Model Simulations Research Articles

Computer Simulation for Effective Pharmaceutical Kinetics and Dynamics: A Review.

Computer-based modelling and simulation are developing as effective tools for supplementing biological data processing and interpretation. It helps to accelerate the creation of dosage forms at a lower cost and with the less human effort required to conduct the work. This paper aims to provide a comprehensive description of the different computer simulation models for various drugs along with their outcomes. The data used are taken from different sources, including review papers from Science Direct, Elsevier, NCBI, and Web of Science from 1995-2020. Keywords like - pharmacokinetic, pharmacodynamics, computer simulation, whole-cell model, and cell simulation, were used for the search process. The use of computer simulation helps speed up the creation of new dosage forms at a lower cost and less human effort required to complete the work. It is also widely used as a technique for researching the structure and dynamics of lipids and proteins found in membranes. It also facilitates both the diagnosis and prevention of illness. Conventional data analysis methods cannot assess and comprehend the huge amount, size, and complexity of data collected by in vitro, in vivo</i>, and ex vivo</i> experiments. As a result, numerous in silico</i> computational e-resources, databases, and simulation software are employed to determine pharmacokinetic (PK) and pharmacodynamic (PD) parameters for illness management. These techniques aid in the provision of multiscale representations of biological processes, beginning with proteins and genes and progressing through cells, isolated tissues and organs, and the whole organism.

Optimization of oral isavuconazole dose for population in special physiological or pathological state: a physiologically based pharmacokinetics model-informed precision dosing.

To recommend precision dosing and improve therapeutic efficacy against invasive fungal disease, a physiologically based pharmacokinetic model (PBPK) of oral isavuconazole (ISA) was established and used to explore its disposition across populations in different physiological and pathological states. Twenty-five pharmacokinetic (PK) studies of oral ISA were identified through a systematic search of PubMed. Concentration-time data were extracted using WebPlotDigitizer. Physiochemical parameters were obtained from published literature and DrugBank. Model development and simulation used the Simcyp population-based simulator, and visual predictive check and predictive error were used for the model evaluation. Probability of target attainment and the cumulative fraction of response analyses were performed for dose optimization. The developed PBPK model was successfully validated in different populations. Most predicted concentration-time points aligned with the observed data, with acceptable predictive errors for the critical parameters. We predicted the PK profiles and parameters of ISA in a population with severe hepatic impairment (HI), a population with obesity and paediatric patients aged 1 to less than 6 years old. The probability of target attainment and cumulative fraction of response analyses indicated that the population with severe HI should have half the maintenance dose. The population with obesity and population with severe HI should have a loading dose of 300 mg every 8 h for 2 days. For paediatric patients aged 1 to less than 6 years old, a weight-based dosing regimen (5.38 mg/kg) of ISA was suggested. The predicted value aligns with observations, suggesting ISA's potential predictability in PK profiles for other populations. The recommended dosing regimens increase our understanding of the use of ISA in special populations.

Cost-Effectiveness of Low-Dose Computed Tomography Screenings for Lung Cancer in High-Risk Populations: A Markov Model.

Domestic and foreign studies on lung cancer have been oriented to the medical efficacy of low-dose computed tomography (LDCT), but there is a lack of studies on the costs, value and cost-effectiveness of the treatment. There is a scarcity of conclusive evidence regarding the cost-effectiveness of LDCT within the specific context of Taiwan. This study is designed to address this gap by conducting a comprehensive analysis of the cost-effectiveness of LDCT and chest X-ray (CXR) as screening methods for lung cancer. Markov decision model simulation was used to estimate the cost-effectiveness of biennial screening with LDCT and CXR based on a health provider perspective. Inputs are based on probabilities, health status utility (quality-adjusted life years (QALYs)), costs of lung cancer screening, diagnosis, and treatment from the literatures, and expert opinion. A total of 1,000 simulations and five cycles of Markov bootstrapping simulations were performed to compare the incremental cost-utility ratio (ICUR) of these two screening strategies. Probability and one-way sensitivity analyses were also performed. The ICUR of early lung cancer screening compared LDCT to CXR is $-24,757.65/QALYs, and 100% of the probability agree to adopt it under a willingness-to-pay (WTP) threshold of the Taiwan gross domestic product (GDP) per capita ($35,513). The one-way sensitivity analysis also showed that ICUR depends heavily on recall rate. Based on the prevalence rate of 39.7 lung cancer cases per 100,000 people in 2020, it could be estimated that LDCT screening for high-risk populations could save $17,154,115. LDCT can detect more early lung cancers, reduce mortality and is cost-saving than CXR in a long-term simulation of Taiwan's healthcare system. This study provides valuable insights for healthcare decision-makers and suggests analyzing cost-effectiveness for additional variables in future research.

