Simulated Values Research Articles

Two-phase model for inverse Hall–Petch effect in nanocrystalline thin film: Atomistic simulation study

This work presents two methods to improve the understanding of the mechanical behavior of nanocrystalline thin films. Firstly, a simple two-phase model is proposed to explain the inverse Hall–Petch effect. The model suggests that the nanocrystalline material consists of the grain-boundary and the grain interior with different mechanical properties. Secondly, a computational method is developed to create more realistic grain boundaries in simulations of polycrystals. Traditionally created grain boundaries by Voronoi tessellation are too dense and do not accurately reflect the reality and the experimental results. The strength of the nanocrystalline aluminum thin film is simulated using molecular dynamics. The grain size dependence of the elastic modulus, ultimate tensile stress, and engineering yield strength is demonstrated. The experimental values of modulus and strength are approximately 6 times smaller probably due to the presence of porosity in the real sample. An approach is developed to introduce porosity in the simulated samples at the grain boundaries and in the grain interior to reduce this discrepancy. The modeled value of modulus for a grain size of 40 nm without porosity is 67GPa, whereas, with 50% grain-boundary porosity and 20% intra-granular porosity it is 30GPa. For the ultimate tensile strength, we get 3GPa and 1.4GPa for samples without porosity and with porosity, respectively. The simulated values of modulus with porosity are still 4 times higher and the values of strength are about 2 times higher than the experimental ones. The amount of porosity in our method can be adjusted to fit the experimental values; however, high values of porosity cause the mechanical instability of the simulated samples.

Exploring nitrogen doped mesoporous carbon spheres for superior microwave absorption

Mesoporous carbon materials hold significant promise for usage in microwave absorption due to their wide and unique advantages stemming from their mesoporous structure. This study synthesizes N-doped mesoporous carbon spheres (NMCs) by carbonizing mesoporous silica nanospheres (MSN). By controlling the annealing temperature, the resulting NMCs exhibit a significant increase in pore size. The exceptional microwave absorption capability of multifunctional carbon with a mesoporous structure result from its appropriate composition. The minimum reflection loss (RL) can reach −58.04 dB with a thickness of 3.3 mm, and the corresponding effective absorption bandwidth (EAB) reaches up to 8.19 GHz, outperforming the many previously documented microwave absorbing materials. The material's practical application potential was evaluated using computer simulation technology (CST), resulting in a simulated RCS value of 32.88 dBm2.The microwave absorption process of this material has been extensively studied and thoroughly described. Our study offers valuable guidance for developing and producing efficient microwave absorbers doped with nitrogen and possessing a unique mesoporous structure.

Using the AIEM and Radarsat-2 SAR to Retrieve Bare Surface Soil Moisture

Taking the Jiefangzha irrigation area of the Inner Mongolia Autonomous Region as the research area, the response relationships between the backscattering coefficient and radar frequency, radar incidence angle, root-mean-square height, correlation length, and soil water content under different conditions were simulated using advanced integral equations. The backscattering characteristics of exposed surfaces in cold and dry irrigation areas were discussed, and the reasons for the different effects were analyzed. Based on this, surface roughness models and statistical regression moisture inversion models were constructed through co-polarized backscatter coefficients and combined surface roughness. The correlation between the inverted surface roughness values and the measured values was R2 = 0.7569. The correlation between the soil moisture simulation values and the measured values was R2 = 0.8501, with an RMSE of 0.04. The findings showed a strong correlation between the values from the regression simulation and the measured data, indicating that the model can be applied to soil moisture inversion and has a good inversion accuracy. Compared with previous studies in the same area, the inversion model proposed in this paper has a higher accuracy and is more suitable for the inversion of soil moisture in the Jiefangzha irrigation area. These findings can support research on the water cycle and water environment assessment in the region.

