, 27(8), 779; https://doi.org/10.3390/e27080779 - 23 Jul 2025
Viewed by 86
Abstract
A reduced representation of a dynamical system helps us to understand what the true degrees of freedom of that system are and thus what the possible instabilities are. Here we extend previous work on barotropic flows to the more general non-barotropic flow case [...] Read more.
A reduced representation of a dynamical system helps us to understand what the true degrees of freedom of that system are and thus what the possible instabilities are. Here we extend previous work on barotropic flows to the more general non-barotropic flow case and study the implications for variational analysis and conserved quantities of topological significance such as circulation and helicity. In particular we introduce a four-function Eulerian variational principle of non-barotropic flows, which has not been described before. Also new conserved quantities of non-barotropic flows related to the topological velocity field, topological circulation and topological helicity, including a local version of topological helicity, are introduced. The variational formalism given in terms of a Lagrangian density allows us to introduce canonical momenta and hence a Hamiltonian formalism. Full article
(This article belongs to the Special Issue Unstable Hamiltonian Systems and Scattering Theory)
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18 pages, 2381 KiB  
Article
Influence of Low-Level Red Laser Irradiation on the Proliferation, Viability, and Differentiation of Human Embryonic Stem Cell-Derived Mesenchymal Stem Cells
by Khalid M. AlGhamdi, Ashok Kumar, Musaad Alfayez and Amer Mahmood
Life 2025, 15(7), 1125; https://doi.org/10.3390/life15071125 - 17 Jul 2025
Viewed by 447
Abstract
The present investigation was conducted to observe the effects of different energy densities of a low-level red laser (LLRL) on human embryonic stem cell-derived mesenchymal stem cells (hESC-MSCs). hESC-MSCs were cultured and irradiated with a LLRL from 0.5 to 5.0 J/cm2 at [...] Read more.
The present investigation was conducted to observe the effects of different energy densities of a low-level red laser (LLRL) on human embryonic stem cell-derived mesenchymal stem cells (hESC-MSCs). hESC-MSCs were cultured and irradiated with a LLRL from 0.5 to 5.0 J/cm2 at a wavelength of 635 nm. Biological parameters such as proliferation, viability, and migration were observed after 72 h of LLRL irradiation. Compared with the control, LLRL irradiation significantly increased the proliferation and viability of hESC-MSCs from 0.5 to 2.5 J/cm2 (p < 0.001, p < 0.05). LLRL irradiation from 0.5 to 3.0 J/cm2 significantly increased the migration of hESC-MSCs (p < 0.01). These results revealed that LLRL irradiation at lower energy densities significantly increased the proliferation, viability, and migration of hESC-MSCs. However, higher energy densities were ineffective; this was also true when we examined osteogenic differentiation, as low energy densities of LLRL had a positive effect on differentiation, whereas higher energy densities had a negative effect on alkaline phosphatase activity, Alizarin Red staining and gene expression analysis. In addition, not all stem cell markers were affected by the laser, and a slight decrease in the expression of CD146, which is a stemness marker, was detected, indicating improved differentiation. These findings indicate that low energy densities of LLRL irradiation have positive effects on the proliferation, migration, and differentiation of hESC-MSCs. However, higher energy densities showed inhibitory effects. Full article
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18 pages, 2905 KiB  
Article
Size Reduction in Micro Gas Turbines Using Silicon Carbide
by Ahmad Abuhaiba
Gases 2025, 5(3), 14; https://doi.org/10.3390/gases5030014 - 2 Jul 2025
Viewed by 727
Abstract
Micro gas turbines serve small-scale generation where swift response and low emissions are highly valued, and they are commonly fuelled by natural gas. True to their ‘micro’ designation, their size is indeed compact; however, a noteworthy portion of the enclosure is devoted to [...] Read more.
