High Simulation Accuracy Research Articles

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

With the development of computer technology, forest fire spread simulation using computers has gradually developed. According to the existing research on forest fire spread, the models established in various countries have typical regional characteristics. A fire spread model established in a specific region is only suitable for the local area, and there is still a great deal of uncertainty as to whether or not the established model is suitable for fire spread simulation for the same fuel in other regions. Although many fire spread models have been established, the fuel characteristics applicable to each model, such as the fuel loading, fuel moisture content, combustibility, etc., are not similar. It is necessary to evaluate the applicability of different fuel characteristics to different fire spread models. We combined ground investigation, historical data collection, model improvements, and statistical analysis to establish a multi-model forest fire spread simulation method (FIRER) that shows the burning time, perimeter, burning area, overlap area, and spread rate of fire sites. This method is a large-scale, high-resolution fire growth model based on fire spread in eight directions on a regular 30 m grid. This method could use any one of four different physical models (McArthur, Rothermel, FBP, and Wang Zhengfei (China)) for fire behavior. This method has an option to represent fire breaks from roads, rivers, and fire suppression. We can evaluate which model is more suitable in a specific area. This method was tested on a single historical lightning fire in the Daxing’an Mountains. Different scenarios were tested and compared: using each of the four fire behavior models, with fire breaks on or off, and with a single or suspected double fire ignition location of the historical fire. The results show that the Rothermel model is the best model in the simulation of the Hanma lightning fire; the overlap area is 5694.4 hm2. Meanwhile, the real fire area in FIRER is 5800.9 hm2; both the Kappa and Sørensen values exceed 0.8, providing high accuracy in fire spread simulations. FIRER performs well in the automatic identification of fire break zones and multiple ignited points. Compared with FARSITE, FIRER performs well in predicting accuracy. Compared with BehavePlus, FIRER also has advantages in simulating large-scale fire spread. However, the complex data preparation stage of FIRER means that FIRER still has great room for improvement. This research provides a practical basis for the comparison of the practicability and applicability of various fire spread models and provides more effective practical tools and a scientific basis for decision-making and the management of fighting forest fires.

An improved time advance method for distributed multi-resolution modelling with interactive deviation characterization

Multi-resolution modelling (MRM) has been increasingly applied in engineering practice. While in a distributed simulation environments, time advance method for MRM is less studied. When the system is running in a relatively low-resolution status, it usually adopts basic time advance modes to save simulation resources. However, these basic modes usually suffer from inaccurate response of interactions and are separate from the resolution switching process. In this paper, we propose an improved time advance method for distributed multi-resolution models. We introduce an interaction table to enable dynamic management of interactions, and decouple the interaction request process from the interaction response process, resulting in a more fine-grained interactive mode. Moreover, the interactive deviation index is introduced to quantitatively characterize the cumulative errors from delayed interactions. This index has been incorporated into resolution switching conditions to provide a more flexible and intuitive way for multi-resolution modelling. Proposed method has been verified on both theoretical cases and practical applications, and can achieve high simulation accuracy with less time consumption.

Research on the distributed Clark confluence model based on basic watershed units

Abstract Based on the confluence principle, the confluence should represent the aggregate of net rainfall confluences within each distinct basic watershed unit (BWU) of a basin. The BWUs are categorized as V-type and Horseshoe-type based on their shape characteristics and two types of time–area curves of slope convergence have been derived separately. The cascade river convergence is modeled using a lagged linear reservoir, resulting in the development of a distributed CLARK convergence model based on the BWUs of a basin (BWU-DCLARK). The key findings are as follows: (1) The BWU-DCLARK model effectively captures the runoff convergence process and has been successfully applied in the Yanduhe River basin. Modeling results demonstrate high simulation accuracy. (2) The time of slope convergence indicates that the regulatory and storage effects on runoff of BWUs cannot be overlooked. (3) The BWU-DCLARK confluence model not only enables the calculation of flow at the basin outlet but also facilitates the computation of flow at any node along the river chain which is of great significance for hydrological forecasting in un-gauged basins but the application effect will need further verification.