Steady-state rolling resistance prediction model of non-pneumatic tyres considering tread temperature: theory and experiment

Non-pneumatic tires have potential advantages over pneumatic tires, including lower rolling resistance. First, a non-pneumatic mechanical elastic wheel (ME-Wheel) was designed. Based on its structural characteristics, a tread temperature prediction model under steady-state rolling conditions was established, and the accuracy of the established model was verified through temperature measurement experiments. Combined with the tread temperature prediction model, a steady-state rolling resistance prediction model of ME-Wheel was established, and the basic parameters (ground length CL , ground width CW , radial stiffness SR ) required for the model were identified through experiments. Finally, the experimental data were compared with the predicted values under different speed and load conditions. The results showed that the average simulation value of the rolling resistance coefficient of ME-Wheel was 0.01853, which was positively correlated with speed and load. The test results and predicted values showed a similar trend under the same operating conditions, with a maximum error of less than 3.5%. This proved that the novel non-pneumatic tyre steady-state rolling resistance prediction model based on tread temperature could accurately predict the steady-state rolling resistance of tyres. This research addresses a significant topic in tyre technology and has the potential to influence future designs and optimizations of non-pneumatic tyres.

Simulating Civil Society in the Middle East and North Africa (MENA): Fog, Friction, and the Lebanese Port Explosion

Introduction Though the effects of fog and friction on decision-making are frequently simulated in military settings, they are less frequently extended to non-military and non-governmental MENA spaces. This article provides a description and evaluation of a disaster simulation rooted in the real-world event of the August 4, 2020 Lebanese port explosion. The aim of the simulation is to prompt student learning of the decision-making dynamics of CSOs amidst fog and friction in democratizing MENA spaces. Background A brief overview of the Lebanon-CSO landscape is provided. As part of their ruling strategy, sectarian elites engineer fog and friction to co-opt and fragment CSOs. This complex power sharing milieu creates dilemmas for CSOs. The Intervention The simulation was designed for an entire classroom of students to experience the fog and friction in decision-making that CSOs encounter in MENA conditions of limited resources and urgent humanitarian need. Methods and Limitations Student learning, framed by Bloom’s taxonomy of levels of intellectual behavior, was assessed using qualitative entry and exit classroom surveys based on three iterations of the simulation. Qualitative interpretive content analysis was used to categorize survey responses. The sample is not statistically significant given the small class sizes at the College. The study serves as an initial exploration for assessing the promising pedagogical benefits of the Lebanon port explosion simulation. Results Before the simulation, students had knowledge and understanding of CSOs, democratization, and the Lebanese context. After the simulation, student surveys yielded results that evidenced higher- ordered learning. Students discovered the challenge of applying their knowledge of Lebanese CSOs amidst fog. Next, the panel stress- testing and critical reflection essay exercises prompted an analysis and evaluation of CSOs as weak or fraught vehicles for democratization in the region. In sum, fog and friction prompted students to experience and learn how CSOs may subvert democratization in the MENA despite intentions to “do good.” Discussion The Lebanon port explosion survey data indicate the value of simulations as “experience vehicles” that may push students to higher orders of abstracted thinking about CSOs in the MENA. Conclusion Coupled with historical knowledge of co-optation and fragmentation strategies of elites vis-à-vis CSOs in Lebanon, the classroom simulation experience provided a low-cost and high-reward learning opportunity for students to grapple with the constraints and consequences of fog and friction on CSO decision-making.