Micro gas turbines serve small-scale generation where swift response and low emissions are highly valued, and they are commonly fuelled by natural gas. True to their ‘micro’ designation, their size is indeed compact; however, a noteworthy portion of the enclosure is devoted to power electronics components. This article considers whether these components can be made even smaller by substituting their conventional silicon switches with switches fashioned from silicon carbide. The wider bandgap of silicon carbide permits stronger electric fields and reliable operation at higher temperatures, which together promise lower switching losses, less heat, and simpler cooling arrangements. This study rests on a simple volumetric model. Two data sets feed the model. First come the manufacturer specifications for a pair of converter modules (one silicon, the other silicon carbide) with identical operation ratings. Second are the operating data and dimensions of a commercial 100 kW micro gas turbine. The model splits the converter into two parts: the semiconductor package and its cooling hardware. It then applies scaling factors that capture the higher density of silicon carbide and its lower switching losses. Lower switching losses reduce generated heat, so heatsinks, fans, or coolant channels can be slimmer. Together these effects shrink the cooling section and, therefore, the entire converter. The findings show that a micro gas turbine inverter built with silicon carbide occupies about one fifth less space and delivers more than a quarter higher power density than its silicon counterpart. Full article
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17 pages, 411 KiB  
Article
Kernel Density Estimation for Joint Scrambling in Sensitive Surveys
by Alvan Caleb Arulandu and Sat Gupta
Mathematics 2025, 13(13), 2134; https://doi.org/10.3390/math13132134 - 30 Jun 2025
Viewed by 210
Abstract
Randomized response models aim to protect respondent privacy when sampling sensitive variables but consequently compromise estimator efficiency. We propose a new sampling method, titled joint scrambling, which preserves all true responses while protecting privacy by asking each respondent to jointly speak both their [...] Read more.
Randomized response models aim to protect respondent privacy when sampling sensitive variables but consequently compromise estimator efficiency. We propose a new sampling method, titled joint scrambling, which preserves all true responses while protecting privacy by asking each respondent to jointly speak both their true response and multiple random responses in an arbitrary order. We give a kernel density estimator for the density function with asymptotically equivalent mean squared error for the optimal bandwidth yet greater generality than existing techniques for randomized response models. We also give consistent, unbiased estimators for a general class of estimands including the mean. For the cumulative distribution function, this estimator is more computationally efficient with asymptotically lower mean squared error than existing approaches. All results are verified via simulation and evaluated with respect to natural generalizations of existing privacy notions. Full article
(This article belongs to the Special Issue Innovations in Survey Statistics and Survey Sampling)
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20 pages, 7094 KiB  
Article
Adaptive Warning Thresholds for Dam Safety: A KDE-Based Approach
by Nathalia Silva-Cancino, Fernando Salazar, Joaquín Irazábal and Juan Mata
Infrastructures 2025, 10(7), 158; https://doi.org/10.3390/infrastructures10070158 - 26 Jun 2025
Viewed by 302
Abstract
Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predictive models—based on [...] Read more.
Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predictive models—based on techniques such as the finite element method (FEM) or machine learning (ML)—to compare real-time data against expected performance. However, these models often rely on static warning thresholds, which fail to reflect the dynamic conditions affecting dam behavior, including fluctuating water levels, temperature variations, and extreme weather events. This study introduces an adaptive warning threshold methodology for dam safety based on kernel density estimation (KDE). The approach incorporates a boosted regression tree (BRT) model for predictive analysis, identifying influential variables such as reservoir levels and ambient temperatures. KDE is then used to estimate the density of historical data, allowing for dynamic calibration of warning thresholds. In regions of low data density—where prediction uncertainty is higher—the thresholds are widened to reduce false alarms, while in high-density regions, stricter thresholds are maintained to preserve sensitivity. The methodology was validated using data from an arch dam, demonstrating improved anomaly detection capabilities. It successfully reduced false positives in data-sparse conditions while maintaining high sensitivity to true anomalies in denser data regions. These results confirm that the proposed methodology successfully meets the goals of enhancing reliability and adaptability in dam safety monitoring. This adaptive framework offers a robust enhancement to dam safety monitoring systems, enabling more reliable detection of structural issues under variable operating conditions. Full article
(This article belongs to the Special Issue Preserving Life Through Dams)
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25 pages, 6129 KiB  
Article
Application of Mercury Intrusion Porosimetry in Coal Pore Structure Characterization: Conformance Effect and Compression Effect Correction
by Shiqi Liu, Yu Liang, Shuxun Sang, He Wang, Wenkai Wang, Jianbo Sun and Fukang Li
Energies 2025, 18(12), 3185; https://doi.org/10.3390/en18123185 - 17 Jun 2025
Viewed by 296
Abstract
Mercury intrusion porosimetry (MIP) is commonly used to characterize coal pore structures, but conformance effect and compression effect can overestimate pore volume. This study uses MIP data from coal with varying metamorphic degrees in China to compare existing correction methods and propose a [...] Read more.