Reconfigurable Sensing‐Memory‐Processing and Logical Integration Within 2D Ferroelectric Optoelectronic Transistor for CMOS‐Compatible Bionic Vision

AbstractNeuromorphic ferroelectric transistors integrating sensing and memory capabilities for photoelectric stimuli have provided a remarkable platform for multifunctional bionic vision. However, most hardware demonstrations utilizing ferroelectric transistors cannot implement multiple bio‐visual functions simultaneously under a small operating voltage with scalable material systems, which reduces the compatibility with complementary metal‐oxide‐semiconductor (CMOS) technology and blocks further bio‐visual applications. Herein, an optoelectronic transistor gated is constructed with ferroelectric LiNbO3, which exhibits sensing‐memory‐processing functions and logic integration simultaneously under a low operating voltage (≈1.5 V). Benefiting from the programmable photoinduction and strong ferroelectric polarization, the reliable and highly controllable synaptic characteristics and the bio‐visual selective learning behavior are successfully demonstrated. A high recognition accuracy (≈94.5%) in simulations is also achieved due to the unique linear synaptic plasticity. Furthermore, based on dual‐wavelength modulation, the full‐optical logics “AND” and “OR” are established within the same device. This work provides novel opportunities for the complex multifunctional bionic vision and toward large‐scale integration compatible with silicon‐based CMOS processes.

Multi-fidelity simulation of aeroengine across wide operation range using auxiliary fully coupled method

Multi-fidelity simulations for the design point or equilibrium lines of aeroengines are implemented using fully coupled methods, simultaneously optimizing computational resources and speed. Fully coupled methods remove the requirement of characteristic maps by directly incorporating computational fluid dynamics models of subcomponents into the thermodynamic model of an aeroengine. However, poor convergence remains an urgent problem for fully coupled methods, particularly when performing multi-fidelity simulations in the near-boundary region. In response to this challenge, an auxiliary fully coupled method was developed in this study. This auxiliary method introduces a parametric priori knowledge and auxiliary coordinate transformations into the existing direct fully coupled method. An automated multi-fidelity simulation platform was established in-house and validated using the KJ66 turbojet engine. The simulation results of the equilibrium line were then compared with experimental data to validate the high accuracy and reliability of the multi-fidelity simulation. Moreover, two fundamental selection schemes for matching parameters (mass flow or static pressure, transferred between the computational fluid dynamics and thermodynamic models) were implemented and systematically compared. The results demonstrate that the adaptive selection of matching parameters extends the application of multi-fidelity simulations to a broader operation range. In addition, the auxiliary fully coupled method was applied to a multi-fidelity simulation of a wider operation range, such as an adjustable nozzle area and variable power extraction. It was also compared with the existing direct fully coupled method with respect to the stable convergence scope. For the mass-flow matching scheme, the maximum stable convergence scope of the direct method was extended from 105 % to 315 % by the auxiliary method. For the static-pressure matching scheme, the auxiliary method also broadened the maximum stable convergence scope of the direct method from 55 % to 190 %. The comparison reveals that the auxiliary fully coupled method exhibits a wider range of far-off-design conditions, including the near-boundary region.

Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches

Soil organic carbon (SOC) is generally thought to act as a carbon sink; however, in areas with high spatial heterogeneity, using a single model to estimate the SOC of the whole study area will greatly reduce the simulation accuracy. The earth surface unit division is important to consider in building different models. Here, we divided the research area into different habitat patches using partitioning around a medoids clustering (PAM) algorithm; then, we built an SOC simulation model using machine learning algorithms. The results showed that three habitat patches were created. The simulation accuracy for Habitat Patch 1 (R2 = 0.55; RMSE = 2.89) and Habitat Patch 3 (R2 = 0.47; RMSE = 3.94) using the XGBoost model was higher than that for the whole study area (R2 = 0.44; RMSE = 4.35); although the R2 increased by 25% and 6.8%, the RMSE decreased by 33.6% and 9.4%, and the field sample points significantly declined by 70% and 74%. The R2 of Habitat Patch 2 using the RF model increased by 17.1%, and the RMSE also decreased by 10.5%; however, the sample points significantly declined by 58%. Therefore, using different models for corresponding patches will significantly increase the SOC simulation accuracy over using one model for the whole study area. This will provide scientific guidance for SOC or soil property monitoring with low field survey costs and high simulation accuracy.