Reaction Rate Ratios for Recent Fast Metal Experiments with Large Plutonium Masses

Reaction rate ratios are integral responses that are used within the criticality experiments field because they contain spectral information. While these types of measurements have been utilized for nuclear data validation with historic experiments, few experiments of this type have been utilized for recent experiments, as few exist. This work focuses on measured reaction rate ratios for two nearly bare plutonium critical assemblies with different geometries: one that is cube like (with a Pu mass of 40 kg) and one that is slab like (with a Pu mass of 109 kg). Irradiations were performed with both configurations in which foils were placed near the center of the assembly. Plutonium, highly enriched uranium, depleted uranium, and Au foils were included in the irradiation and counted via high-purity germanium detectors. From these measurements, reaction rate ratios were calculated. Measured and simulated values and uncertainties are presented for the reaction rate ratios. Ratios utilizing the following reactions are given in this work: 197Au(n, γ ), 197Au(n,2n), 235U(n,fission), 238U(n,fission), 238U(n,2n), 238U(n, γ ), and 239Pu(n,fission). Uncertainties for the measured reaction rate ratios ranged from 4% to 7%, and the contribution of various parameters to this uncertainty was investigated. The results are compared to historical experiments and should be used for nuclear data validation for future nuclear data library releases. These measurements are part of the EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) project, which utilizes measurement responses in addition to keff (such as these reaction rate ratios) to help reduce uncertainties in 239Pu nuclear data.

Switching diffusions for multiscale uncertainty quantification

High-fidelity simulations are increasingly called-upon to resolve the nucleation of instabilities such as crack initiation and propagation and fluid mixing. While multiscale modeling capabilities have progressed rapidly over recent decades, associated simulations still present a computational burden, thus often preempting a systematic statistical analysis of related problems. Given the paucity of data that relate micro-structure to macroscale behavior, and the unavoidable modeling errors at both scales, the lack of a probabilistic characterization significantly limits the value of highly-resolved numerical simulators. The goal of the present research is to develop a probabilistic model that encodes pathwise relationships between micro and macro scales. Specifically, we develop a switching diffusion model to relate damage evolution, characterized at the microscale, to system performance identified with a macroscale property. Microscale behavior is modeled as a Markov switching process with a finite state space while the macroscale counterpart is modeled as a continuous-state diffusion process. The interaction between macro and micro scales is captured by coupling these two processes. Calibrated by data, the switching diffusion model can generate, with minimal computational effort, sample paths with prescribed statistics. The proposed model contributes new capabilities and perspectives at the interface of multiscale simulation, uncertainty quantification, and stochastic modeling.

Lateral Performance Analysis of Trapezoidal Orthogonal Stiffened Steel Plate Shear Walls

This paper introduces a trapezoidal orthogonal stiffener steel plate shear wall (TSW). The finite element model of the TSW was developed following the validation of low-cycle repeated tests conducted on a single-span double-layer steel plate shear wall. The paper studies the effects of the flat steel plate thickness, stiffener thickness, stiffener height, and stiffener bottom width on the seismic performance of TSW. Building upon these findings, a theoretical formula for the ultimate shear capacity of TSW was developed. The results prove the following: (1) By changing the flat steel plate thickness, the stiffener thickness, and the stiffener height, the seismic behavior of TSW can be enhanced. It is suggested that the flat steel plate thickness is 4~6 mm, the stiffener thickness is 4~6 mm, and the stiffener height is not more than 60 mm, while the effect of the stiffener bottom width on the seismic behavior of TSW can be neglected. (2) The maximum error is 22.16%, compared to the theoretical value of TSW ultimate shear capacity with the finite element simulation value. However, as the finite element results surpass the test results, it indicates that the formula-derived results are unsafe, necessitating a recommendation for correction.

Empirical models for calculating soil wetting patterns under surface drip irrigation systems: A comprehensive analysis