Mercury intrusion porosimetry (MIP) is commonly used to characterize coal pore structures, but conformance effect and compression effect can overestimate pore volume. This study uses MIP data from coal with varying metamorphic degrees in China to compare existing correction methods and propose a new approach based on apparent and true density for pore volume correction under no confining pressure. The study also analyzes the impact of conformance and compression effects on MIP data. Correctly identifying the “actual initial intrusion pressure” and “closure pressure” is essential for accurate data correction. The fractal dimension method offers a more robust theoretical foundation, while the conformance and intrusion pressure identification method is simpler. The stage correction method is reliable but requires repeated MIP tests, adding to the workload. The new method, which corrects both coal matrix and mercury volume compression, provides a simpler and reliable solution. Results show that conformance volume accounts for 9.91–83.26% of the apparent mercury intrusion volume and increases with coal metamorphism. Coal matrix volume compression represents 99.86–99.90% of the corrected total volume, with mercury volume compression being negligible. The corrected pore volume decreases as coal metamorphism increases, indicating the effectiveness and simplicity of the proposed method. Full article
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27 pages, 4244 KiB  
Article
Developing a Prediction Model for Real-Time Incident Detection Leveraging User-Oriented Participatory Sensing Data
by Md Tufajjal Hossain, Joyoung Lee, Dejan Besenski, Branislav Dimitrijevic and Lazar Spasovic
Information 2025, 16(6), 423; https://doi.org/10.3390/info16060423 - 22 May 2025
Viewed by 586
Abstract
Effective incident detection is essential for emergency response and transportation management. Traditional methods relying on stationary technologies are often costly and provide limited coverage, prompting the exploration of crowdsourced data such as Waze. While Waze offers extensive coverage, its data can be unverified [...] Read more.
Effective incident detection is essential for emergency response and transportation management. Traditional methods relying on stationary technologies are often costly and provide limited coverage, prompting the exploration of crowdsourced data such as Waze. While Waze offers extensive coverage, its data can be unverified and unreliable. This study aims to identify factors affecting the reliability of Waze alerts and develop a predictive model to distinguish true incidents from false alerts using real-time Waze data, thereby improving emergency response times. Real crash data from the New Jersey Department of Transportation (NJDOT) and crowdsourced data from Waze were matched using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to differentiate true and false alerts. A binary logit model was constructed to reveal significant predictors such as time categories around peak hours, road type, report ratings, and crash type. Findings indicate that the likelihood of accurate Waze alerts increases during peak hours, on streets, and with higher report ratings and major crashes. Additionally, multiple machine learning-based predictive models were developed and evaluated to forecast in real time whether Waze alerts correspond to actual incidents. Among those models, the Random Forest model achieved the highest overall accuracy (82.5%) and F1-score (82.8%), and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.90, demonstrating its robustness and reliability for real-time incident detection. Gradient Boosting, with an AUC-ROC of 0.90 and Area Under the Precision–Recall Curve (AUC-PR) of 0.90, also performed strongly, particularly excelling at predicting true alerts. The analysis further emphasized the importance of key predictors such as time of day, report ratings, and road type. These findings provide actionable insights for enhancing the accuracy of incident detection and improving the reliability of crowdsourced traffic alerts, supporting more effective traffic management and emergency response systems. Full article
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16 pages, 2877 KiB  
Article
From Aromatic Motifs to Cluster-Assembled Materials: Silicon–Lithium Nanoclusters for Hydrogen Storage Applications
by Williams García-Argote, Erika Medel, Diego Inostroza, Alejandro Vásquez-Espinal, José Solar-Encinas, Luis Leyva-Parra, Lina María Ruiz, Osvaldo Yañez and William Tiznado
Molecules 2025, 30(10), 2163; https://doi.org/10.3390/molecules30102163 - 14 May 2025
Viewed by 464
Abstract
Silicon–lithium clusters are promising candidates for hydrogen storage due to their lightweight composition, high gravimetric capacities, and favorable non-covalent binding characteristics. In this study, we employ density functional theory (DFT), global optimization (AUTOMATON and Kick–MEP), and Born–Oppenheimer molecular dynamics (BOMD) simulations to evaluate [...] Read more.