Applicability Analysis of Three Atmospheric Radiative Transfer Models in Nighttime

The relatively stable lunar illumination may be used to realize radiometric calibration under low light. However, there is still an insufficient understanding of the accuracy of models and the influence of parameters when conducting research on low-light radiometric calibration. Therefore, this study explores the applicability of three atmospheric radiative transfer models under different nighttime conditions. The simulation accuracies of three nighttime atmospheric radiative transfer models (Night-SCIATRAN, Night-MODTRAN, and Night-6SV) were evaluated using the visible-infrared imaging radiometer suite day/night band (VIIRS/DNB) data. The results indicate that Night-MODTRAN has the highest simulation accuracy under DNB. The consistency between simulated top-of-atmosphere (TOA) radiance and DNB radiance is approximately 3.1%, and uncertainty is 2.5%. This study used Night-MODTRAN for parameter sensitivity analysis. The results indicate that for the lunar phase angle, aerosol optical depth, surface reflectance, lunar zenith angle, satellite zenith angle, and relative azimuth angle, the average change rates are 68%, 100%, 2561%, 75%, 20%, and 0%. This paper can help better understand the performance of models under different atmospheric and geographical conditions, as well as whether existing models can simulate the complex processes of atmospheric radiation.

Study of experimental and numerical simulation on the influence of gravel on the interflow of slope land.

The formation of interflow is of great significance for the stability of mountain slopes and soil erosion. Hillsides are often covered with a certain amount of gravel, and research on interflow of slope land with different gravel ratios needs to be carried out. This article is based on indoor experiments and numerical models to study the formation law of interflow in hillside soil under different gravel coverage ratios. It was found that the interflow in the soil rapidly increased in the early stage and began to decrease after briefly reaching equilibrium. The formation of interflow is a complex process that is related to slope, soil characteristics, and rainfall intensity, but the correlation is not high when viewed separately. The lattice Boltzmann model can effectively simulate such problems and achieve high simulation accuracy. The [Formula: see text] of the simulated data and measured data ranged from 0.5217 to 0.7403, and [Formula: see text] of the simulated data and measured data ranged from 0.4051 to 0.5711.

Digital twin modeling method for lithium-ion batteries based on data-mechanism fusion driving

Lithium-ion batteries have been rapidly developed as clean energy sources in many industrial fields, such as new energy vehicles and energy storage. The core issues hindering their further promotion and application are reliability and safety. A digital twin model that maps onto the physical entity of the battery with high simulation accuracy helps to monitor internal states and improve battery safety. This work focuses on developing a digital twin model via a mechanism-data-driven parameter updating algorithm to increase the simulation accuracy of the internal and external characteristics of the full-time domain battery under complex working conditions. An electrochemical model is first developed with the consideration of how electrode particle size impacts battery characteristics. By adding the descriptions of temperature distribution and particle-level stress, a multi-particle size electrochemical-thermal-mechanical coupling model is established. Then, considering the different electrical and thermal effect among individual cells, a model for the battery pack is constructed. A digital twin model construction method is finally developed and verified with battery operating data.

Explicit Topology Optimization of Voronoi Foams.

Topology optimization can maximally leverage the high DOFs and mechanical potentiality of porous foams but faces challenges in adapting to free-form outer shapes, maintaining full connectivity between adjacent foam cells, and achieving high simulation accuracy. Utilizing the concept of Voronoi tessellation may help overcome the challenges owing to its distinguished properties on highly flexible topology, natural edge connectivity, and easy shape conforming. However, a variational optimization of the so-called Voronoi foams has not yet been fully explored. In addressing the issue, a concept of explicit topology optimization of open-cell Voronoi foams is proposed that can efficiently and reliably guide the foam's topology and geometry variations under critical physical and geometric requirements. Taking the site (or seed) positions and beam radii as the DOFs, we explore the differentiability of the open-cell Voronoi foams w.r.t. its seed locations, and propose a highly efficient local finite difference method to estimate the derivatives. During the gradient-based optimization, the foam topology can change freely, and some seeds may even be pushed out of shape, which greatly alleviates the challenges of prescribing a fixed underlying grid. The foam's mechanical property is also computed with a much-improved efficiency by an order of magnitude, in comparison with benchmark FEM, via a new material-aware numerical coarsening method on its highly heterogeneous density field counterpart. We show the improved performance of our Voronoi foam in comparison with classical topology optimization approaches and demonstrate its advantages in various settings.