Controllable cross-building multi-objective optimisation for NZEBs: A framework Utilising parametric generation and intelligent algorithms

Balancing performance and cost are crucial in optimising nearly zero-energy buildings. This study proposes a method for efficient preliminary design tailored to diverse requirements. It starts with parametric modelling and performance simulation to compile a dataset of common spatial forms in almost zero-energy office buildings. Subsequently, a multilayer perceptron surrogate model is trained on this dataset to map the relationships between design parameters and performance objectives across various spatial forms. A controllable NSGA-II constraint method enables designers to manage design variables and performance objectives independently for cross-building optimisation. The framework integrates the surrogate model with the NSGA-II algorithm under precise constraints, using TOPSIS to finalise Pareto solutions, and includes visualisation software for architects. The framework is validated in a high-performance office building in northern China by measuring data and using an equivalent model under two constraints. The results indicated that the framework reduced the energy consumption of the building by 6.6% and 14.7%, increased the thermal comfort time ratios by 2.3% and 11.4%, and lowered the initial investment costs by 19.6% and 16%, respectively, under each condition.

Power supply characterization of baseload and flexible enhanced geothermal systems

We investigated the techno-economic feasibility and power supply potential of enhanced geothermal systems (EGS) across the contiguous United States using a new subsurface temperature model and detailed simulations of EGS project life cycle. Under business-as-usual scenarios and across depths of 1–7 kilometers, we estimated 82,945 GW and 0.65 GW of EGS supply capacity with lower levelized cost of electricity than conventional hydrothermal and solar photovoltaic projects, respectively. Considering the scenario of flexible geothermal dispatch via wellhead throttling and power plant bypass, these estimates climbed up to 184,112 GW and 44.66 GW, respectively. The majority of EGS supply potential was found in the Western and Southwestern regions of the United States, where California, Oregon, Nevada, Montana, and Texas had the greatest EGS capacity potential. With advanced drilling rates based on state-of-the-art implementations of recent EGS projects, we estimated an average improvement of 25.1% in the levelized cost of electricity. These findings underscored the pivotal role of flexible operations in enhancing the competitiveness and scalability of EGS as a dispatchable renewable energy source.

Drought risk in Moldova under global warming and possible crop adaptation strategies.

This study analyzes the relationship between drought processes and crop yields in Moldova, together with the effects of possible future climate change on crops. The severity of drought is analyzed over time in Moldova using the Standard Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and their relationship with crop yields. In addition, rainfall variability and its relationship with crop yields are examined using spectral analysis and squared wavelet coherence. Observed station data (1950-2020 and 1850-2020), ERA5 reanalysis data (1950-2020), and climate model simulations (period 1970-2100) are used. Crop yield data (maize, sunflower, grape), data from experimental plots (wheat), and the Enhanced Vegetation Index from Moderate Resolution Imaging Spectroradiometer satellites were also used. Results show that although the severity of meteorological droughts has decreased in the last 170 years, the impact of precipitation deficits on different crop yields has increased, concurrent with a sharp increase in temperature, which negatively affected crop yields. Annual crops are now more vulnerable to natural rainfall variability and, in years characterized by rainfall deficits, the possibility of reductions in crop yield increases due to sharp increases in temperature. Projections reveal a pessimistic outlook in the absence of adaptation, highlighting the urgency of developing new agricultural management strategies.