Silicon–lithium clusters are promising candidates for hydrogen storage due to their lightweight composition, high gravimetric capacities, and favorable non-covalent binding characteristics. In this study, we employ density functional theory (DFT), global optimization (AUTOMATON and Kick–MEP), and Born–Oppenheimer molecular dynamics (BOMD) simulations to evaluate the structural stability and hydrogen storage performance of key Li–Si systems. The exploration of their potential energy surface (PES) reveals that the true global minima of Li6Si6 and Li10Si10 differ markedly from those of the earlier Si–Li structures proposed as structural analogs of aromatic hydrocarbons such as benzene and naphthalene. Instead, these clusters adopt compact geometries composed of one or two Si4 (Td) units and a Si2 dimer, all stabilized by surrounding Li atoms. Motivated by the recurrence of the Si4Td motif, we explore oligomers of Li4Si4, which can be viewed as electronically transmuted analogues of P4, confirming the additive H2 uptake across dimer, trimer, and tetramer assemblies. Within the series of Si–Li clusters evaluated, the Li12Si5 sandwich complex, featuring a σ-aromatic Si510− ring encapsulated by two Li65+ moieties, achieves the highest hydrogen capacity, adsorbing 34 H2 molecules with a gravimetric density of 23.45 wt%. Its enhanced performance arises from the high density of accessible Li+ adsorption sites and the electronic stabilization afforded by delocalized σ-bonding. BOMD simulations at 300 and 400 K confirm their dynamic stability and reversible storage behavior, while analysis of the interaction regions confirms that hydrogen adsorption proceeds via weak, dispersion-driven physisorption. These findings clarify the structure–property relationships in Si–Li clusters and provide a basis for designing modular, lightweight, and thermally stable hydrogen storage materials. Full article
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24 pages, 1566 KiB  
Article
Finite Mixture Models: A Key Tool for Reliability Analyses
by Marko Nagode, Simon Oman, Jernej Klemenc and Branislav Panić
Mathematics 2025, 13(10), 1605; https://doi.org/10.3390/math13101605 - 14 May 2025
Viewed by 358
Abstract
As system complexity increases, accurately capturing true system reliability becomes increasingly challenging. Rather than relying on exact analytical solutions, it is often more practical to use approximations based on observed time-to-failure data. Finite mixture models provide a flexible framework for approximating arbitrary probability [...] Read more.
As system complexity increases, accurately capturing true system reliability becomes increasingly challenging. Rather than relying on exact analytical solutions, it is often more practical to use approximations based on observed time-to-failure data. Finite mixture models provide a flexible framework for approximating arbitrary probability density functions and are well suited for reliability modelling. A critical factor in achieving accurate approximations is the choice of parameter estimation algorithm. The REBMIX&EM algorithm, implemented in the rebmix R package, generally performs well but struggles when components of the finite mixture model overlap. To address this issue, we revisit key steps of the REBMIX algorithm and propose improvements. With these improvements, we derive parameter estimators for finite mixture models based on three parametric families commonly applied in reliability analysis: lognormal, gamma, and Weibull. We conduct a comprehensive simulation study across four system configurations, using lognormal, gamma, and Weibull distributions with varying parameters as system component time-to-failure distributions. Performance is benchmarked against five widely used R packages for finite mixture modelling. The results confirm that our proposal improves both estimation accuracy and computational efficiency, consistently outperforming existing packages. We also demonstrate that finite mixture models can approximate analytical reliability solutions with fewer components than the actual number of system components. Our proposals are also validated using a practical example from Backblaze hard drive data. All improvements are included in the open-source rebmix R package, with complete source code provided to support the broader adoption of the R programming language in reliability analysis. Full article
(This article belongs to the Section E1: Mathematics and Computer Science)
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17 pages, 1755 KiB  
Article
FST-Based Marker Prioritization Within Quantitative Trait Loci Regions and Its Impact on Genomic Selection Accuracy: Insights from a Simulation Study with High-Density Marker Panels for Bovines
by Sajjad Toghiani, Samuel E. Aggrey and Romdhane Rekaya
Genes 2025, 16(5), 563; https://doi.org/10.3390/genes16050563 - 10 May 2025
Viewed by 570
Abstract
Background/Objectives: Genomic selection (GS) has improved accuracy compared to traditional methods. However, accuracy tends to plateau beyond a certain marker density. Prioritizing influential SNPs could further enhance the accuracy of GS. The fixation index (FST) allows for the identification of SNPs [...] Read more.