A data-driven model for power system operating costs based on different types of wind power fluctuations

The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%–18% and 3%–13%, respectively, which verified the effectiveness of the method.

Congestive Heart Failure Category Classification Using Neural Networks in Short-Term Series

Congestive heart failure carries immense importance in the realm of public health. This significance arises from its substantial influence on the number of lives lost, economic burdens, the potential for prevention, and the opportunity to enhance the well-being of both individuals and the broader community through decision-making in healthcare. Several researchers have proposed neural networks for classification of different congestive heart failure categories. However, there is little information about the confidence of the prediction on short-term series. Therefore, evaluating classification models is required for effective decision-making in healthcare. This paper explores the use of three classical variants of neural networks to classify three groups of patients with congestive heart failure. The study considered the iterative method Multilayer Perceptron neural network (MLP), two non-iterative models (Extreme Learning Machine (ELM) and Random Vector Functional Link Network (RVFL)), and the CNN approach. The results showed that the deep feature learning system obtained better classification rates than MLP, ELM, and RVFL. Several scenarios designed by coupling some deep feature maps with the RVFL and MLP models showed very high simulation accuracy. The overall accuracy rate of CNN–MLP and CNN–RVFL varies between 98% and 99%.

Experimental and simulation investigation of thermal runaway propagation in lithium-ion battery pack systems

Safety issues related to battery thermal runaway hinder the widespread adoption of electric vehicles. Thermal runaway propagation in a single cell within the modules and the entire battery system can be hazardous. Limited research has been published on thermal runaway propagation at the battery system level. In this study, thermal runaway propagation experiments were conducted at the battery system level for a comprehensive, multi perspective examination of the temperature, voltage, gas, and pressure changes during thermal runaway propagation in a battery system. Additionally, based on the fundamentals of heat transfer, a preliminary three-dimensional thermal runaway propagation model was developed for battery packs. This model exhibited high simulation accuracy and effectively captured the thermal propagation trends within modules. This research uncovers several of thermal runaway propagation characteristics in battery systems, providing valuable insights for the safe design of battery packs.

Model simulation of vegetation canopy precipitation interception in grassland ecosystems on the northeast margin of the Qinghai-Tibet Plateau

Canopy interception plays a crucial role in eco-hydrological processes, and the development of spatiotemporal simulation models of canopy interception is essential for the study of this phenomenon. This study aims to fill the gap in the application of modeling to the study of canopy interception in grassland ecosystems. By using the A.P.J. DE ROO model and the RS-Gash model in conjunction with remote sensing data, we quantitatively simulate and analyze the interception capacity of the grassland vegetation canopy in the study area during the decade. Remote sensing data and measured data are used to assess the simulation accuracy of the models. The model with higher accuracy is used to analyze the spatiotemporal interception of the vegetation canopy in the study area. The results show that the vegetation canopy interception model can be applied not only to forest ecosystems but also to grassland ecosystems; the RS-Gash model has higher simulation accuracy than the A.P.J.DE ROO model; the two models show different simulation accuracy for different grassland types; the vegetation canopy interception rates of different grassland types are different, respectively: alpine meadows is 3.11 %, mountain meadows is 3.82 %, temperate grassland is 2.45 % and temperate desert grassland is 1.13 %; the ratio of vegetation canopy interception area at different levels is roughly low interception area: medium interception area: high interception area = 5:4:1. It can be seen that the application of mechanistic models combined with remote sensing data to simulate canopy interception in grassland ecosystems has great potential. Alpine meadows and mountain meadows are the main contributors to the interception of the grassland vegetation canopy in the study area.