AbstractAccurate estimation of the surface wetted radius (R) and vertical wetted depth (Z) of wetting patterns in drip irrigation systems is crucial for ensuring that the designs of such systems are effective. This study compared 14 empirical models for estimating drip irrigation wetting patterns by assessing their accuracy using published measurement data and HYDRUS‐2D/3D simulations. The technique for order of preference by similarity to the ideal solution (TOPSIS) was employed to comprehensively rank the models. The results indicate that the empirical model proposed by Fan et al. (2023) (FY) exhibited the highest accuracy when the estimations of R and measured and simulated values were compared, with mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe modelling efficiency (NSE), and percent bias (PB) values of 2.2 cm, 3.4 cm, 0.79, and −7.1% and 5.2 cm, 7.0 cm, 0.50, and −14.1%, respectively. The empirical model proposed by Amin and Ekhmaj (2006) (AE) demonstrated the highest accuracy when the estimations of Z were compared with measured and simulated values, with MAE, RMSE, NSE and PB values of 1.7 cm, 2.0 cm, 0.95 and 4.15% and 4.4 cm, 5.9 cm, 0.82 and 4.7%, respectively. The comprehensive rankings of available models in the present study indicate that the FY model is the most universally applicable, followed by the Li et al. (2022) (LY) model, with comprehensive indices of 0.960 and 0.936, respectively. This research can aid in the selection of universally applicable, reliable and straightforward empirical models for estimating wetting patterns in drip irrigation systems.

Properties of ethanol-based foamed asphalt binders using the molecular dynamics (MD) method

The molecular structure models of asphalt binder, ethanol additive, and ethanol-based foamed asphalt were constructed through the Molecular Dynamics (MD) method. The standard ethanol-based foamed asphalt model was employed to describe the modifier with its different compositions, including 10%, 20%, and 30% ethanol. The simulation calculations were done for the ethanol-based foamed asphalt molecular models under the NPT and NVT ensembles. The density, glass transition temperature, and radial distribution function of ethanol-based foamed asphalt molecular models were obtained to verify the rationalization of asphalt models and analyze the variation of density parameters with temperature and ethanol content for ethanol-based foamed asphalt molecular models. The results show that the simulated densities of the asphalt binder and three ethanol-based foamed asphalt molecular models remained constant with the increase of simulation steps. The simulated density values of basic and 10%-ethanol-based foamed asphalt molecular models are close to 0.9 g cm−3. The simulated densities of 20%-ethanol-based and 30%-ethanol-based foamed asphalt molecular models were 0.8 g cm−3 and 0.75 g cm−3. Meanwhile, the simulated density values of both asphalt binder and all ethanol-based foamed asphalt decreased with the increase in temperature and ethanol additive dosage. The glass transition temperatures of basic asphalt binder, 10%-ethanol-based, 20%-ethanol-based, and 30%-ethanol-based foamed asphalt occurred in the range of 275–295 K, 330–350 K, 330–350 K, and 320–340 K, respectively. In contrast, the glass transition temperature of ethanol-based foamed asphalt increased with the increase of ethanol additive dosage, indicating that adding ethanol additive significantly improved the high-temperature resistance of matrix asphalt. In the radial distribution function diagrams of all samples, the first strong peak appeared at 0.85–1.3 Å, and the second strong peak appeared at 1.95–2.35 Å. Moreover, both peaks increased with the increase of ethanol additive dosage, suggesting that the contact between ethanol molecules and asphalt molecules was closer with the rise of ethanol additive dosage.

Numerical Coupling Simulation of the Vertical Blowing Suspension Position of Tea Leaves Based on Computational Fluid Dynamics and the Discrete Element Method

To provide reference for the design of the air-suction tea sorting device, the coupled numerical simulation model was established by the coupling method of computational fluid dynamics (CFD) and discrete element method (DEM) with tea of different quality as test objects, and the model was verified experimentally. Regarding tea particles of different quality, when the test tea particle mass was 0.215, the test value was located in the simulation value with a minimum error of 9 mm, which an error rate of 3.33%, and maximum error of 19 mm, with an error rate of 7.03%. When the test tea particle mass was 0.145, the minimum error of the test value was 5 mm and the error rate was 1.54%, and the maximum error was 9 mm and the error rate was 3.33%. The verification results established the accuracy of the model. During the suspension test and simulation, tea particles were affected by the air flow field of the observation tube, and tea particles fluctuated. During suspension, tea particles were attached to the inner wall of the observation tube under the action of the air flow field. An in-depth study showed that the relationship between the different distances from the initial position of the particles during suspension and the simulation time was a peak function. The extreme function is used to fit the actual trajectory, and the fitting degree is good. The fitting degree of the particle closest to the initial position was 0.9455, and the fitting degree of the particle farthest from the initial position was 0.9981.