Enhanced SWAT calibration through intelligent range-based parameter optimization

Hydrological models are vital tools in environmental management. Weaknesses in model robustness for hydrological parameters transfer uncertainties to the model outputs. For streamflow, the optimized parameters are the primary source of uncertainty. A reliable calibration approach that reduces prediction uncertainty in model simulations is crucial for enhancing model robustness and reliability. The optimization of parameter ranges is a key aspect of parameter calibration, yet there is a lack of literature addressing the optimization of parameter ranges in hydrological models. In this paper, we introduce a parameter calibration strategy that applies a clustering technique, specifically the Self-Organizing Map (SM), to intelligently navigate the parameter space during the calibration of the Soil and Water Assessment Tool (SWAT) model for monthly streamflow simulation in the Baishan Basin, Jilin Province, China. We selected the representative algorithm, the Sequential Uncertainty Fitting version 2 (SUFI-2), from the commonly used SWAT Calibration and Uncertainty Programs for comparison. We developed three schemes: SUFI-2, SUFI-2-Narrowing Down (SUFI-2-ND), and SM. Multiple diagnostic error metrics were used to compare simulation accuracy and prediction uncertainty. Among all schemes, SM outperformed the others in describing watershed streamflow, particularly excelling in the simulation of spring snowmelt runoff (baseflow period). Additionally, the prediction uncertainty was effectively controlled, demonstrating the SM's adaptability and reliability in the interval optimization process. This provides managers with more credible prediction results, highlighting its potential as a valuable calibration tool in hydrological modeling.

Comparison of in situ black-lipped oyster spat collection and larval dispersal modelling results in semi-closed pearl-farming lagoons of the Tuamotu Archipelago

Spat collection of the pearl oyster Pinctada margaritifera in atoll lagoons of French Polynesia is the fundamental sustain of black pearl farming. Spat collection has always yielded variable results in space and time, but obvious signs of steady decreases, even collapses, have emerged in several lagoons. Spat collection materializes the ecological connectivity pathways between wild spawning populations and the location of artificial larval settlement substrates. To assess if oyster larval dispersal modelling could capture such pathways, we compared four six-week long spat collector deployment periods with dispersal simulations in two different lagoons. Spat collectors displayed wide spatial and temporal variations. Numerical modelling and field experiments were generally not in agreement. Although both methods have limitations, they can still approximate each other. But the accuracy of model simulations cannot be ascertained with spat collection data only. Using a SWOT (Strength-Weakness-Opportunities-Threats) analysis, we emphasize the complementarity of both approaches for management decisions.

Movement of decaying quasi-2-day wave in the austral summer-time mesosphere

The quasi-2-day wave (Q2DW) is a large temperature disturbance in the austral summer-time mesosphere. Its decay-phase movement and phase-speed are poorly understood. Q2DW events observed by the microwave limb sounder (MLS) onboard the NASA Aura satellite reveal that during the temperature Q2DW’s decay-phase, the Q2DW temperature disturbance is still substantial, but its phase-speed reduces exponentially, on average, from around −70 to −20 m/s in around 30-days. Observations also reveal significant interannual variability in these phase-speed values. Q2DW events simulated by the extended Canadian middle atmosphere model (eCMAM) reveal that during the Q2DW decay-phase, the temperature Q2DW phase-speed is strongly correlated with the summer mesosphere easterly jet (SMEJ) wind values. eCMAM also shows that the phase-speed interannual variabilities partially reflect the interannual variabilities of the SMEJ. Planetary-wave diagnostics indicate that during the Q2DW’s decay phase, there is still an active transfer of momentum from the SMEJ into Q2DW. The model simulations therefore indicate that the SMEJ plays a substantial role in the decaying temperature Q2DW phase-speed variabilities observed by MLS. This adds to our knowledge of how the SMEJ affects the Q2DW.