Evaluating the Effects of Controlled Drainage on Nitrogen Uptake, Utilization, Leaching, and Loss in Farmland Soil

Controlling drainage during the growth stage is one of the means to provide suitable water and fertilizer conditions for crops, alleviate environmental pollution, and increase crop yield. Therefore, in this study, we studied three drainage treatments: free drainage (FD) and growth-stage subsurface controlled drainage (CD) at depths of 40 cm (CWT1) and 70 cm (CWT2). We used the HYDRUS-2D model to simulate the dynamic changes of NO3-N in the 0–100 cm soil layer as well as NO3-N uptake by crops, leaching after irrigation and fertilization, and loss through subsurface pipes in 2020 (model calibration period) and 2021 (model validation period). The degree of agreement between the simulated and measured values was high, indicating a high simulation accuracy. CD increased the soil NO3-N content and crop NO3-N uptake, and decreased NO3-N leaching and loss. We observed significant differences in the soil NO3-N content after irrigation at the budding stage of oilseed sunflower between CD and FD, with the largest difference seen for the 0–40 cm soil layer. CD increased crop yield, and the average oilseed sunflower yield of the CWT1 and CWT2 treatments increased by 4.52% and 3.04% relative to the FD treatment (p < 0.05). CD also enhanced nitrogen use efficiency. In moderately salinized soil, CD at 40 cm (CWT1) reduced the nutrient difference in vertical and horizontal directions while retaining water and fertilizer. CWT1 stabilized the groundwater depth, reduced the hydraulic gradient of groundwater runoff, and decreased the drainage flow rate. The NO3-N leaching and loss dropped, which promoted crop nitrogen uptake and utilization, improved nitrogen use efficiency, reduced nitrogen loss, and had a positive effect on protecting the soil and water environment. The results demonstrate that CD is a suitable drainage method for the experimental area.

Ecological well-being and its equity in various economic contexts: Evidence from 130 cities in the yangtze river economic belt

Economic development often leads to environmental damage, resulting in the disorder of ecological well-being in various regions and affecting the equity of ecological well-being in multiple areas. However, economic growth and ecological well-being do not always have a single linear relationship. Therefore, exploring the changes in ecological well-being and its equity in different economic growth contexts is a question worthy of study, which will be beneficial to achieving high-quality development. This paper constructs an analytical framework to explore the equilibrium point between economic growth rate and ecological well-being and its equity. It explores the state of ecological well-being and its equity under different economic growth rates by simulating the monetary value of ecological well-being in 130 cities in the Yangtze River Economic Belt under different economic growth scenarios. The results show that: ① The SD-PLUS model has high simulation accuracy and can simulate land-use change and ecological well-being in different scenarios. ② By 2035, the overall ecological well-being and GDP growth will show an inverse U-shaped relationship. Under the six growth rates measured in this article, a 5% GDP growth rate will be closest to the inflection point. ③ By 2035, there will be a U-shaped relationship between ecological well-being equity and GDP growth. Under the six growth rates measured in this article, a 5% GDP growth rate will be closest to the inflection point. Therefore, when formulating policies, the rate of economic development should be planned rationally and kept above 5% as far as possible so that when ecological well-being per capita is relatively consistent, rapid economic development can be maintained while improving the equity of ecological well-being and achieving high-quality development.

Tools for estimating fake/non-prompt lepton backgrounds with the ATLAS detector at the LHC

Measurements and searches performed with the ATLAS detector at the CERN LHC often involve signatures with one or more prompt leptons. Such analysesare subject to `fake/non-prompt' lepton backgrounds, where either a hadron or a lepton from a hadron decay or an electron from a photon conversion satisfies the prompt-leptonselection criteria. These backgrounds often arise within a hadronic jet because of particle decays in the showering process, particle misidentification or particleinteractions with the detector material. As it is challenging to model these processes with high accuracy in simulation, their estimation typically uses data-driven methods.Three methods for carrying out this estimation are described, along with their implementation in ATLAS and their performance.