Background/Objectives: Genomic selection (GS) has improved accuracy compared to traditional methods. However, accuracy tends to plateau beyond a certain marker density. Prioritizing influential SNPs could further enhance the accuracy of GS. The fixation index (FST) allows for the identification of SNPs under selection pressure. Although the FST method was shown to be able to prioritize SNPs across the whole genome and to increase accuracy, its performance could be further improved by focusing on the prioritization process within QTL regions. Methods: A trait with heritability of 0.1 and 0.4 was generated under different simulation scenarios (number of QTL, size of SNP windows around QTL, and number of selected SNPs within a QTL region). In total, six simulation scenarios were analyzed. Each scenario was replicated five times. The population comprised 30K animals from the last 2 generations (G9–G10) of a 10-generation (G1–G10) selection process. All animals in G9–10 were genotyped with a 600K SNP panel. FST scores were calculated for all 600K SNPs. Two prioritization scenarios were used: (1) selecting the top 1% SNPs with the highest FST scores, and (2) selecting a predetermined number of SNPs within each QTL window. GS accuracy was evaluated using the correlation between true and estimated breeding values for 5000 randomly selected animals from G10. Results: Prioritizing SNPs using FST scores within QTL window regions increased accuracy by 5 to 18%, with the 50-SNP windows showing the best performance. Conclusions: The increase in GS accuracy warrants the testing of the algorithm when the number and position of QTL are unknown. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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16 pages, 1288 KiB  
Review
Serum Amyloid A: A Double-Edged Sword in Health and Disease
by Ailing Ji, Luke W. Meredith and Preetha Shridas
Int. J. Mol. Sci. 2025, 26(10), 4528; https://doi.org/10.3390/ijms26104528 - 9 May 2025
Viewed by 793
Abstract
Despite more than fifty years since its discovery in the 1970s, Serum Amyloid A (SAA)’s true biological functions remain enigmatic. The research so far has primarily associated SAA with chronic inflammatory conditions such as cardiovascular disease, obesity, and type 2 diabetes; its role [...] Read more.
Despite more than fifty years since its discovery in the 1970s, Serum Amyloid A (SAA)’s true biological functions remain enigmatic. The research so far has primarily associated SAA with chronic inflammatory conditions such as cardiovascular disease, obesity, and type 2 diabetes; its role in acute inflammation is less understood. Unlike the modest elevations observed in chronic conditions, SAA levels surge dramatically during acute inflammatory responses. Notably, approximately 2.5% of hepatic protein synthesis is devoted to SAA production during acute inflammation—despite the high energy demands required for synthesizing pro-inflammatory cytokines and immune cell activation—leaving its precise necessity unclear. Elucidating SAA’s physiological role in acute inflammation is crucial to determine the therapeutic potential of SAA inhibition for chronic inflammatory diseases, such as atherosclerosis and abdominal aortic aneurysms. The evidence suggests that SAA may play a protective role in acute inflammation, positioning it as a “double-edged sword”: detrimental in chronic inflammation, yet potentially beneficial in acute settings. This review explores the divergent roles of SAA in chronic versus acute inflammation, proposing that while SAA inhibition could offer therapeutic benefits for chronic conditions, it might pose risks during acute inflammation. As the primary transporter of SAA in circulation, high-density lipoprotein (HDL) has been shown to become dysfunctional in chronic inflammation, at least partly due to SAA’s effects. However, we propose that SAA may confer functional properties to HDL during acute inflammatory states, such as sepsis, thereby highlighting the context-dependent nature of its impact. Full article
(This article belongs to the Section Molecular Biology)
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19 pages, 4907 KiB  
Article
Synergistic Framework for Fuel Cell Mass Transport Optimization: Coupling Reduced-Order Models with Machine Learning Surrogates
by Shixin Li, Qingshan Liu and Yisong Chen
Energies 2025, 18(10), 2414; https://doi.org/10.3390/en18102414 - 8 May 2025
Viewed by 421
Abstract
Facing the complex coupled process of thermal mass transfer and electrochemical reaction inside fuel cells, the development of a one-dimensional model is an efficient solution to study the influence of mass transfer property parameters on the transfer and reaction process, which can effectively [...] Read more.