Calculation of Lateral Logging Response and Environmental Impact Factor Analysis for Small Borehole Array

This paper refines an optimized array lateral logging tool designed for small boreholes, leveraging existing technologies. The tool features four investigation depth curves, and resistivity response curves are derived through finite element model simulations considering variables such as borehole size, mud characteristics, invasion zone features, resistive annuli, formation rock properties, and formation resistivity anisotropy. The findings included the following: (1) Increasing the wellbore diameter uniformly decreased resistivity across all four investigation depths, positively correlating with borehole size. When mud resistivity (Rm) exceeded formation resistivity (Rt), resistivity curves became distorted. (2) For high- and low-invasion models, the ratio of the deepest to the shallowest investigation depth curves ranged from 1 to 8 and 0.6 to 0.9, respectively, with maximum separation at an invasion depth of 0.5–0.8 m. (3) Under invasion conditions with annuli, an invasion zone depth and annulus width around 0.4 m yield well separated the resistivity curves for all depths. Low- and high-resistivity annuli of 2 m and 0.7 m, respectively, can cause curve intersections. (4) When the formation thickness exceeded 0.2 m, the tool accurately reflected formation resistivity variations and demonstrated effective layer identification in multi-layer conditions. (5) In anisotropic formations, resistivity was negatively correlated with the anisotropy coefficient (λ) as it changed from 2 to 4. The instrument can be fully utilized in the exploration of thin interlayers in oil and gas, significantly enhancing the accuracy of resource identification and extraction technologies.

A Critical Evaluation and Future Projection of Extreme Precipitation Over South Korea in Observation-Based Products and a High-Resolution Model Simulation

A Critical Evaluation and Future Projection of Extreme Precipitation Over South Korea in Observation-Based Products and a High-Resolution Model Simulation

A novel residual-pyramid-attention super resolution model for mesoscale meteorological forecasting spatial downscaling

ABSTRACT Developing a spatial downscaling method using machine learning techniques has emerged as a significant endeavor in meteorological forecasting. In this study, we propose a novel Residual-Pyramid-Attention Super Resolution (RPASR) model that effectively addresses the checkerboard artifacts issue prevalent in existing approaches, offering a unique perspective on spatial downscaling. Moreover, we propose a unique scaling technique during the data training to enhance the prediction performance. To validate and apply the proposed method, we utilize both low and high-resolution meteorological variables obtained from a numerical weather prediction model and observations in the Zhangbei region, China. The results demonstrate the exceptional performance of the RPASR model in spatial downscaling, exhibiting superior accuracy in meteorological prediction. Notably, the model outperforms interpolation techniques, significantly reducing deviations by 50% for variables such as 2 m temperature (T2), 2 m humidity (rh2), and 10 m wind speed (wspd10). In practical applications, our proposed model surpasses the DeepSD super-resolution model and low-resolution simulations as a whole, particularly for T2, where it enhances the T2 correlation coefficient by 1.2% compared to DeepSD. Additionally, it reduces the T2 root mean square error by 10.6% and the rh2 root mean square error by 8.6% compared to the low-resolution simulations. Overall, the RPASR model demonstrates robust validation and exhibits promising potential for advancing spatial downscaling methods in meteorological forecasting.

A model for the lattice thermal conductivity of nanocrystalline materials with arbitrary grain size distribution

Nanocrystalline materials are a popular metamaterial used to control heat transport in semiconductor industrial applications. An accurate and reliable theoretical model is urgently needed but challenging for elucidating detailed microscopic heat transfer phenomena. In this study, we develop a mathematical model to predict the lattice thermal conductivity of nanocrystalline materials with arbitrary grain size distribution. The model first obtains the effective scattering rate of grain boundaries in single-grain-size materials, and then integrates the Landauer’s system transmission concept and series transmission concept to become applicable to multiple grain sizes. It takes into account not only intrinsic and grain boundary scattering effects, but also the effects of finite system size and grain size distribution. Numerical samples of silicon nanocrystals with single grain size, dual size, constant grain size distribution, and logarithmic normal grain size distribution are constructed and full-spectrum Monte Carlo simulations are performed for comparison. The excellent agreement observed between model predictions and simulation results validates the accuracy of the proposed theoretical model. This model will be very helpful in designing micron-scale thermal manipulation systems using nanostructures.