Stretchable photosynaptic transistor with an ultralow energy consumption conferred using conjugated block copolymers/perovskite quantum dots nanocomposites

Emulation of photonic synapses offers a promising avenue for developing low-energy consumption of soft electronics, neurologically inspired robotics, and neuromorphic network computation. In this paper, a fully stretchable photosynaptic device with ultralow energy consumption using intrinsically stretchable poly(δ-decanolactone) (PDL)-based conjugated block copolymers (BCPs) with perovskite quantum dots (PeQDs) is first reported. The findings reveal that selectively choosing solvents for the PDL-based BCPs effectively regulates the assembly of P3HT and the accommodation of PeQDs, leading to improved self-aggregation of PeQDs, increased grain size, and optimized interfaces between P3HT and PeQDs The BCPs/PeQDs composite effectively emulates significant features of photonic synapses, such as paired-pulse facilitation (PPF), spike-dependent and short/long-term neuroplasticity, demonstrating excellent performance, including the fastest response time (1 ms), the highest current contrast (4.9 × 105), PPF (1.93) and ultra-low energy consumption (0.3 aJ) at an operating voltage of –0.1 mV. Furthermore, the BCP/PeQDs exhibit remarkable neuromuscular synapse characteristics, including high strain and bending tolerance and spike-dependent plasticity, enabling the devices to achieve high classification accuracy in artificial neural network simulations during tensile strain. The accommodation solvent selectivity of BCP/PeQDs suggests a promising strategy for advancing neurologically soft electronics, human-like pattern recognition, and neuromorphic computation.

A 1D-2D dynamic bidirectional coupling model for high-resolution simulation of urban water environments based on GPU acceleration techniques

Urban water pollution is an important issue of global concern. An integrated model that can be employed to efficiently and accurately simulate the whole system of urban surfaces, underground drainage systems and river and lake water environments is an important tool for comprehensively treating urban water pollution. Therefore, we developed a 1D-2D dynamic bidirectional coupling model for high-resolution simulation of the entire urban water environment (GAST-GCSE) comprising the graphics processing unit (GPU)-accelerated surface water flow and transport (GAST) model and the storm water management model (SWMM). The SWMM and GAST model were used for coupled simulation of hydrological and hydrodynamic quantities and water quality in 1D and 2D regions, respectively. The time steps of the two models are the same. Compute Unified Device Architecture (CUDA) parallel architecture programming and advanced model algorithms were adopted to improve the simulation accuracy and efficiency of the GAST model. The two models were dynamically coupled by calling the dynamic link library (DLL) in the SWMM and adding external functions. Two test cases were considered to verify the performance of the proposed model. The results showed that the proposed model provides high simulation accuracy. Compared with the results obtained by commercial software and theoretical values, the relative errors of the water quantity and water quality simulation results were less than 3%. The whole water environment of the main urban area of Changzhi city in North China was evaluated by the GAST-GCSE model, and the simulation results were compared with monitoring data and pure SWMM simulation results. The simulation errors at three waterlogging points were less than 14%, and the Nash efficiency coefficients (NSE) of the NH4+-N concentration in the three river sections were 0.77, 0.56 and 0.64, respectively. Notably, the model needed only 4.39 h to simulate rainfall production and confluence on a 2D surface, a river flood in complex terrain and its accompanying pollutant evolution process involving 3,849,428 cells over 4 h. This study provides new insights, ideas and tools for urban water environment simulation and change mechanism study.

Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes

Rhenium-186 (186Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models. MethodsWe calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters. ResultsAcross the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively. ConclusionWe have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment. Statement of SignificanceThis manuscript explores the application of computational models to predict response to radionuclide therapy in glioblastoma. There are few, to our knowledge, examples of mathematical models used in clinical radionuclide therapy. We have tested a family of models to determine the applicability of different radiation coupling terms for response to the localized radiation delivery. We show that with patient-specific parameter estimation, we can make accurate predictions of future glioblastoma response to the treatment. As a comparison, we have shown that population trends in response can be used to forecast growth from the moment the treatment has been delivered.In addition to the high simulation and prediction accuracy our modeling methods have achieved, the evaluation of a family of models has given insight into the response dynamics of radionuclide therapy. These dynamics, while different than we had initially hypothesized, should encourage future imaging studies involving high dosage radiation treatments, with specific emphasis on the local immune and vascular response.