Facing the complex coupled process of thermal mass transfer and electrochemical reaction inside fuel cells, the development of a one-dimensional model is an efficient solution to study the influence of mass transfer property parameters on the transfer and reaction process, which can effectively balance the computational efficiency and accuracy. Firstly, a one-dimensional two-phase non-isothermal parametric model is established to capture the performance and state of fuel cell quickly. Then, a sensitivity analysis is performed on various mass transfer parameters of the membrane electrode assembly. Subsequently, a neural network surrogate model and genetic algorithm are combined to optimize the mass transfer property parameters globally. The impact of these parameters on the thermal and mass transfer within the fuel cell is analyzed. The results show that the maximum error between the calculation results of the developed numerical model and the experimental results is 3.87%, and the maximum error between the predicted values of the trained surrogate model and the true values is 0.15%. The mass transfer characteristics of the gas diffusion layer have the most significant impact on the performance of the fuel cell. After optimizing the mass transfer characteristic parameters, the net power density of the fuel cell increased by 5.51%. The combination of the one-dimensional model, the surrogate model, and the genetic algorithm can effectively improve the optimization efficiency. Full article
(This article belongs to the Section D2: Electrochem: Batteries, Fuel Cells, Capacitors)
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12 pages, 786 KiB  
Article
Early Postoperative Increase in Transforming Growth Factor Beta-1 Predicts Microvascular Flap Loss in Reconstructive Surgery: A Prospective Cohort Study
by Rihards Peteris Rocans, Janis Zarins, Evita Bine, Insana Mahauri, Renars Deksnis, Margarita Citovica, Simona Donina, Sabine Gravelsina, Anda Vilmane, Santa Rasa-Dzelzkaleja, Olegs Sabelnikovs and Biruta Mamaja
Medicina 2025, 61(5), 863; https://doi.org/10.3390/medicina61050863 - 8 May 2025
Viewed by 410
Abstract
Background and Objectives: Microvascular flap surgery is a widely used reconstructive technique for the repair of various defects. Biomarkers have become an essential tool for monitoring flap viability, early detection of complications, and prediction of surgical outcomes. Studies focusing on immunomodulatory cytokines in [...] Read more.
Background and Objectives: Microvascular flap surgery is a widely used reconstructive technique for the repair of various defects. Biomarkers have become an essential tool for monitoring flap viability, early detection of complications, and prediction of surgical outcomes. Studies focusing on immunomodulatory cytokines in the early prediction of microvascular flap complications are lacking. We aimed to investigate the predictive value of postoperative changes in transforming growth factor beta-1 (TGF-β1) for microvascular flap complications. Materials and Methods: This prospective observational study comprised 44 adults scheduled for elective microvascular flap surgery. Preoperative blood samples for analysis were obtained before surgery, prior to the administration of intravenous fluids. Postoperative blood draws were collected after surgery, before leaving the operating room. Preoperative and postoperative serum concentrations of TGF-β1, as well as preoperative plasma albumin, total protein, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, full blood count, albumin, interleukin-6, C-reactive protein, and fibrinogen, were determined. Results: Postoperative changes in TGF-β1 were higher in cases with flap loss compared to patients with healthy recovery or patients with minor flap complications (0.403 log10 of ng/mL [0.024–0.782] vs. 0.157 [0.029–0.285] vs. −0.089 [−0.233–0.056], p = 0.002). Increased postoperative TGF-β1 was positively linked to preoperative C-reactive protein (p = 0.021), fibrinogen (p = 0.020), hematocrit (p = 0.039), and hemoglobin (p = 0.009). Conclusions: The postoperative increase in circulating TGF-β1 was associated with microvascular flap complications. Assessment of the postoperative changes in circulating TGF-β1 may be valuable for the early postoperative prediction of true flap loss. Full article
(This article belongs to the Special Issue New Insights into Plastic and Reconstructive Surgery)
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