Inferences of water–ice cloud physics determined from MAVEN/IUVS aerosol retrievals and Mars GCM comparisons

We performed retrievals of dust and water–ice optical depths at select times spanning Mars years (MYs) 33 through 36. We used data taken by the Imaging Ultraviolet Spectrograph (IUVS) instrument aboard the Mars Atmosphere and Volatile Evolution (MAVEN) spacecraft, whose precessing orbit allowed IUVS to image the diurnal evolution of water–ice clouds at local times throughout this MY range where they have not been studied. We created a radiative-transfer retrieval algorithm and used it to simultaneously fit the dust and water–ice optical depth of each usable spectrum in IUVS apoapse data for the majority of the MAVEN mission. We compared our results to other datasets to ensure our retrievals produced plausible values and discuss potential reasons for discrepancies. We obtained global climate model (GCM) simulations from two state-of-the-art models and compared our results to both sets of simulations. We show that there are significant differences between our water–ice retrievals and both sets of simulations, both in the spatial cloud patterns and column-integrated optical depths. We discuss the different approaches both models used for simulating these clouds and the implications for accurately modeling these clouds in future GCM work.

Information sources and congruency modulate preference-based decision-making processes

ABSTRACT Preference-based decisions often need to combine multiple pieces of information. This study investigated how the number of information sources and information congruency affect decision performance. Participants made preference-based choices between two groups of food items. Increasing the number of items in each option led to slower and less accurate decisions. Drift-diffusion modelling showed that more information sources relate to a slower rate of evidence accumulation. Therefore, the additional information impeded rather than improved the decision accuracy. In Experiment 2, each choice option contained either fully congruent information or one piece of incongruent information. Decisions with incongruent information is associated with a lower drift rate than that with congruent information, leading to inferior behaviorual performance. Further model simulations support that the change in attention weighting over information sources leads to the observed effects of item numbers and item congruency. Our results suggest a bounded combination of information sources during preference-based decisions.

Methane Combustion Mechanisms on Ni10/CeO2 Studied by DFT, Microkinetic Modeling, and Kinetic Monte Carlo Simulation

Methane Combustion Mechanisms on Ni₁₀/CeO₂ Studied by DFT, Microkinetic Modeling, and Kinetic Monte Carlo Simulation

A Model-Informed Drug Development Approach Supporting the Approval of an Unstudied Valbenazine Dose for Patients With Tardive Dyskinesia.

Valbenazine is a highly potent and selective inhibitor of synaptic vesicular monoamine transporter 2. The current therapeutic doses of valbenazine for tardive dyskinesia (TD) are 40, 60, or 80 mg capsules, given orally, once daily (QD). While 40 and 80 mg were investigated in phase 3 KINECT® 3 trial and initially approved, the approval of valbenazine 60mg was based on the analysis utilizing the Model-informed drug development (MIDD) approach, facilitated through the US Food and Drug Administration's MIDD Pilot Program. This study aimed to demonstrate the efficacy of 60 mg QD dose through model simulations using an established exposure-response (E-R) relationship between valbenazine active metabolite [+]-α-dihydrotetrabenazine exposure and the change from baseline in Abnormal Involuntary Movement Scale total score (AIMS-CFB). A longitudinal E-R model was constructed based on the 40 and 80 mg data from the KINECT 3 trial. The final Emax model adequately predicted dose-dependent improvement in the primary endpoint and was used to interpolate AIMS-CFB for 60 mg at week 6. The efficacy of the unstudied 60 mg dose regimen is expected to be within the range of doses studied clinically with predicted mean AIMS-CFB (95% confidence interval) of -2.69 (-3.30, -2.13) between observed mean AIMS-CFB for 40 mg of -1.92 and 80 mg of -3.39. Results from this analysis provided the key evidence to establish efficacy of 60 mg QD without the need for an additional clinical trial. The availability of valbenazine 60mg dose fills an existing medical need for patients with TD who could benefit from this third effective dose.