Application of Monte Carlo simulation to optimise the dosage regimen of meropenem in patients with augmented renal clearance for Pseudomonas aeruginosa infection

ObjectiveTo optimise the dosing regimen of meropenem for treating Pseudomonas aeruginosa (PA) infections in critically ill patients with augmented renal clearance (ARC) using pharmacokinetic/pharmacodynamic (PK/PD) principles and Monte Carlo simulation (MCS). MethodsThis research involves an MCS based on PK data from patients with ARC and a minimum inhibitory concentration (MIC) distribution of PA. This study simplifies the methods section, focusing on the critical aspects of simulation and target values for effective treatment. ResultsThe study highlights key findings and emphasises that tailored dosing based on bacterial MIC values is essential for patients with ARC. It also notes that empirical treatment in patients with ARC should consider the MIC distribution, with 2 g every (q) 6 h administered to achieve the PK/PD target, while 3 g q 6 h is effective in inhibiting resistance. ConclusionTailored dosing based on bacterial MIC values is crucial for patients with ARC. Prolonged infusion time alone does not enhance efficacy. Empirical treatment in patients with ARC should consider MIC distribution; a dosage of 2 g q 6 h achieves the PK/PD target, while 3 g q 6 h (≥12 g daily) inhibits resistance.

Low insertion loss open-loop resonator–based microstrip diplexer with high selective for wireless applications

This paper presents a low-insertion-loss open-loop resonator (OLR)-based microstrip diplexer with high-selective for wireless applications. We used two series capacitive gaps in the microstrip transmission line, loaded with rectangular-shaped half-wavelength OLRs, to create a high-selectivity bandpass filter (BPF). The planned BPFs are linked through a T-junction combiner, precisely tuned to align with both filters and the antenna port in order to produce the proposed diplexer. The system is implemented on a rogers TMM4 substrate with a loss tangent of 0.002, a dielectric constant of 4.7, and a thickness of 1.52 mm. The suggested diplexer has dimensions of (90×70) mm². It achieves a modest frequency space ratio of R=0.1646 in both transmit and receive modes by having two resonance frequencies of ft=2.191 GHz and fr=2.584 GHz, respectively. The simulated structure displays good insertion losses of approximately 1.2 dB and 1.79 dB for the two channels, respectively, at fractional bandwidths of 1.24% at 2.191 GHz and 0.636% at 2.584 GHz. The simulated isolation values for 2.191 GHz and 2.584 GHz are 53.3 dB and 66.5 dB, respectively.

Kinematic modelling of three link robot manipulator and joint torque optimization using genetic algorithm in MATLAB

This research article presents the non-linear dynamic of a three-link robotic manipulator formulated by the Newton-Euler method. The planar manipulator is composed of three links and three revolute joints rotating about the z-axis. The three nonlinear non-homogeneous dynamic equations have been solved graphically with the help of MATLAB by phase variable method. The work represents the graphical solution of the transient response of angular position, and angular velocity of each link member for a predetermined interval of time. With the help of simulated value from MATLAB, torque characteristics have been determined for different torque ratios and optimum torque has been derived using a genetic algorithm to move the manipulator in a proper direction.

Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modeling

ABSTRACT OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

48‐1: A Data‐Driven Intelligent Stress Monitoring for a Robust Manufacturing of a Phone Display with the Extremely Narrow Bottom Bezel

A data‐driven intelligent stress monitoring methodology is proposed for a robust manufacturing of a phone display with the extremely narrow bottom bezel. For the narrow bottom bezel, a bottom of a display panel should be bent 180 degrees with asymmetric bended protrusion by tight attachment. The mechanical stress of the bended panel increases rapidly depending on the amount attached after bending 180 degrees. For stable operation of assembly process with high risk process method, the virtual stress monitoring are applied for total product samples during the process. The intelligent stress sensor produces the virtual stress/strain data using a surrogated model, which is learned with the dataset of measured data and simulated data of the tightly bending attachment using it as input. The virtual stress/strain data which matches 99.4% of the simulation value with regression error of less than 1% can be obtained in real time from all products during assembly process with bending of the display panel. The crack risks of the bending 180 degrees are monitored in real time for anomaly detection using the virtual stress data produced by the proposed virtual sensor. With these approaches, we believe that the manufacturing processes having high risks could be stably controlled for a real infinite display creating an incredibly immersive viewing experience.

Influence of urbanization on meteorological conditions and ozone pollution in the Central Plains Urban Agglomeration, China

Changes in aerodynamic and thermal conditions caused by urbanization can impact regional meteorological conditions, subsequently affecting air quality. Updated Moderate-resolution Imaging Spectroradiometer (MODIS) land use data and coupled with the urban canopy models (UCMs) in the Weather Research and Forecasting (WRF) model. This enabled the impact of urban land expansion on meteorological conditions and ozone (O3) concentrations to be evaluated. Urban expansion increased the temperature at 2 m (T2) and the probability of precipitation in urban expansion areas, and enhanced the surface urban heat island at night. As the expansion areas became progressively larger, the increase in T2 became more pronounced. The proportions of urban surfaces in June 2016, 2018, and 2020 compared to 2001 increased by 0.69%, 0.83%, and 1.04%, respectively, while T2 increased by 0.12, 0.19, and 0.20 °C in urban areas, respectively. With urban expansion, the O3 concentration increased by 1.12, 1.37, and 0.76 μg/m3 (three-year averages) in urban, suburban, and rural areas, respectively. After coupling a multi-layer urban canopy model (building effect parameterization, BEP), or a multi-layer urban canopy model with a building energy model including anthropogenic heat due to air conditioning (BEP + BEM, abbreviated as BEM simulation), the O3 concentration changed significantly in the urban expansion area, compared to the results of a single-layer urban canopy model (UCM). O3 concentrations decreased most in the BEP simulation (−0.77 μg/m3), while O3 concentrations increased most in the BEM simulation (+1.85 μg/m3). The average observed O3 concentration was 108.35 μg/m3 (three-year average), while the simulated value was 75.65–83.72 μg/m3 (R = 0.69−0.77). The validation results in the BEM and Global Optimal Scenario (GOS) simulations were relatively good, with the GOS simulation producing slightly better results than the BEM. The simulation of O3 in urban agglomerations could be improved by integrating the results of the UCMs.

Optimization of aerospace engine bolt tightening based on Kriging model

Bolt tightening is widely used in various types of aerospace engine assembly process. To ensure the tightness of the aerospace engine, it is extremely important to reduce the deformation of the engine and achieve uniform bolt preload in the tightening process. The initial clearance value between nozzle and chamber of different types of aerospace engine is different, which makes the tightening target of low deformation and even load difficult. Besides, the impact of elastic interaction makes the tightening of aerospace engine very challenging. And aerospace engine bolt tightening is too time-consuming. A method is proposed in this paper to solve the above puzzle. The influence of elastic interaction in the tightening process can be weakened by optimizing the tightening sequence, and the optimal tightening process of different types of aerospace engines under different clearance values can be predicted based on kriging model. The experiment shows that the deformation of the engine deduce by 82% and 72%, the average preload deduce by 3.8% and 5.1%. Then kriging model is used to predict the best tightening sequence under different types and tightening clearance. The genetic algorithm is used to obtain the best sequence in the design domain, and the deviation between the final predicted value and the simulation value is less than 3%. After comparing, the research also improves the efficiency of process planning of and medium-sized batches and multiple batches of aerospace engines.