Modeling and Numerical Simulation of a Triboelectric Nanogenerator to Achieve Optimal Performance by Considering the Dielectric Constant Effect

Modeling and Numerical Simulation of a Triboelectric Nanogenerator to Achieve Optimal Performance by Considering the Dielectric Constant Effect

Optimization of biological nitrogen removal in full-scale municipal WWTPs using activated sludge model simulation

The study evaluated the most efficient biological nitrogen removal (BNR) process in four full-scale municipal wastewater treatment plants (WWTPs) by using BioWin, a simulation software based on the activated sludge model (ASM). A series of experiments were conducted to determine the kinetic and stoichiometric parameters for the ASM. Results indicated that autotrophic maximum specific growth rates in the studied WWTPs were generally higher compared to previous findings, likely due to their low COD/N ratios, emphasizing the importance of local parameterization. Continuous water quality monitoring in each plant was employed to validate the model. Dynamic simulation results indicated that the error remained within an acceptable range, with a mean relative error of less than 20%, confirming the reliability of ASM parameters derived from batch experiments. Subsequently, various operational scenarios were analyzed to determine the optimal BNR process for each plant, considering influent flow rate, internal recycling, and methanol addition. Simulation outcomes suggested that O/A (oxic/anoxic) operation is preferable for plants with low organic content (COD = 60–110 mg/L), considering both operational costs and total nitrogen removal rate. Conversely, A/O (anoxic/oxic) operation might be advantageous for plants with higher influent organic matter (COD = 200 mg/L).

Tropical cyclone activity over the past 1200 years at the Pelican Cays, Belize

Tropical cyclone (TC) models indicate that continued planet warming will likely increase the global proportion of powerful TCs (specifically Categories 4 and 5 hurricanes), increasingly jeopardizing low-lying coastal communities and resources such as the Pelican Cays, Belize. The combination of increased coastal development and continued relative sea-level rise puts these communities at even higher risk of damage from TCs. The short TC observational record for the western Caribbean hampers the extensive study of TC activity on centennial timescales, which hinders our ability to fully understand past TC climatology and improve the accuracy of TC models. To better assess TC risk, paleotempestological studies are necessary to put future scenarios in perspective. Here, we present a high-resolution reconstruction of coarser-grained sediment deposits associated with TC (predominately ≥ Category 2 hurricanes) passages over the past 1200 years from Elbow and Lagoon Cays, two coral reef-bounded lagoons at the northern and southern end of the Pelican Cays; the most southern Belizean paleotempestological site to date. Coincident timing of historic storms with statistically significant coarser-grained deposits within cay lagoon sediment cores allows us to determine which historic TCs likely generated event layers (tempestites) archived in the sediment record. Our compilation frequency analysis indicates one active interval (above-normal TC activity) from 1740 to 1950 CE and one quiet interval (below-normal TC activity) from 850 to 1018 CE. The active and quiet intervals in the Pelican Cays composite record are anticorrelated with those from nearby and re-analyzed TC records to the north, including the Great Blue Hole (∼100 km north) and the Northeast Yucatan (∼380 km northwest). This site-specific anticorrelation in TC activity along the western Caribbean indicates that we cannot rely on any one single TC record to represent regional TC activity. However, we cannot discount that these anticorrelated periods between the western Caribbean sites are due to randomness. To confirm that the anticorrelation in TC activity among sites from the western Caribbean is indeed a function of climate change and not randomness, an integration of more records and TC model simulations over the past millennium is necessary to assess the significance of centennial-scale variability in TC activity recorded in reconstructions from the western Caribbean.