Unveiling the Molecular Dynamics, Anticancer Activity, and Stability of Spearmint Oil Nanoemulsions with Triglycerides.

Although spearmint oil (SMO) has various pharmacological properties, especially for cancer treatment, its low water solubility results in poor bioavailability. This limits its application as a medicine. One possible solution is to the use of SMO in the form of nanoemulsion, which has already been shown to have anticancer effects. However, the mechanism of SMO nanoemulsion formation remains unclear. The objective of this study was to use molecular dynamics (MD) for clarifying the formation of SMO nanoemulsion with triglycerides (trilaurin, tripalmitin, and triolein) and Cremophor RH40 (PCO40). Nanoemulsions with different SMO:triglyceride ratios and triglyceride types were prepared and analyzed for anticancer activity, droplet size, droplet morphology, and stability. Despite switching the type of carrier oil, SMO nanoemulsions retained strong anticancer effects. A ratio of 80SMO:20triglycerides produced the smallest droplets (<100 nm) and exhibited excellent physical stability after a temperature cycling test. MD simulations showed that polyoxyethylenes of PCO40 are located at the water interface, stabilizing the emulsion structure in an egglike layer. Droplet size correlated with triglyceride concentration, which was consistent with the experimental findings. Decreasing triglyceride content, except for the 90SMO:10triglyceride ratio, led to a decrease in droplet sizes. Hydrogen bond analysis identified interactions between triglyceride-PCO40 and carvone-PCO40. Geometry analysis showed PCO40 had an "L-like" shape, which maximizes the hydrophilic interfaces. These findings highlight the value of MD simulations in understanding the formation mechanism of SMO and triglyceride nanoemulsions. In addition, it might also be beneficial to use MD simulations before the experiment to select the potential composition for nanoemulsions, especially essential oil nanoemulsions.

Exploring cumulative probability functions for streamflow drought magnitude: Global scale analysis and parametric vs. non-parametric comparisons

Streamflow droughts pose significant challenges to water resource management and environmental sustainability. The accurate characterization of streamflow drought magnitude is essential for effective mitigation, assessment and adaptation strategies. In this study, we explore the implications of choosing different cumulative probability distribution functions (cdfs) on the determination of streamflow drought magnitude, as assessed by the z-score values of the standardized streamflow index (SSI) and return periods. Our investigation encompasses a comprehensive global-scale analysis, with the aim of identifying the most suitable cdfs for characterizing streamflow droughts. To accomplish this objective, we incorporate global simulated streamflow values from the model WaterGAP 2.2d and observed streamflow values from 280 Global Runoff Data Centre (GRDC) stations for the period 1981 – 2010. We evaluate the performance of 118 parametric cdfs for each of the 12 calendar monthly streamflow values at each model grid cell and GRDC station to determine the best fitting parametric cdf. Moreover, we go beyond the conventional scope of such studies by further assessing the best-fitting cdfs alongside non-parametric cdfs such as the Empirical cumulative distribution function (ecdf) and the cumulative distribution function derived from the Kernel density estimation function (kcdf). Among our findings, we establish a subset of 23 parametric cdfs that exhibit the best fit for the global streamflow values concerning all 12 calendar monthly streamflow values at individual GRDC stations or grid cells within the specified period. Furthermore, we observe that the accuracy of streamflow simulation and the length of the reference period directly influence the selection of the most suitable parametric cdf. Our comparative analysis reaffirms the existing understanding that opting for the best-fitting parametric cdf, rather than a non-parametric alternative, mitigates the risk of overestimating drought magnitudes. However, we emphasize the crucial importance of considering the intended purpose, approach-specific sensitivities, and uncertainties when choosing the most suitable parametric cdf for any calendar monthly streamflow values.